@article{nokey,
title = {Exploring the Relationship between Temporal Fluctuations in Satellite Nightlight Imagery and Human Mobility across Africa},
author = {Grant Rogers and Patrycja Koper and Cori Ruktanonchai and and Nick Ruktanonchai and Edson Utazi and Dorothea Woods and Alexander Cunningham and Andrew J. Tatem and Jessica Steele and Shengjie Lai and Alessandro Sorichetta},
url = {https://doi.org/10.3390/rs15174252},
doi = {10.3390/rs15174252},
year  = {2023},
date = {2023-09-30},
journal = {Remote Sensing},
volume = {15},
number = {4252;},
issue = {17},
abstract = {Mobile phone data have been increasingly used over the past decade or more as a pretty reliable indicator of human mobility to measure population movements and the associated changes in terms of population presence and density at multiple spatial and temporal scales. However, given the fact mobile phone data are not available everywhere and are generally difficult to access and share, mostly because of commercial restrictions and privacy concerns, more readily available data with global coverage, such as night-time light (NTL) imagery, have been alternatively used as a proxy for population density changes due to population movements. This study further explores the potential to use NTL brightness as a short-term mobility metric by analysing the relationship between NTL and smartphone-based Google Aggregated Mobility Research Dataset (GAMRD) data across twelve African countries over two periods: 2018–2019 and 2020. The data were stratified by a measure of the degree of urbanisation, whereby the administrative units of each country were assigned to one of eight classes ranging from low-density rural to high-density urban. Results from the correlation analysis, between the NTL Sum of Lights (SoL) radiance values and three different GAMRD-based flow metrics calculated at the administrative unit level, showed significant differences in NTL-GAMRD correlation values across the eight rural/urban classes. The highest correlations were typically found in predominantly rural areas, suggesting that the use of NTL data as a mobility metric may be less reliable in predominantly urban settings. This is likely due to the brightness saturation and higher brightness stability within the latter, showing less of an effect than in rural or peri-urban areas of changes in brightness due to people leaving or arriving. Human mobility in 2020 (during COVID-19-related restrictions) was observed to be significantly different than in 2018–2019, resulting in a reduced NTL-GAMRD correlation strength, especially in urban settings, most probably because of the monthly NTL SoL radiance values remaining relatively similar in 2018–2019 and 2020 and the human mobility, especially in urban settings, significantly decreasing in 2020 with respect to the previous considered period. The use of NTL data on its own to assess monthly mobility and the associated fluctuations in population density was therefore shown to be promising in rural and peri-urban areas but problematic in urban settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Validating indicators for monitoring availability and geographic distribution of emergency obstetric and newborn care (EmoNC) facilities: A study triangulating health system, facility, and geospatial data},
author = {Jewel Gausman and Verónica Pingray and Richard Adanu and Delia A. B. Bandohand Mabel Berrueta and Jeff Blossom and Suchandrima Chakraborty and Winfred Dotse-Gborgbortsi and Ernest Kenu and Nizamuddin Khan and Ana Langer and Carolina Nigri and Magdalene A. Odikro and Sowmya Ramesh and Niranjan Saggurti and Paula Vázquez and Caitlin R. Williams and R. Rima Jolivet},
url = {https://doi.org/10.1371/journal.pone.0287904
},
doi = {10.1371/journal.pone.0287904},
year  = {2023},
date = {2023-09-14},
journal = {PLOS one},
abstract = {Availability of emergency obstetric and newborn care (EmONC) is a strong supply side measure of essential health system capacity that is closely and causally linked to maternal mortality reduction and fundamentally to achieving universal health coverage. The World Health Organization’s indicator “Availability of EmONC facilities” was prioritized as a core indicator to prevent maternal death. The indicator focuses on whether there are sufficient emergency care facilities to meet the population need, but not all facilities designated as providing EmONC function as such. This study seeks to validate “Availability of EmONC” by comparing the value of the indicator after accounting for key aspects of facility functionality and an alternative measure of geographic distribution. This study takes place in four subnational geographic areas in Argentina, Ghana, and India using a census of all birthing facilities. Performance of EmONC in the 90 days prior to data collection was assessed by examining facility records. Data were collected on facility operating hours, staffing, and availability of essential medications. Population estimates were generated using ArcGIS software using WorldPop to estimate the total population, and the number of women of reproductive age (WRA), pregnancies and births in the study areas. In addition, we estimated the population within two-hours travel time of an EmONC facility by incorporating data on terrain from Open Street Map. Using these data sources, we calculated and compared the value of the indicator after incorporating data on facility performance and functionality while varying the reference population used. Further, we compared its value to the proportion of the population within two-hours travel time of an EmONC facility. Included in our study were 34 birthing facilities in Argentina, 51 in Ghana, and 282 in India. Facility performance of basic EmONC (BEmONC) and comprehensive EmONC (CEmONC) signal functions varied considerably. One facility (4.8%) in Ghana and no facility in India designated as BEmONC had performed all seven BEmONC signal functions. In Argentina, three (8.8%) CEmONC-designated facilities performed all nine CEmONC signal functions, all located in Buenos Aires Region V. Four CEmONC-designated facilities in Ghana (57.1%) and the three CEmONC-designated facilities in India (23.1%) evidenced full CEmONC performance. No sub-national study area in Argentina or India reached the target of 5 BEmONC-level facilities per 20,000 births after incorporating facility functionality yet 100% did in Argentina and 50% did in India when considering only facility designation. Demographic differences also accounted for important variation in the indicator’s value. In Ghana, the total population in Tolon within 2 hours travel time of a designated EmONC facility was estimated at 99.6%; however, only 91.1% of women of reproductive age were within 2 hours travel time. Comparing the value of the indicator when calculated using different definitions reveals important inconsistencies, resulting in conflicting information about whether the threshold for sufficient coverage is met. This raises important questions related to the indicator’s validity. To provide a valid measure of effective coverage of EmONC, the construct for measurement should extend beyond the most narrow definition of availability and account for functionality and geographic accessibility.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mapping the timeliness of routine childhood vaccination in The Gambia: A spatial modelling study},
editor = {Oghenebrume Wariri and Chigozie Edson Utazi and Uduak Okomo and C. Jessica E. Metcalf and Malick Sogur and Sidat Fofana and Kris A. Murray and Chris Grundy and Beate Kampmann},
url = {https://doi.org/10.1016/j.vaccine.2023.08.004},
doi = {10.1016/j.vaccine.2023.08.004},
year  = {2023},
date = {2023-09-09},
journal = {Vaccine},
volume = {41},
issue = {39},
abstract = {Timeliness of routine vaccination shapes childhood infection risk and thus is an important public health metric. Estimates of indicators of the timeliness of vaccination are usually produced at the national or regional level, which may conceal epidemiologically relevant local heterogeneities and make it difficult to identify pockets of vulnerabilities that could benefit from targeted interventions. Here, we demonstrate the utility of geospatial modelling techniques in generating high-resolution maps of the prevalence of delayed childhood vaccination in The Gambia. To guide local immunisation policy and prioritize key interventions, we also identified the districts with a combination of high estimated prevalence and a significant population of affected infants.

We used the birth dose of the hepatitis-B vaccine (HepB0), third-dose of the pentavalent vaccine (PENTA3), and the first dose of measles-containing vaccine (MCV1) as examples to map delayed vaccination nationally at a resolution of 1 × 1-km2 pixel. We utilized cluster-level childhood vaccination data from The Gambia 2019–20 Demographic and Health Survey. We adopted a fully Bayesian geostatistical model incorporating publicly available geospatial covariates to aid predictive accuracy. The model was implemented using the integrated nested Laplace approximation—stochastic partial differential equation (INLA-SPDE) approach.

We found significant subnational heterogeneity in delayed HepB0, PENTA3 and MCV1 vaccinations. Specific districts in the central and eastern regions of The Gambia consistently exhibited the highest prevalence of delayed vaccination, while the coastal districts showed a lower prevalence for all three vaccines. We also found that districts in the eastern, central, as well as in coastal parts of The Gambia had a combination of high estimated prevalence of delayed HepB0, PENTA3 and MCV1 and a significant population of affected infants.

Our approach provides decision-makers with a valuable tool to better understand local patterns of untimely childhood vaccination and identify districts where strengthening vaccine delivery systems could have the greatest impact.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {A zero-dose vulnerability index for equity assessment and spatial prioritization in low- and middle-income countries},
author = {C.E. Utazi and H.M.T. Chan and I. Olowe and A. Wigley and N. Tejedor-Garavito and A. Cunningham and M. Bondarenko and J. Lorin and D. Boyda and D. Hogan and A.J. Tatem},
url = {https://doi.org/10.1016/j.spasta.2023.100772
},
doi = {10.1016/j.spasta.2023.100772},
year  = {2023},
date = {2023-09-05},
journal = {Spatial Statistics},
number = {100772},
abstract = {Many low- and middle-income countries (LMICs) continue to experience substantial inequities in vaccination coverage despite recent efforts to reach missed communities and reduce zero-dose prevalence. Geographic inequities in vaccination coverage are often characterized by a multiplicity of risk factors which should be operationalized through data integration to inform more effective and equitable vaccination policies and programmes. Here, we explore approaches for integrating information from multiple risk factors to create a zero-dose vulnerability index to improve the identification and prioritization of vulnerable communities and understanding of inequities in vaccination coverage. We assembled geolocated data on vaccination coverage and associated risk factors in six LMICs, focusing on the coverage of DTP1, DTP3 and MCV1 vaccines as indicators of zero dose and under-vaccination. Using geospatial modelling techniques built on a suite of geospatial covariate information, we produced 1 × 1 km and district level maps of the previously unmapped risk factors and vaccination coverage. We then integrated data from the maps of the risk factors using different approaches to construct a zero-dose vulnerability index to classify districts within the countries into different vulnerability groups, ranging from the least vulnerable (1) to the most vulnerable (5) areas. Through integration with population data, we estimated numbers of children aged under 1 living within the different vulnerability classes. Our results show substantial variation in the spatial distribution of the index, revealing the most vulnerable areas despite little variation in coverage in some cases. We found that the most distinguishing characteristics of the most vulnerable areas cut across the different subdomains (health, socioeconomic, demographic and geographic) of the risk factors included in our study. We also demonstrated that the index can be robustly estimated with fewer risk factors and without linkage to information on vaccination coverage. The index constitutes a practical and effective tool to guide targeted vaccination strategies in LMICs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Effects of public-health measures for zeroing out different SARS-CoV-2 variants},
author = {Yong Ge and Xilin Wu and Wenbin Zhang and Xiaoli Wang and Die Zhang and Jianghao Wang and Haiyan Liu and Zhoupeng Ren and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Eimear Cleary and Yongcheng Yao and Amy Wesolowski and Derek A. T. Cummings and Zhongjie Li and Andrew J. Tatem and Shengjie La},
url = {https://doi.org/10.1038/s41467-023-40940-4
},
doi = {10.1038/s41467-023-40940-4},
year  = {2023},
date = {2023-08-29},
urldate = {2023-08-29},
journal = {Nature Communications},
volume = {14},
number = {5270},
abstract = {Targeted public health interventions for an emerging epidemic are essential for preventing pandemics. During 2020-2022, China invested significant efforts in strict zero-COVID measures to contain outbreaks of varying scales caused by different SARS-CoV-2 variants. Based on a multi-year empirical dataset containing 131 outbreaks observed in China from April 2020 to May 2022 and simulated scenarios, we ranked the relative intervention effectiveness by their reduction in instantaneous reproduction number. We found that, overall, social distancing measures (38% reduction, 95% prediction interval 31-45%), face masks (30%, 17-42%) and close contact tracing (28%, 24-31%) were most effective. Contact tracing was crucial in containing outbreaks during the initial phases, while social distancing measures became increasingly prominent as the spread persisted. In addition, infections with higher transmissibility and a shorter latent period posed more challenges for these measures. Our findings provide quantitative evidence on the effects of public-health measures for zeroing out emerging contagions in different contexts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Combined and delayed impacts of epidemics and extreme weather on urban mobility recovery},
author = {Haiyan Liu and Jianghao Wang and Jian Liu and Yong Ge and Xiaoli Wang and Chi Zhang and Eimear Cleary and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Yongcheng Yao and Amy Wesolowski and Xin Lu and Andrew J. Tatem and Xuemei Bai and Shengjie Lai},
url = {https://doi.org/10.1016/j.scs.2023.104872
},
doi = {10.1016/j.scs.2023.104872},
year  = {2023},
date = {2023-08-22},
journal = {Sustainable Cities and Society},
volume = {99},
number = {104872},
abstract = {The ever-increasing pandemic and natural disasters might spatial-temporal overlap to trigger compound disasters that disrupt urban life, including human movements. In this study, we proposed a framework for data-driven analyses on mobility resilience to uncover the compound effects of COVID-19 and extreme weather events on mobility recovery across cities with varied socioeconomic contexts. The concept of suppression risk (SR) is introduced to quantify the relative risk of mobility being reduced below the pre-pandemic baseline when certain variables deviate from their normal values. By analysing daily mobility data within and between 313 Chinese cities, we consistently observed that the highest SR under outbreaks occurred at high temperatures and abnormal precipitation levels, regardless of the type of travel, incidences, and time. Specifically, extremely high temperatures (at 35 °C) increased SR during outbreaks by 12.5%-120% but shortened the time for mobility recovery. Increased rainfall (at 20 mm/day) added SRs by 12.5%-300%, with delayed effects reflected in cross-city movements. These compound impacts, with varying lagged responses, were aggravated in cities with high population density and low GDP levels. Our findings provide quantitative evidence to inform the design of preparedness and response strategies for enhancing urban resilience in the face of future pandemics and compound disasters.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {High-resolution gridded population datasets for Latin America and the Caribbean using official statistics},
author = {Tom McKeen and Maksym Bondarenko and David Kerr and Thomas Esch and Mattia Marconcini and Daniela Palacios-Lopez and Julian Zeidler and R. Catalina Valle and Sabrina Juran and Andrew J. Tatem and Alessandro Sorichetta},
url = {https://doi.org/10.1038/s41597-023-02305-w},
doi = {10.1038/s41597-023-02305-w},
year  = {2023},
date = {2023-07-07},
journal = {Scientific Data},
volume = {10},
number = {436},
abstract = {“Leaving no one behind” is the fundamental objective of the 2030 Agenda for Sustainable Development. Latin America and the Caribbean is marked by social inequalities, whilst its total population is projected to increase to almost 760 million by 2050. In this context, contemporary and spatially detailed datasets that accurately capture the distribution of residential population are critical to appropriately inform and support environmental, health, and developmental applications at subnational levels. Existing datasets are under-utilised by governments due to the non-alignment with their own statistics. Therefore, official statistics at the finest level of administrative units available have been implemented to construct an open-access repository of high-resolution gridded population datasets for 40 countries in Latin American and the Caribbean. These datasets are detailed here, alongside the ‘top-down’ approach and methods to generate and validate them. Population distribution datasets for each country were created at a resolution of 3 arc-seconds (approximately 100 m at the equator), and are all available from the WorldPop Data Repository.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics},
author = {Die Zhang and Yong Ge and Xilin Wu and Haiyan Liu and Wenbin Zhang and Shengjie Lai},
url = {https://doi.org/10.3390/ijgi12070266},
doi = {10.3390/ijgi12070266},
year  = {2023},
date = {2023-07-03},
journal = {International Journal of Geo-Information},
volume = {12},
number = {266},
issue = {7},
abstract = {Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Joint modelling of systolic and diastolic blood pressure and its associated factors among women in Ghana: Multivariate response multilevel modelling methods},
author = {Justice Moses K. Aheto and Tracy Gates and Rahmatu Babah and Wisdom Takramah},
url = {https://doi.org/10.1371/journal.pgph.0001613},
doi = {10.1371/journal.pgph.0001613},
year  = {2023},
date = {2023-04-26},
journal = {PLOS Global Public Health},
volume = {3},
issue = {4},
pages = {e0001613},
abstract = {Elevated blood pressure is the leading cause of cardiovascular diseases related mortality and a major contributor to non-communicable diseases globally, especially in sub-Saharan Africa where about 74.7 million people live with hypertension. In Ghana, hypertension is epidemic with prevalence of over 30% and experiencing continuing burden with its associated morbidity and mortality. Using the 2014 Ghana Demographic and Health Survey, we analyzed data on 4744 women aged 15–49 years residing in 3722 households. We employed univariate and multivariate response multilevel linear regression models to analyze predictors of systolic blood pressure (SBP) and diastolic blood pressure (DBP). Geospatial maps were produced to show the regional distribution of hypertension prevalence in Ghana. Stata version 17 and R version 4.2.1 were used to analyze the data. Of the 4744 woman, 337 (7.1%) and 484 (10.2%) were found to be hypertensive on SBP and DBP, respectively. A combined prevalence of 12.3% was found. Older ages 25–34 (OR 2.45, 95%CI: 1.27, 3.63), 35–44 (OR 8.72, 95%CI: 7.43, 10.01), 45–49 (OR 15.85, 95%CI: 14.07, 17.64), being obese (OR 5.10, 95%CI: 3.62, 6.58), and having no education (OR -2.05, 95%CI: -3.40, -0.71) were associated with SBP. For DBP, we found the associated factors to be older ages 25–34 (OR 3.29, 95%CI: 2.50, 4.08), 35–44 (OR 6.78, 95%CI: 5.91, 7.64), 45–49 (OR 10.05, 95%CI: 8.85, 11.25), being obese (OR 4.20, 95%CI: 3.21, 5.19), and having no education (OR -1.23, 95%CI: -2.14, -0.33). Substantial residual household level differences in SBP (15%) and DBP (14%) were observed. We found strong residual correlation of SBP and DBP on individual women (r = 0.73) and household-level (r = 0.81). The geospatial maps showed substantial regional differences in the observed and reported hypertension prevalence. Interventions should be targeted at the identified high-risk groups like older age groups and those who are obese, and the high-risk regions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Social cartography and satellite-derived building coverage for post-census population estimates in difficult-to-access regions of Colombia},
author = {Lina Maria Sanchez-Cespedes and Douglas Ryan Leasure and Natalia Tejedor-Garavito and Glenn Harry Amaya Cruz and Gustavo Adolfo Garcia Velez and Andryu Enrique Mendoza and Yenny Andrea Marín Salazar and Thomas Esch and Andrew J. Tatem and Mariana Ospina Bohórquez},
url = {https://doi.org/10.1080/00324728.2023.2190151},
doi = {10.1080/00324728.2023.2190151},
year  = {2023},
date = {2023-03-28},
journal = {Population Studies},
pages = {1-18},
abstract = {Effective government services rely on accurate population numbers to allocate resources. In Colombia and globally, census enumeration is challenging in remote regions and where armed conflict is occurring. During census preparations, the Colombian National Administrative Department of Statistics conducted social cartography workshops, where community representatives estimated numbers of dwellings and people throughout their regions. We repurposed this information, combining it with remotely sensed buildings data and other geospatial data. To estimate building counts and population sizes, we developed hierarchical Bayesian models, trained using nearby full-coverage census enumerations and assessed using 10-fold cross-validation. We compared models to assess the relative contributions of community knowledge, remotely sensed buildings, and their combination to model fit. The Community model was unbiased but imprecise; the Satellite model was more precise but biased; and the Combination model was best for overall accuracy. Results reaffirmed the power of remotely sensed buildings data for population estimation and highlighted the value of incorporating local knowledge.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@techreport{nokey,
title = {Field testing of pre-Enumeration Areas created using semi-automated delineation approach, Democratic Republic of Congo},
author = {Sarchil Qader and Heather Chamberlain and Mathias Kuepie and Freja K. Hunt and Attila Lazar and Andrew J. Tatem},
url = {https://eprints.soton.ac.uk/475327/},
doi = {10.5258/SOTON/WP00759},
year  = {2023},
date = {2023-03-15},
urldate = {2023-03-15},
abstract = {This report details the main outcomes of the field testing of pre-Enumeration Areas (EAs) created from WorldPop and Flominder’s semi-automated EA approach that took place across three test sites in the provinces of Kinshasa and Kongo-Central, Democratic Republic of the Congo in December 2019. The field testing was conducted over four days by the BCR technical staff with participation from UNFPA and WorldPop staff.

Generally, EA boundaries from one census will form the basis for the EAs in the next census, with updates needed to account for new settlements and changes in population density. However, in countries where there hasn’t been a census for many years, often due to conflict or insecurity, EA boundaries can be incomplete, outdated, or missing altogether. The delineation of EAs is, therefore, a crucial pre-census activity but can often be particularly challenging and highly resource intensive. Creating EAs requires consideration of population and area size within each unit to ensure that they have approximately equal-sized populations and are a manageable size to be covered by census enumeration staff. To respond to this challenge, WorldPop has developed a semi-automatic approach of delineating pre-EAs to support census cartography. This approach utilises high-resolution gridded population estimates and digitised geographic features, including administrative boundaries, and natural and man-made features, such as rivers and roads, to divide the regions into small areas which are then merged to meet criteria specified for population size and geographic area.

The last census in DRC was conducted in 1984; consequently, a recent, national, digital EA dataset which can be used for cartography planning does not exist. GRID3 is supporting the realisation of a fully digital 2020 round census in the DRC and is working closely with the National Institute of Statistics and the DRC Census Bureau (Bureau Central de Recensement, BCR) to provide technical guidance regarding options for incorporating geospatial methodologies into census planning and census cartography. As the DRC Census Bureau prepares for the 2nd National Population and Housing Census (RGPH2), a new dataset of EA boundaries is needed. As part of GRID3’s work with the BCR, a field test was conducted to assess the feasibility of using a semi-automated approach for the delineation of pre-EA boundaries.

A preliminary pre-EA dataset was produced for the three test sites (Site 1: Quartier Kingu, Kinshasa (urban), Site 2: Quartier Dumi, Kinshasa (sub-urban), Site 3: Secteur Kasangulu, Kongo-Central (rural)) that span both rural and urban contexts. The geographic area covered by the three sites totalled 1,190 km2 and was sub-divided into approximately 312 pre-EAs. The pre-EAs created for the three test sites were classified as classes 1-3 depending on the degree to which the pre-EA boundaries followed visible features (e.g. roads). Class 1 being those pre-EAs with boundaries which fully followed visible features, class 2 boundaries followed visible features in part, and class 3 which didn’t follow visible features at all. A visual assessment was carried out by comparing the pre-EA boundaries with recent high-resolution satellite imagery. A subset of the pre-EAs (15 pre-EAs), covering classes 1, 2 and 3 were selected, and assessed in the field to check how the boundaries related to ground features and their feasibility as units for population enumeration. Class 1 pre-EAs were only found in urban contexts and tended to be bounded fully by roads, which were found to be simple for the field teams to follow. In class 2 and class 3 pre-EAs, the field teams were generally able to follow roads or tracks throughout the pre-EA to reach settlements, and ascertain when they had reached the boundary of the pre-EA using the maps and GPS location indicator on the tablets. The pre-EA boundaries were also created to avoid splitting settlements and therefore even in rural areas, the field teams were able to know where housing units needed to be enumerated.

A range of limitations with this work have been identified, both with the methods and equipment used in the field data collection and the methods and input data used to produce the pre-EA boundaries. Despite the identified limitations and the challenges encountered in the field, the findings from the field test were generally consistent, with the pre-EAs created by the semi-automated approach found to be suitable for population enumeration in the field. Overall the fieldwork was successfully conducted and expectations were met and even exceeded: the BCR found that the pre-EA outputs were found to help facilitate enumeration, as the BCR team could navigate within the pre-EA boundaries and know which housing units to enumerate. The findings of the field test indicate this semi-automated approach to creating pre-EAs has the potential to be used by the BCR to create pre-EAs in preparation for census cartography, and offers large savings in terms of time, labour and cost. Nonetheless, it would be expected that the pre-EA outputs created in the approach are carefully reviewed in the lab, and manually edited as needed prior to census cartography. Then whilst in the field, the pre-EA boundaries should be validated. Limitations associated with input datasets can be addressed through a comprehensive review of existing datasets, incorporating newly available feature extraction datasets as appropriate. Further development of the approach and potential solutions and suggestions to overcome the identified limitations are outlined and discussed in detail in the report.

We expect the findings of the field test in DRC to be transferable to other similar contexts, with the approach having applicability in countries with no recent digital EAs. We also expect the approach could be adapted to update digital EA boundaries in contexts with outdated EA datasets, but this should be explored through further research and testing in such contexts.

Worth noting that in close collaboration with GeoData at the University of Southampton, UNFPA and multiple national statistical offices around the world, WorldPop has now converted the automatic delineation script to a user-friendly tool which require minimal GIS skill to run.},
howpublished = {eprints Soton},
keywords = {},
pubstate = {published},
tppubtype = {techreport}
}
@article{nokey,
title = {Impact of mobile health on maternal and child health service utilization and continuum of care in Northern Ghana},
author = {Abdul Ganiyu Kantamah and Duah Dwomoh and Susan Ama Amuasi and Winfred Dotse-Gborgbortsi and Chrysantus Kubio and Edward Anane Apraku and Jonas Kolong Timbire and Justice Nonvignon},
url = {https://doi.org/10.1038/s41598-023-29683-w
},
doi = {10.1038/s41598-023-29683-w},
year  = {2023},
date = {2023-02-21},
journal = {Scientific Reports},
volume = {13},
number = {3004},
abstract = {Maternal and child mortality are of public health concern. Most of these deaths occur in rural communities of developing countries. Technology for maternal and child health (T4MCH) is an intervention introduced to increase Maternal and Child Health (MCH) services utilization and continuum of care in some health facilities across Ghana. The objective of this study is to assess the impact of T4MCH intervention on MCH services utilization and continuum of care in the Sawla-Tuna-Kalba District in the Savannah Region of Ghana. This is a quasi-experimental study with a retrospective review of records of MCH services of women who attended antenatal services in some selected health centers in the Bole (comparison district) and Sawla-Tuna-Kalba (intervention district) of the Savannah region, Ghana. A total of 469 records were reviewed, 263 in Bole and 206 in Sawla-Tuna-Kalba. A multivariable modified Poisson and logistic regression models with augmented inverse-probability weighted regression adjustment based on propensity scores were used to quantify the impact of the intervention on service utilization and continuum of care. The implementation of T4MCH intervention increased antenatal care attendance, facility delivery, postnatal care and continuum of care by 18 percentage points (ppts) [95% CI - 17.0, 52.0], 14 ppts [95% CI 6.0%, 21.0%], 27 ppts [95% CI 15.0, 26.0] and 15.0 ppts [95% CI 8.0, 23.0] respectively compared to the control districts. The study showed that T4MCH intervention improved antenatal care, skilled delivery, postnatal services utilization, and continuum of care in health facilities in the intervention district. The intervention is recommended for a scale-up in other rural areas of Northern Ghana and the West-African sub-region.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {A subnational reproductive, maternal, newborn, child, and adolescent health and development atlas of India},
author = {Carla Pezzulo and Natalia Tejedor-Garavito and Ho Man Theophilus Chan and Ilda Dreoni and David Kerr and Samik Ghosh and Amy Bonnie and Maksym Bondarenko and Mihretab Salasibew and Andrew J. Tatem },
url = {https://www.nature.com/articles/s41597-023-01961-2},
doi = {10.1038/s41597-023-01961-2},
year  = {2023},
date = {2023-02-10},
journal = {Scientific Data},
volume = {10},
issue = {86},
abstract = {Understanding the fine scale and subnational spatial distribution of reproductive, maternal, newborn, child, and adolescent health and development indicators is crucial for targeting and increasing the efficiency of resources for public health and development planning. National governments are committed to improve the lives of their people, lift the population out of poverty and to achieve the Sustainable Development Goals. We created an open access collection of high resolution gridded and district level health and development datasets of India using mainly the 2015–16 National Family Health Survey (NFHS-4) data, and provide estimates at higher granularity than what is available in NFHS-4, to support policies with spatially detailed data. Bayesian methods for the construction of 5 km × 5 km high resolution maps were applied for a set of indicators where the data allowed (36 datasets), while for some other indicators, only district level data were produced. All data were summarised using the India district administrative boundaries. In total, 138 high resolution and district level datasets for 28 indicators were produced and made openly available.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Exploring the use of Sentinel-2 datasets and environmental variables to model wheat crop yield in smallholder arid and semi-arid farming systems},
author = {Sarchil Hama Qader and Chigozie Edson Utazi and Rhorom Priyatikanto and Peshawa Najmaddin and Emad Omer Hama-Ali and Nabaz R. Khwarahm and Andrew J. Tatem and Jadu Dash},
url = {https://doi.org/10.1016/j.scitotenv.2023.161716},
doi = {10.1016/j.scitotenv.2023.161716},
year  = {2023},
date = {2023-01-24},
urldate = {2023-01-24},
journal = {Science of The Total Environment},
volume = {869},
number = {161716},
abstract = {Low levels of agricultural productivity are associated with the persistence of food insecurity, poverty, and other socio-economic stresses. Mapping and monitoring agricultural dynamics and production in real-time at high spatial resolution are essential for ensuring food security and shaping policy interventions. However, an accurate yield estimation might be challenging in some arid and semi-arid regions since input datasets are generally scarce, and access is restricted due to security challenges. This work examines how well Sentinel-2 satellite sensor-derived data, topographic and climatic variables, can be used as covariates to accurately model and predict wheat crop yield at the farm level using statistical models in low data settings of arid and semi-arid regions, using Sulaimani governorate in Iraq as an example. We developed a covariate selection procedure that assessed the correlations between the covariates and their relationships with wheat crop yield. Potential non-linear relationships were investigated in the latter case using regression splines. In the absence of substantial non-linear relationships between the covariates and crop yield, and residual spatial autocorrelation, we fitted a Bayesian multiple linear regression model to model and predict crop yield at 10 m resolution. Out of the covariates tested, our results showed significant relationships between crop yield and mean cumulative NDVI during the growing season, mean elevation, mean end of the season, mean maximum temperature and mean the start of the season at the farm level. For in-sample prediction, we estimated an R2 value of 51 % for the model, whereas for out-of-sample prediction, this was 41 %, both of which indicate reasonable predictive performance. The calculated root-mean-square error for out-of-sample prediction was 69.80, which is less than the standard deviation of 89.23 for crop yield, further showing that the model performed well by reducing prediction variability. Besides crop yield estimates, the model produced uncertainty metrics at 10 m resolution. Overall, this study showed that Sentinel-2 data can be valuable for upscaling field measurement of crop yield in arid and semi-arid regions. In addition, the environmental covariates can strengthen the model predictive power. The method may be applicable in other areas with similar environments, particularly in conflict zones, to increase the availability of agricultural statistics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Quality of maternal healthcare and travel time influence birthing service utilisation in Ghanaian health facilities: a geographical analysis of routine health data},
author = {Winfred Dotse-Gborgbortsi and Andrew J Tatem and Zoe Matthews and Victor A Alegana and Anthony Ofosu and Jim A Wright},
url = {http://dx.doi.org/10.1136/bmjopen-2022-066792},
doi = {10.1136/bmjopen-2022-066792},
year  = {2023},
date = {2023-01-18},
journal = {BMJ Open},
volume = {13},
issue = {1},
abstract = {Objectives: To investigate how the quality of maternal health services and travel times to health facilities affect birthing service utilisation in Eastern Region, Ghana.

Design: The study is a cross-sectional spatial interaction analysis of birth service utilisation patterns. Routine birth data were spatially linked to quality care, service demand and travel time data.

Setting: 131 Health facilities (public, private and faith-based) in 33 districts in Eastern Region, Ghana.

Participants: Women who gave birth in health facilities in the Eastern Region, Ghana in 2017.

Outcome measures: The count of women giving birth, the quality of birthing care services and the geographic coverage of birthing care services.

Results: As travel time from women’s place of residence to the health facility increased up to two (2) hours, the utilisation rate markedly decreased. Higher quality of maternal health services haves a larger, positive effect on utilisation rates than service proximity. The quality of maternal health services was higher in hospitals than in primary care facilities. Most women (88.6%) travelling via mechanised transport were within two2 hours of any birthing service. The majority (56.2%) of women were beyond the two2 -hour threshold of critical comprehensive emergency obstetric and newborn care (CEmONC) services. Few CEmONC services were in urban centres, disadvantaging rural populations.

Conclusions: To increase birthing service utilisation in Ghana, higher quality health facilities should be located closer to women, particularly in rural areas. Beyond Ghana, routinely collected birth records could be used to understand the interaction of service proximity and quality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Prevalence of common adverse events experienced following COVID‐19 vaccination and its associated factors in Ghana: Cross‐sectional study design},
author = {Naa A. A. Boi-Dsane and Bartholomew Dzudzor and Yakubu Alhassan and Justice M. K. Aheto },
url = {https://doi.org/10.1002/hsr2.1012
},
doi = {10.1002/hsr2.1012},
year  = {2022},
date = {2022-12-24},
journal = {Health Science Reports},
volume = {6},
number = {e1012},
issue = {1},
abstract = {The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) (COVID-19) pandemic devasted the general life of people and various human activities across the globe, and Ghana is of no exception. This led to development of vaccines within record time to combat morbidity and mortality associated with the virus. In Ghana, COVID-19 vaccines were introduced in addition to existing COVID-19 protocols. However, the vaccines have adverse events among those who received them. In this study, we determined the prevalence of some common adverse events of the COVID-19 vaccines and its associated sociodemographic factors in Ghana.
An online snowball cross-sectional survey was conducted between April and June 2021 among 240 people who had taken at least one dose of any of the COVID-19 vaccines approved in Ghana. The penalized binary logistic regression model was used to assess the factors associated with experience of at least one adverse event and the experience of number of adverse events using Stata version 16.
Among the 240 participants, 88.2% had experienced at least one adverse event. The most common adverse event after the first dose was pain at injection site (65.8%), headache (57.5%), tiredness (55.8%), fever (51.7%), chills (39.6%), and muscle pains (38.3%). Experience of adverse events was 16 times higher among those who took their vaccines in Ghana (adjusted odd ratio [AOR]: 16.2, 95% confidence interval [CI]: 1.98–132.56, p = 0.009), 94% less among those who took AstraZeneca (India) compared to AstraZeneca (Oxford) (AOR: 0.06, 95% CI: 0.01–0.37, p = 0.002) and 86% less among 40–49 years compared with less than 30 years old (AOR: 0.14, 95% CI: 0.03–0.58, p = 0.007).
Pain at the injection site, headache, tiredness, fever, chills, and muscle pains were the most frequently reported adverse events. The study identified country of vaccination, country of origin of AstraZeneca vaccine and age to be associated with adverse events of vaccination.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Spatially Varying Intergenerational Changes in the Prevalence of Female Genital Mutilation/Cutting in Nigeria: Lessons Learnt from a Recent Household Survey},
author = {Chibuzor Christopher Nnanatu and Adeniyi Francis Fagbamigbe and Emmanuel Afuecheta and Chigozie Edson Utazi},
editor = {Vikram Aditya},
url = {https://doi.org/10.1007/s12061-022-09497-5
},
doi = {10.1007/s12061-022-09497-5},
year  = {2022},
date = {2022-12-20},
urldate = {2023-12-20},
journal = {Applied Spatial Analysis and Policy},
abstract = {Considering the concerted investments in anti-female genital mutilation/cutting (FGM/C) campaigns championed by the Nigerian government and non-governmental organizations, research findings suggest that reduction in intergenerational (mother-to-daughter) prevalence of FGM/C in Nigeria has been very slow. What can we learn from the 2018 Nigerian Demographic and Health Survey (2018 NDHS) about the roles of the key drivers of mother-to-daughter FGM/C prevalence in Nigeria? Here, drawing upon the 2018 NDHS dataset, we provided a context-specific study on the geographical patterns and the enabling factors of intergenerational trends in FGM/C among Nigerian women aged 15 – 49 years and their daughters aged 0 – 14 years. Using Bayesian semi-parametric geo-additive regression model, we simultaneously controlled for the effects of individual-level, community-level and unobserved geographical factors. We learnt that although there has been an overall decline in mother-to-daughter prevalence of FGM/C, the practice persists in Nigeria largely due to geographical location and social norm related factors – risk was high among daughters of circumcised women and daughters of women who supported the continuation of FGM/C. We identified Kano, Kaduna, Imo and Bauchi states as the hotspots and there was an increased risk of FGM/C among daughters of women who lived in the neigbouring states of Jigawa and Yobe. Daughters of circumcised women were about 2.7 times more likely to be cut. We recommend the development of tailored community-level interventions targeting circumcised women in the hotspot states and their neighbours to ensure a total eradication of female circumcision in Nigeria by the year 2030.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mapping the distribution of zero-dose children to assess the performance of vaccine delivery strategies and their relationships with measles incidence in Nigeria},
author = {C. Edson Utazi and Justice M. K. Aheto and Adelle Wigley and Natalia Tejedor-Garavito and Amy Bonnie and Chris Nnanatu and John Wagai and Cheryl Williams and Hamidrez Setayesh and Andrew J.Tatem},
doi = {10.1016/j.vaccine.2022.11.026},
year  = {2022},
date = {2022-11-19},
urldate = {2022-11-19},
journal = {Vaccine},
abstract = {Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.},
keywords = {Measles vaccination, Nigeria, zero dose},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mapping the distribution of zero-dose children to assess the performance of vaccine delivery strategies and their relationships with measles incidence in Nigeria},
author = {Utazi CE and Aheto JM and Wigley A and Tejedor-Garavito N and Bonnie A and Nnanatu CC and Wagai J and Williams C and Setayesh H and Tatem AJ and Cutts FT},
url = {https://doi.org/10.1016/j.vaccine.2022.11.026
},
doi = {10.1016/j.vaccine.2022.11.026},
year  = {2022},
date = {2022-11-19},
urldate = {2023-11-19},
journal = {Vaccine},
volume = {41},
issue = {1},
pages = {170-181},
abstract = {Geographically precise identification and targeting of populations at risk of vaccine-preventable diseases has gained renewed attention within the global health community over the last few years. District level estimates of vaccination coverage and corresponding zero-dose prevalence constitute a potentially useful evidence base to evaluate the performance of vaccination strategies. These estimates are also valuable for identifying missed communities, hence enabling targeted interventions and better resource allocation. Here, we fit Bayesian geostatistical models to map the routine coverage of the first doses of diphtheria-tetanus-pertussis vaccine (DTP1) and measles-containing vaccine (MCV1) and corresponding zero-dose estimates in Nigeria at 1x1 km resolution and the district level using geospatial data sets. We also map MCV1 coverage before and after the 2019 measles vaccination campaign in the northern states to further explore variations in routine vaccine coverage and to evaluate the effectiveness of both routine immunization (RI) and campaigns in reaching zero-dose children. Additionally, we map the spatial distributions of reported measles cases during 2018 to 2020 and explore their relationships with MCV zero-dose prevalence to highlight the public health implications of varying performance of vaccination strategies across the country. Our analysis revealed strong similarities between the spatial distributions of DTP and MCV zero dose prevalence, with districts with the highest prevalence concentrated mostly in the northwest and the northeast, but also in other areas such as Lagos state and the Federal Capital Territory. Although the 2019 campaign reduced MCV zero-dose prevalence substantially in the north, pockets of vulnerabilities remained in areas that had among the highest prevalence prior to the campaign. Importantly, we found strong correlations between measles case counts and MCV RI zero-dose estimates, which provides a strong indication that measles incidence in the country is mostly affected by RI coverage. Our analyses reveal an urgent and highly significant need to strengthen the country’s RI program as a longer-term measure for disease control, whilst ensuring effective campaigns in the short term.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa},
author = {Heather R. Chamberlain and Attila N. Lazar and Andrew J. Tatem },
doi = {10.1038/s41597-022-01799-0},
year  = {2022},
date = {2022-11-18},
journal = {Scientific Data},
volume = {9},
number = {711},
abstract = {Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.},
keywords = {Africa, covid-19, NPIs},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {High-resolution estimates of social distancing feasibility, mapped for urban areas in sub-Saharan Africa},
author = {Heather R. Chamberlain and Attila N. Lazar and Andrew J. Tatem },
url = {https://doi.org/10.1038/s41597-022-01799-0
},
doi = {10.1038/s41597-022-01799-0},
year  = {2022},
date = {2022-11-18},
urldate = {2023-11-18},
journal = {Scientific Data},
volume = {9},
number = {711 (2022)},
abstract = {Social distancing has been widely-implemented as a public health measure during the COVID-19 pandemic. Despite widespread application of social distancing guidance, the feasibility of people adhering to such guidance varies in different settings, influenced by population density, the built environment and a range of socio-economic factors. Social distancing constraints however have only been identified and mapped for limited areas. Here, we present an ease of social distancing index, integrating metrics on urban form and population density derived from new multi-country building footprint datasets and gridded population estimates. The index dataset provides estimates of social distancing feasibility, mapped at high-resolution for urban areas across 50 countries in sub-Saharan Africa.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Geographic inequalities in health intervention coverage – mapping the composite coverage index in Peru using geospatial modelling},
author = {Leonardo Z. Ferreira and C. Edson Utazi and Luis Huicho and Kristine Nilsen and Fernando P. Hartwig and Andrew J. Tatem and Aluisio J. D. Barros},
url = {https://doi.org/10.1186/s12889-022-14371-7
},
doi = {10.1186/s12889-022-14371-7},
year  = {2022},
date = {2022-11-17},
urldate = {2023-11-17},
journal = {BMC Public Health 22},
volume = {22},
number = {2104 (2022)},
abstract = {The composite coverage index (CCI) provides an integrated perspective towards universal health coverage in the context of reproductive, maternal, newborn and child health. Given the sample design of most household surveys does not provide coverage estimates below the first administrative level, approaches for achieving more granular estimates are needed. We used a model-based geostatistical approach to estimate the CCI at multiple resolutions in Peru.
We generated estimates for the eight indicators on which the CCI is based for the departments, provinces, and areas of 5 × 5 km of Peru using data from two national household surveys carried out in 2018 and 2019 plus geospatial covariates. Bayesian geostatistical models were fit using the INLA-SPDE approach. We assessed model fit using cross-validation at the survey cluster level and by comparing modelled and direct survey estimates at the department-level.
CCI coverage in the provinces along the coast was consistently higher than in the remainder of the country. Jungle areas in the north and east presented the lowest coverage levels and the largest gaps between and within provinces. The greatest inequalities were found, unsurprisingly, in the largest provinces where populations are scattered in jungle territory and are difficult to reach.
Our study highlighted provinces with high levels of inequality in CCI coverage indicating areas, mostly low-populated jungle areas, where more attention is needed. We also uncovered other areas, such as the border with Bolivia, where coverage is lower than the coastal provinces and should receive increased efforts. More generally, our results make the case for high-resolution estimates to unveil geographic inequities otherwise hidden by the usual levels of survey representativeness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Global estimates of pregnancies at risk of Plasmodium falciparum and Plasmodium vivax infection in 2020 and changes in risk patterns since 2000},
author = {Georgia R. Gore-Langton and Jorge Canoand Hope Simpson and Andrew Tatemand Natalia Tejedor-Garavito and Adelle Wigley and Alessandra Carioli and Peter Gething and Daniel J. Weiss and Daniel Chandramohan and Patrick G. T. Walker and Matthew E. Cairns and R. Matthew Chico},
url = {https://journals.plos.org/globalpublichealth/article?id=10.1371/journal.pgph.0001061},
doi = {10.1371/journal.pgph.0001061},
year  = {2022},
date = {2022-11-09},
urldate = {2023-11-09},
journal = {PLOS Global Public Health},
abstract = {Women are at risk of severe adverse pregnancy outcomes attributable to Plasmodium spp. infection in malaria-endemic areas. Malaria control efforts since 2000 have aimed to reduce this burden of disease.
We used data from the Malaria Atlas Project and WorldPop to calculate global pregnancies at-risk of Plasmodium spp. infection. We categorised pregnancies as occurring in areas of stable and unstable P. falciparum and P. vivax transmission. We further stratified stable endemicity as hypo-endemic, meso-endemic, hyper-endemic, or holo-endemic, and estimated pregnancies at risk in 2000, 2005, 2010, 2015, 2017, and 2020.
In 2020, globally 120.4M pregnancies were at risk of P. falciparum, two-thirds (81.0M, 67.3%) were in areas of stable transmission; 85 2M pregnancies were at risk of P. vivax, 93.9% (80.0M) were in areas of stable transmission. An estimated 64.6M pregnancies were in areas with both P. falciparum and P. vivax transmission. The number of pregnancies at risk of each of P. falciparum and P. vivax worldwide decreased between 2000 and 2020, with the exception of sub-Saharan Africa, where the total number of pregnancies at risk of P. falciparum increased from 37 3M in 2000 to 52 4M in 2020.
Historic investments in malaria control have reduced the number of women at risk of malaria in pregnancy in all endemic regions except sub-Saharan Africa. Population growth in Africa has outpaced reductions in malaria prevalence. Interventions that reduce the risk of malaria in pregnancy are needed as much today as ever.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {The Population Seen from Space: When Satellite Images Come to the Rescue of the Census},
author = {Edith Darin and Mathias Kuépié and Hervé Bassinga and Gianluca Boo and Andrew J. Tatem,},
url = {https://www.cairn-int.info/journal-population-2022-3-page-437.htm},
year  = {2022},
date = {2022-10-30},
urldate = {2023-10-30},
journal = { Population},
volume = {77},
issue = {3},
pages = {437 - 464},
abstract = {Today, developing public policies requires precise knowledge of the size and characteristics of the population. To respond to this need, national statistical offices must perform counts. National censuses are the foundational data collection operations on the number of inhabitants in each country. The national population is the denominator for many development indicators (Carr-Hill, 2014). Reliably and regularly estimating this denominator is important in all domains (land use planning and development, education, democratic representation, social protection, health, etc.) and at various geographical scales (United Nations, 2017). While traditionally the publication of population sizes is organized by administrative units such as provinces or regions, this format leads to spatial discontinuities that can prove arbitrary and that do not reflect other ways of dividing a territory according to criteria such as employment (employment basin) or health (healthcare districts .},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Estimates of the number and distribution of zero-dose and under-immunised children across remote-rural, urban, and conflict-affected settings in low and middle-income countries},
author = {Adelle Wigley and Josh Lorin and Dan Hogan and C. Edson Utazi and Brittany Hagedorn and Emily Dansereau and Andrew J. Tatem and Natalia Tejedor-Garavito},
doi = {10.1371/journal.pgph.0001126},
year  = {2022},
date = {2022-10-26},
urldate = {2022-10-26},
journal = {PLOS Global Public Health},
volume = {2},
issue = {10},
pages = {e0001126},
abstract = {While there has been great success in increasing the coverage of new childhood vaccines globally, expanding routine immunization to reliably reach all children and communities has proven more challenging in many low- and middle-income countries. Achieving this requires vaccination strategies and interventions that identify and target those unvaccinated, guided by the most current and detailed data regarding their size and spatial distribution. Through the integration and harmonisation of a range of geospatial data sets, including population, vaccination coverage, travel-time, settlement type, and conflict locations. We estimated the numbers of children un- or under-vaccinated for measles and diphtheria-tetanus-pertussis, within remote-rural, urban, and conflict-affected locations. We explored how these numbers vary both nationally and sub-nationally, and assessed what proportions of children these categories captured, for 99 lower- and middle-income countries, for which data was available. We found that substantial heterogeneities exist both between and within countries. Of the total 14,030,486 children unvaccinated for DTP1, over 11% (1,656,757) of un- or under-vaccinated children were in remote-rural areas, more than 28% (2,849,671 and 1,129,915) in urban and peri-urban areas, and up to 60% in other settings, with nearly 40% found to be within 1-hour of the nearest town or city (though outside of urban/peri-urban areas). Of the total number of those unvaccinated, we estimated between 6% and 15% (826,976 to 2,068,785) to be in conflict-affected locations, based on either broad or narrow definitions of conflict. Our estimates provide insights into the inequalities in vaccination coverage, with the distributions of those unvaccinated varying significantly by country, region, and district. We demonstrate the need for further inquiry and characterisation of those unvaccinated, the thresholds used to define these, and for more country-specific and targeted approaches to defining such populations in the strategies and interventions used to reach them.},
keywords = {conflict, LMICs, vaccination, zero dose},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Conditional probability and ratio-based approaches for mapping the coverage of multi-dose vaccines},
author = {Chigozie Edson Utazi and Justice Moses K. Aheto and Ho Man Theophilus Chan and Andrew J. Tatem and Sujit K. Sahu},
doi = {10.1002/sim.9586},
year  = {2022},
date = {2022-09-21},
urldate = {2022-09-21},
journal = {Statistics in Medicine},
abstract = {Many vaccines are often administered in multiple doses to boost their effectiveness. In the case of childhood vaccines, the coverage maps of the doses and the differences between these often constitute an evidence base to guide investments in improving access to vaccination services and health system performance in low and middle-income countries. A major problem often encountered when mapping the coverage of multi-dose vaccines is the need to ensure that the coverage maps decrease monotonically with successive doses. 
The fully Bayesian model is implemented using the INLA and SPDE approaches. Using a simulation study, we find that both approaches perform comparably for out-of-sample estimation under varying point-level sample size distributions. We apply the methodology to map the coverage of the three doses of diphtheria-tetanus-pertussis vaccine using data from the 2018 Nigeria Demographic and Health Survey. The coverage maps produced using both approaches are almost indistinguishable, although the CP approach yielded more precise estimates on average in this application. We also provide estimates of zero-dose children and the dropout rates between the doses. The methodology is straightforward to implement and can be applied to other vaccines and geographical contexts.},
keywords = {Bayesian inference, vaccination},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Distance is “a big problem”: a geographic analysis of reported and modelled proximity to maternal health services in Ghana},
author = {Dotse-Gborgbortsi, Winfred and Nilsen, Kristine and Ofosu, Anthony and Matthews, Zoe and Tejedor-Garavito, Natalia and Wright, Jim and Tatem, Andrew J},
doi = {10.1186/s12884-022-04998-0},
year  = {2022},
date = {2022-08-31},
urldate = {2022-08-31},
journal = {BMC Pregnancy and Childbirth},
volume = {22},
number = {672},
abstract = {Geographic barriers to healthcare are associated with adverse maternal health outcomes. Modelling travel times using georeferenced data is becoming common in quantifying physical access. Multiple Demographic and Health Surveys ask women about distance-related problems accessing healthcare, but responses have not been evaluated against modelled travel times. This cross-sectional study aims to compare reported and modelled distance by socio-demographic characteristics and evaluate their relationship with skilled birth attendance. Also, we assess the socio-demographic factors associated with self-reported distance problems in accessing healthcare.

Distance problems and socio-demographic characteristics reported by 2210 women via the 2017 Ghana Maternal Health Survey were included in analysis. Geospatial methods were used to model travel time to the nearest health facility using roads, rivers, land cover, travel speeds, cluster locations and health facility locations. Logistic regressions were used to predict skilled birth attendance and self-reported distance problems.

Women reporting distance challenges accessing healthcare had significantly longer travel times to the nearest health facility. Poverty significantly increased the odds of reporting challenges with distance. In contrast, living in urban areas and being registered with health insurance reduced the odds of reporting distance challenges. Women with a skilled attendant at birth, four or more skilled antenatal appointments and timely skilled postnatal care had shorter travel times to the nearest health facility. Generally, less educated, poor, rural women registered with health insurance had longer travel times to their nearest health facility. After adjusting for socio-demographic characteristics, the following factors increased the odds of skilled birth attendance: wealth, health insurance, higher education, living in urban areas, and completing four or more antenatal care appointments.

Studies relying on modelled travel times to nearest facility should recognise the differential impact of geographic access to healthcare on poor rural women. Physical access to maternal health care should be scaled up in rural areas and utilisation increased by improving livelihoods.},
keywords = {Ghana, maternal health, travel time},
pubstate = {published},
tppubtype = {article}
}
@article{,
title = {Assessing spread risk of COVID-19 within and beyond China in early 2020},
author = {Lai, Shengjie and Bogoch, Isaac I and Ruktanonchai, Nick W and Watts, Alexander and Lu, Xin and Yang, Weizhong and Yu, Hongjie and Khan, Kamran and Tatem, Andrew J},
doi = {10.1016/j.dsm.2022.08.004},
year  = {2022},
date = {2022-08-26},
urldate = {2022-08-26},
journal = {Data Science and Management},
abstract = {A novel coronavirus emerged in Wuhan in late 2019 and has caused the COVID-19 pandemic announced by the World Health Organization on March 12, 2020. This study was originally conducted in January 2020 to estimate the potential risk and geographic range of COVID-19 spread within and beyond China at the early stage of the pandemic. A series of connectivity and risk analyses based on domestic and international travel networks were conducted using historical aggregated mobile phone data and air passenger itinerary data. We found that the cordon sanitaire of Wuhan was likely to have occurred during the latter stages of peak population numbers leaving the city, with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% uncertainty interval: 478–1349) had COVID-19 infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to the city’s lockdown. Most of these destinations were located in Asia, but major hubs in Europe, the US and Australia were also prominent, with a strong correlation seen between the predicted risks of importation and the number of imported cases found. Given the limited understanding of emerging infectious diseases in the very early stages of outbreaks, our approaches and findings in assessing travel patterns and risk of transmission can help guide public health preparedness and intervention design for new COVID-19 waves caused by variants of concern and future pandemics to effectively limit transmission beyond its initial extent.},
keywords = {China, covid-19, Mobility},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Exploring methods for mapping seasonal population changes using mobile phone data},
author = {D. Woods and A. Cunningham and C. E. Utazi and M. Bondarenko and L. Shengjie and G. E. Rogers and P. Koper and C. W. Ruktanonchai and E. zu Erbach-Schoenberg and A. J. Tatem and J. Steele and A. Sorichetta},
doi = {10.1057/s41599-022-01256-8},
year  = {2022},
date = {2022-07-28},
urldate = {2022-07-28},
journal = {Humanities and Social Sciences Communications},
number = {247},
abstract = {Data accurately representing the population distribution at the subnational level within countries is critical to policy and decision makers for many applications. Call data records (CDRs) have shown great promise for this, providing much higher temporal and spatial resolutions compared to traditional data sources. For CDRs to be integrated with other data and in order to effectively inform and support policy and decision making, mobile phone user must be distributed from the cell tower level into administrative units. This can be done in different ways and it is often not considered which method produces the best representation of the underlying population distribution. Using anonymised CDRs in Namibia between 2011 and 2013, four distribution methods were assessed at multiple administrative unit levels. Estimates of user density per administrative unit were ranked for each method and compared against the corresponding census-derived population densities, using Kendall’s tau-b rank tests. Seasonal and trend decomposition using Loess (STL) and multivariate clustering was subsequently used to identify patterns of seasonal user variation and investigate how different distribution methods can impact these. Results show that the accuracy of the results of each distribution method is influenced by the considered administrative unit level. While marginal differences between methods are displayed at “coarser” level 1, the use of mobile phone tower ranges provided the most accurate results for Namibia at finer levels 2 and 3. The use of STL is helpful to recognise the impact of the underlying distribution methods on further analysis, with the degree of consensus between methods decreasing as spatial scale increases. Multivariate clustering delivers valuable insights into which units share a similar seasonal user behaviour. The higher the number of prescribed clusters, the more the results obtained using different distribution methods differ. However, two major seasonal patterns were identified across all distribution methods, levels and most cluster numbers: (a) units with a 15% user decrease in August and (b) units with a 20–30% user increase in December. Both patterns are likely to be partially linked to school holidays and people going on vacation and/or visiting relatives and friends. This study highlights the need and importance of investigating CDRs in detail before conducting subsequent analysis like seasonal and trend decomposition. In particular, CDRs need to be investigated both in terms of their area and population coverage, as well as in relation to the appropriate distribution method to use based on the spatial scale of the specific application. The use of inappropriate methods can change observed seasonal patterns and impact the derived conclusions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {How accurate are WorldPop-Global-Unconstrained gridded population data at the cell-level?: A simulation analysis in urban Namibia},
author = {Dana R. Thomson and Douglas R. Leasure and Tomas Bird and Nikos Tzavidis and Andrew J. Tatem},
doi = {10.1371/journal.pone.0271504},
year  = {2022},
date = {2022-07-21},
journal = {PLoS One},
volume = {17},
issue = {7},
abstract = {Disaggregated population counts are needed to calculate health, economic, and development indicators in Low- and Middle-Income Countries (LMICs), especially in settings of rapid urbanisation. Censuses are often outdated and inaccurate in LMIC settings, and rarely disaggregated at fine geographic scale. Modelled gridded population datasets derived from census data have become widely used by development researchers and practitioners; however, accuracy in these datasets are evaluated at the spatial scale of model input data which is generally courser than the neighbourhood or cell-level scale of many applications. We simulate a realistic synthetic 2016 population in Khomas, Namibia, a majority urban region, and introduce several realistic levels of outdatedness (over 15 years) and inaccuracy in slum, non-slum, and rural areas. We aggregate the synthetic populations by census and administrative boundaries (to mimic census data), resulting in 32 gridded population datasets that are typical of LMIC settings using the WorldPop-Global-Unconstrained gridded population approach. We evaluate the cell-level accuracy of these gridded population datasets using the original synthetic population as a reference. In our simulation, we found large cell-level errors, particularly in slum cells. These were driven by the averaging of population densities in large areal units before model training. Age, accuracy, and aggregation of the input data also played a role in these errors. We suggest incorporating finer-scale training data into gridded population models generally, and WorldPop-Global-Unconstrained in particular (e.g., from routine household surveys or slum community population counts), and use of new building footprint datasets as a covariate to improve cell-level accuracy (as done in some new WorldPop-Global-Constrained datasets). It is important to measure accuracy of gridded population datasets at spatial scales more consistent with how the data are being applied, especially if they are to be used for monitoring key development indicators at neighbourhood scales within cities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Timeliness of routine childhood vaccination in 103 low-and middle-income countries, 1978–2021: A scoping review to map measurement and methodological gaps},
author = {Wariri, O. and Okomo, U. and Kwarshak, Y.K. and Utazi, C.E. and Murray, K. and Grundy, C. and Kampmann, B.},
doi = {10.1371/journal.pgph.0000325},
year  = {2022},
date = {2022-07-14},
urldate = {2022-07-14},
journal = {PLOS Global Public Health},
volume = {2},
issue = {7},
abstract = {Empiric studies exploring the timeliness of routine vaccination in low-and middle-income countries (LMICs) have gained momentum in the last decade. Nevertheless, there is emerging evidence suggesting that these studies have key measurement and methodological gaps that limit their comparability and utility. Hence, there is a need to identify, and document these gaps which could inform the design, conduct, and reporting of future research on the timeliness of vaccination. We synthesised the literature to determine the methodological and measurement gaps in the assessment of vaccination timeliness in LMICs. We searched five electronic databases for peer-reviewed articles in English and French that evaluated vaccination timeliness in LMICs, and were published between 01 January 1978, and 01 July 2021. Two reviewers independently screened titles and abstracts and reviewed full texts of relevant articles, following the guidance framework for scoping reviews by the Joanna Briggs Institute. From the 4263 titles identified, we included 224 articles from 103 countries. China (40), India (27), and Kenya (23) had the highest number of publications respectively. Of the three domains of timeliness, the most studied domain was ‘delayed vaccination’ [99.5% (223/224)], followed by ‘early vaccination’ [21.9% (49/224)], and ‘untimely interval vaccination’ [9% (20/224)]. Definitions for early (seven different definitions), untimely interval (four different definitions), and delayed vaccination (19 different definitions) varied across the studies. Most studies [72.3% (166/224)] operationalised vaccination timeliness as a categorical variable, compared to only 9.8% (22/224) of studies that operationalised timeliness as continuous variables. A large proportion of studies [47.8% (107/224)] excluded the data of children with no written vaccination records irrespective of caregivers’ recall of their vaccination status. Our findings show that studies on vaccination timeliness in LMICs has measurement and methodological gaps. We recommend the development and implement of guidelines for measuring and reporting vaccination timeliness to bridge these gaps.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@workshop{nokey,
title = {SICSS-Covenant: Using gridded population to gain spatial insights in R},
author = {Edith Darin},
url = {https://edarin.github.io/thatsme/posts/2022-06-24-sicss-covenantusing-gridded-population-to-gain-spatial-insights-in-r/},
year  = {2022},
date = {2022-06-24},
abstract = {Access to high-resolution population counts is key for local, national and international decision-making and intervention. It supports data-driven planning of critical infrastructures, such as schools, health facilities and transportation networks.

WorldPop has developed modelling techniques to estimate population in grid cells of 100m by 100m by disaggregating census-based population totals for the entire world, leveraging the growing availability of products derived from satellite imagery. This level of detail offers the advantage of flexible aggregation of the population estimates within different administrative and functional units, for instance, school catchment areas and health zones.},
keywords = {},
pubstate = {published},
tppubtype = {workshop}
}
@article{nokey,
title = {Mapping urban physical distancing constraints, sub-Saharan Africa: a case study from Kenya},
author = {Chamberlain, Heather R and Macharia, Peter M and Tatem, Andrew J},
doi = {10.2471%2FBLT.21.287572},
year  = {2022},
date = {2022-06-22},
urldate = {2022-06-22},
journal = {Bulletin of  the World Health Organisation},
volume = {100},
issue = {9},
abstract = {With the onset of the coronavirus disease 2019 (COVID-19) pandemic, public health measures such as physical distancing were recommended to reduce transmission of the virus causing the disease. However, the same approach in all areas, regardless of context, may lead to measures being of limited effectiveness and having unforeseen negative consequences, such as loss of livelihoods and food insecurity. A prerequisite to planning and implementing effective, context-appropriate measures to slow community transmission is an understanding of any constraints, such as the locations where physical distancing would not be possible. Focusing on sub-Saharan Africa, we outline and discuss challenges that are faced by residents of urban informal settlements in the ongoing COVID-19 pandemic. We describe how new geospatial data sets can be integrated to provide more detailed information about local constraints on physical distancing and can inform planning of alternative ways to reduce transmission of COVID-19 between people. We include a case study for Nairobi County, Kenya, with mapped outputs which illustrate the intra-urban variation in the feasibility of physical distancing and the expected difficulty for residents of many informal settlement areas. Our examples demonstrate the potential of new geospatial data sets to provide insights and support to policy-making for public health measures, including COVID-19.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Small area population denominators for improved disease surveillance and response},
author = {Andrew J Tatem
},
doi = {10.1016/j.epidem.2022.100597},
year  = {2022},
date = {2022-06-17},
journal = {Epidemics},
volume = {40},
abstract = {The Covid-19 pandemic has highlighted the value of strong surveillance systems in supporting our abilities to respond rapidly and effectively in mitigating the impacts of infectious diseases. A cornerstone of such systems is basic subnational scale data on populations and their demographics, which enable the scale of outbreaks to be assessed, risk to specific groups to be determined and appropriate interventions to be designed. Ongoing weaknesses and gaps in such data have however been highlighted by the pandemic. These can include outdated or inaccurate census data and a lack of administrative and registry systems to update numbers, particularly in low and middle income settings. Efforts to design and implement globally consistent geospatial modelling methods for the production of small area demographic data that can be flexibly integrated into health-focussed surveillance and information systems have been made, but these often remain based on outdated population data or uncertain projections. In recent years, efforts have been made to capitalise on advances in computing power, satellite imagery and new forms of digital data to construct methods for estimating small area population distributions across national and regional scales in the absence of full enumeration. These are starting to be used to complement more traditional data collection approaches, especially in the delivery of health interventions, but barriers remain to their widespread adoption and use in disease surveillance and response. Here an overview of these approaches is presented, together with discussion of future directions and needs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Delineating natural catchment health districts with routinely collected health data from women’s travel to give birth in Ghana},
author = {Winfred Dotse-Gborgbortsi and Andrew J. Tatem and Zoë Matthews and Victor Alegana and Anthony Ofosu and Jim Wright },
doi = {10.1186/s12913-022-08125-9},
year  = {2022},
date = {2022-06-13},
urldate = {2022-06-13},
journal = {BMC Health Services Research},
volume = {22},
number = {772},
abstract = {Health service areas are essential for planning, policy and managing public health interventions. In this study, we delineate health service areas from routinely collected health data as a robust geographic basis for presenting access to maternal care indicators.

Methods
A zone design algorithm was adapted to delineate health service areas through a cross-sectional, ecological study design. Health sub-districts were merged into health service areas such that patient flows across boundaries were minimised. Delineated zones and existing administrative boundaries were used to provide estimates of access to maternal health services. We analysed secondary data comprising routinely collected health records from 32,921 women attending 27 hospitals to give birth, spatial demographic data, a service provision assessment on the quality of maternal healthcare and health sub-district boundaries from Eastern Region, Ghana.

Results
Clear patterns of cross border movement to give birth emerged from the analysis, but more women originated closer to the hospitals. After merging the 250 sub-districts in 33 districts, 11 health service areas were created. The minimum percent of internal flows of women giving birth within any health service area was 97.4%. Because the newly delineated boundaries are more “natural” and sensitive to observed flow patterns, when we calculated areal indicator estimates, they showed a marked improvement over the existing administrative boundaries, with the inclusion of a hospital in every health service area.

Conclusion
Health planning can be improved by using routine health data to delineate natural catchment health districts. In addition, data-driven geographic boundaries derived from public health events will improve areal health indicator estimates, planning and interventions.},
keywords = {Ghana, Health, maternal health, Public health},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Untangling the changing impact of non-pharmaceutical interventions and vaccination on European COVID-19 trajectories},
author = {Ge, Yong and Zhang, Wen-Bin and Wu, Xilin and Ruktanonchai, Corrine W and Liu, Haiyan and Wang, Jianghao and Song, Yongze and Liu, Mengxiao and Yan, Wei and Yang, Juan and Cleary, Eimear and Qader, Sarchil H. and Atuhaire, Fatumah and Ruktanonchai, Nick W. and Tatem, Andrew J. and Lai Shengjie },
doi = {10.1038/s41467-022-30897-1},
year  = {2022},
date = {2022-06-03},
urldate = {2022-06-03},
journal = {Nature Communications},
volume = {13},
issue = {3106},
abstract = {Non-pharmaceutical interventions (NPIs) and vaccination are two fundamental approaches for mitigating the coronavirus disease 2019 (COVID-19) pandemic. However, the real-world impact of NPIs versus vaccination, or a combination of both, on COVID-19 remains uncertain. To address this, we built a Bayesian inference model to assess the changing effect of NPIs and vaccination on reducing COVID-19 transmission, based on a large-scale dataset including epidemiological parameters, virus variants, vaccines, and climate factors in Europe from August 2020 to October 2021. We found that (1) the combined effect of NPIs and vaccination resulted in a 53% (95% confidence interval: 42–62%) reduction in reproduction number by October 2021, whereas NPIs and vaccination reduced the transmission by 35% and 38%, respectively; (2) compared with vaccination, the change of NPI effect was less sensitive to emerging variants; (3) the relative effect of NPIs declined 12% from May 2021 due to a lower stringency and the introduction of vaccination strategies. Our results demonstrate that NPIs were complementary to vaccination in an effort to reduce COVID-19 transmission, and the relaxation of NPIs might depend on vaccination rates, control targets, and vaccine effectiveness concerning extant and emerging variants.},
keywords = {covid-19, Europe, NPIs, vaccination},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Who and which regions are at high risk of returning to poverty during the COVID-19 pandemic?},
author = {Ge, Yong and Liu, Mengxiao and Hu, Shan and Wang, Daoping and Wang, Jinfeng and Wang, Xiaolin and Qader, Sarchil and Cleary, Eimear and Tatem, Andrew J. and Lai, Shengjie},
doi = {https://doi.org/10.1057/s41599-022-01205-5},
year  = {2022},
date = {2022-05-25},
urldate = {2022-05-25},
journal = {Humanities and Social Sciences Communications},
volume = {9},
number = {183},
abstract = {Pandemics such as COVID-19 and their induced lockdowns/travel restrictions have a significant impact on people’s lives, especially for lower-income groups who lack savings and rely heavily on mobility to fulfill their daily needs. Taking the COVID-19 pandemic as an example, this study analysed the risk of returning to poverty for low-income households in Hubei Province in China as a result of the COVID-19 lockdown. Employing a dataset including information on 78,931 government-identified poor households, three scenarios were analysed in an attempt to identify who is at high risk of returning to poverty, where they are located, and how the various risk factors influence their potential return to poverty. The results showed that the percentage of households at high risk of returning to poverty (falling below the poverty line) increased from 5.6% to 22% due to a 3-month lockdown. This vulnerable group tended to have a single source of income, shorter working hours, and more family members. Towns at high risk (more than 2% of households returning to poverty) doubled (from 27.3% to 46.9%) and were mainly located near railway stations; an average decrease of 10–50 km in the distance to the nearest railway station increased the risk from 1.8% to 9%. These findings, which were supported by the representativeness of the sample and a variety of robustness tests, provide new information for policymakers tasked with protecting vulnerable groups at high risk of returning to poverty and alleviating the significant socio-economic consequences of future pandemics.},
keywords = {covid-19},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Multilevel analysis of predictors of multiple indicators of childhood vaccination in Nigeria},
author = {Justice M. K. Aheto and Oliver Pannell and Winfred Dotse-Gborgbortsi and Mary K. Trimner and Andrew J. Tatem and Dale A. Rhoda and Felicity T. Cutts and C Edson Utazi},
doi = {https://doi.org/10.1371/journal.pone.0269066},
year  = {2022},
date = {2022-05-25},
urldate = {2022-05-25},
journal = {PLoS ONE},
volume = {15},
number = {5},
pages = {e0269066},
abstract = {Substantial inequalities exist in childhood vaccination coverage levels. To increase vaccine uptake, factors that predict vaccination coverage in children should be identified and addressed.

Methods
Using data from the 2018 Nigeria Demographic and Health Survey and geospatial data sets, we fitted Bayesian multilevel binomial and multinomial logistic regression models to analyse independent predictors of three vaccination outcomes: receipt of the first dose of Pentavalent vaccine (containing diphtheria-tetanus-pertussis, Hemophilus influenzae type B and Hepatitis B vaccines) (PENTA1) (n = 6059) and receipt of the third dose having received the first (PENTA3/1) (n = 3937) in children aged 12–23 months, and receipt of measles vaccine (MV) (n = 11839) among children aged 12–35 months.

Results
Factors associated with vaccination were broadly similar for documented versus recall evidence of vaccination. Based on any evidence of vaccination, we found that health card/document ownership, receipt of vitamin A and maternal educational level were significantly associated with each outcome. Although the coverage of each vaccine dose was higher in urban than rural areas, urban residence was not significant in multivariable analyses that included travel time. Indicators relating to socio-economic status, as well as ethnic group, skilled birth attendance, lower travel time to the nearest health facility and problems seeking health care were significantly associated with both PENTA1 and MV. Maternal religion was related to PENTA1 and PENTA3/1 and maternal age related to MV and PENTA3/1; other significant variables were associated with one outcome each. Substantial residual community level variances in different strata were observed in the fitted models for each outcome.

Conclusion
Our analysis has highlighted socio-demographic and health care access factors that affect not only beginning but completing the vaccination series in Nigeria. Other factors not measured by the DHS such as health service quality and community attitudes should also be investigated and addressed to tackle inequities in coverage.},
keywords = {Nigeria, Predictive clustering, vaccination},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Characterising the Land Surface Phenology of Middle Eastern Countries Using Moderate Resolution Landsat Data},
author = {Qader, Sarchil Hama and Priyatikanto, Rhorom and Khwarahm, Nabaz R. and Tatem, Andrew J. and Dash, Jadunandan},
doi = {10.3390/rs14092136},
year  = {2022},
date = {2022-04-28},
urldate = {2022-04-28},
journal = {Remote Sensing},
volume = {14},
number = {9},
abstract = {Global change impacts including climate change, increased CO2 and nitrogen deposition can be determined through a more precise characterisation of Land Surface Phenology (LSP) parameters. In addition, accurate estimation of LSP dates is being increasingly used in applications such as mapping vegetation types, yield forecasting, and irrigation management. However, there has not been any attempt to characterise Middle East vegetation phenology at the fine spatial resolution appropriate for such applications. Remote-sensing based approaches have proved to be a useful tool in such regions since access is restricted in some areas due to security issues and their inter-annual vegetation phenology parameters vary considerably because of high uncertainty in rainfall. This study aims to establish for the first time a comprehensive characterisation of the vegetation phenological characteristics of the major vegetation types in the Middle East at a fine spatial resolution of 30 m using Landsat Normalized Difference Vegetation Index (NDVI) time series data over a temporal range of 20 years (2000 - 2020). Overall, a progressive pattern in phenophases was observed from low to high latitude. The earliest start of the season was concentrated in the central and east of the region associated mainly with grassland and cultivated land, while the significantly delayed end of the season was mainly distributed in northern Turkey and Iran corresponding to the forest, resulting in the prolonged length of the season in the study area. There was a significant positive correlation between LSP parameters and latitude, which indicates a delay in the start of the season of 4.83 days (R2 = 0.86, p < 0.001) and a delay in the end of the season of 6.54 days (R2 = 0.83, p < 0.001) per degree of latitude increase. In addition, we have discussed the advantages of fine resolution LSP parameters over the available coarse datasets and showed how such outputs can improve many applications in the region. This study shows the potential of Landsat data to quantify the LSP of major land cover types in heterogeneous landscapes of the Middle East which enhances our understanding of the spatial-temporal dynamics of vegetation dynamics in arid and semi-arid settings in the world.},
keywords = {Iran, Landsat, phenology, Turkey},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Assessing the characteristics of un- and under-vaccinated children in low- and middle-income countries: A multi-level cross-sectional study},
author = {Utazi, C Edson and Pannell, Oliver and Aheto, Justice MK and Wigley, Adelle and Tejedor-Garavito, Natalia and Wunderlich, Josh and Hagedorn, Brittany and Hogan, Dan and and Tatem, Andrew J. },
doi = {https://doi.org/10.1371/journal.pgph.0000244},
year  = {2022},
date = {2022-04-27},
urldate = {2022-04-27},
journal = {PLoS Global Public Health},
volume = {2},
number = {4},
pages = {e0000244},
abstract = {Achieving equity in vaccination coverage has been a critical priority within the global health community. Despite increased efforts recently, certain populations still have a high proportion of un- and under-vaccinated children in many low- and middle-income countries (LMICs). These populations are often assumed to reside in remote-rural areas, urban slums and conflict-affected areas. Here, we investigate the effects of these key community-level factors, alongside a wide range of other individual, household and community level factors, on vaccination coverage. Using geospatial datasets, including cross-sectional data from the most recent Demographic and Health Surveys conducted between 2008 and 2018 in nine LMICs, we fitted Bayesian multi-level binary logistic regression models to determine key community-level and other factors significantly associated with non- and under-vaccination. We analyzed the odds of receipt of the first doses of diphtheria-tetanus-pertussis (DTP1) vaccine and measles-containing vaccine (MCV1), and receipt of all three recommended DTP doses (DTP3) independently, in children aged 12–23 months. In bivariate analyses, we found that remoteness increased the odds of non- and under-vaccination in nearly all the study countries. We also found evidence that living in conflict and urban slum areas reduced the odds of vaccination, but not in most cases as expected. However, the odds of vaccination were more likely to be lower in urban slums than formal urban areas. Our multivariate analyses revealed that the key community variables–remoteness, conflict and urban slum–were sometimes associated with non- and under-vaccination, but they were not frequently predictors of these outcomes after controlling for other factors. Individual and household factors such as maternal utilization of health services, maternal education and ethnicity, were more common predictors of vaccination. Reaching the Immunisation Agenda 2030 target of reducing the number of zero-dose children by 50% by 2030 will require country tailored analyses and strategies to identify and reach missed communities with reliable immunisation services.},
keywords = {Demographic and Health Surveys, LMICs, vaccination},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {High-resolution population estimation using household survey data and building footprints},
author = {Boo, Gianluca and Darin, Edith and Leasure, Douglas R and Dooley, Claire A and Chamberlain, Heather R and and Lázár, Attila N and Tschirhart, Kevin and Sinai, Cyrus and Hoff, Nicole A and Fuller, Trevon},
doi = {https://doi.org/10.1038/s41467-022-29094-x},
year  = {2022},
date = {2022-03-14},
urldate = {2022-03-14},
journal = {Nature Communications},
volume = {13},
number = {1330},
abstract = {The national census is an essential data source to support decision-making in many areas of public interest. However, this data may become outdated during the intercensal period, which can stretch up to several decades. In this study, we develop a Bayesian hierarchical model leveraging recent household surveys and building footprints to produce up-to-date population estimates. We estimate population totals and age and sex breakdowns with associated uncertainty measures within grid cells of approximately 100 m in five provinces of the Democratic Republic of the Congo, a country where the last census was completed in 1984. The model exhibits a very good fit, with an R2 value of 0.79 for out-of-sample predictions of population totals at the microcensus-cluster level and 1.00 for age and sex proportions at the province level. This work confirms the benefits of combining household surveys and building footprints for high-resolution population estimation in countries with outdated censuses.},
keywords = {Bayesian inference, Demographic and Health Surveys, Population},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Understanding factors associated with attending secondary school in Tanzania using household survey data},
author = {Carla Pezzulo and Victor A Alegana and Andrew Christensen and Omar Bakari and Andrew Tatem},
doi = {http://dx.doi.org/10.1371/journal.pone.0263734},
year  = {2022},
date = {2022-02-25},
urldate = {2022-02-25},
journal = {PLoS ONE},
volume = {17},
number = {2},
abstract = {Sustainable Development Goal (SDG) 4 aims to ensure inclusive and equitable access for all by 2030, leaving no one behind. One indicator selected to measure progress towards achievement is the participation rate of youth in education (SDG 4.3.1). Here we aim to understand drivers of school attendance using one country in East Africa as an example.

Methods
Nationally representative household survey data (2015–16 Tanzania Demographic and Health Survey) were used to explore individual, household and contextual factors associated with secondary school attendance in Tanzania. These included, age, head of household’s levels of education, gender, household wealth index and total number of children under five. Contextual factors such as average pupil to qualified teacher ratio and geographic access to school were also tested at cluster level. A two-level random intercept logistic regression model was used in exploring association of these factors with attendance in a multi-level framework.

Results
Age of household head, educational attainments of either of the head of the household or parent, child characteristics such as gender, were important predictors of secondary school attendance. Being in a richer household and with fewer siblings of lower age (under the age of 5) were associated with increased odds of attendance (OR = 0.91, CI 95%: 0.86; 0.96). Contextual factors were less likely to be associated with secondary school attendance.

Conclusions
Individual and household level factors are likely to impact secondary school attendance rates more compared to contextual factors, suggesting an increased focus of interventions at these levels is needed. Future studies should explore the impact of interventions targeting these levels. Policies should ideally promote gender equality in accessing secondary school as well as support those families where the dependency ratio is high. Strategies to reduce poverty will also increase the likelihood of attending school.},
keywords = {Africa, Demographic and Health Surveys, education, SDG4, Tanzania},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Unmet need for COVID-19 vaccination coverage in Kenya},
author = {Samuel K. Muchiri and Rose Muthee and Hellen Kiarie and Joseph Sitienei and Ambrose Agweyu and Peter M. Atkinson and C. {Edson Utazi} and Andrew J. Tatem and Victor A. Alegana},
doi = {https://doi.org/10.1016/j.vaccine.2022.02.035},
issn = {0264-410X},
year  = {2022},
date = {2022-02-14},
urldate = {2022-02-14},
journal = {Vaccine},
volume = {40},
number = {13},
abstract = {COVID-19 has impacted the health and livelihoods of billions of people since it emerged in 2019. Vaccination for COVID-19 is a critical intervention that is being rolled out globally to end the pandemic. Understanding the spatial inequalities in vaccination coverage and access to vaccination centres is important for planning this intervention nationally. Here, COVID-19 vaccination data, representing the number of people given at least one dose of vaccine, a list of the approved vaccination sites, population data and ancillary GIS data were used to assess vaccination coverage, using Kenya as an example. Firstly, physical access was modelled using travel time to estimate the proportion of population within 1 hour of a vaccination site. Secondly, a Bayesian conditional autoregressive (CAR) model was used to estimate the COVID-19 vaccination coverage and the same framework used to forecast coverage rates for the first quarter of 2022. Nationally, the average travel time to a designated COVID-19 vaccination site (n = 622) was 75.5 min (Range: 62.9 – 94.5 min) and over 87% of the population >18 years reside within 1 hour to a vaccination site. The COVID-19 vaccination coverage in December 2021 was 16.70% (95% CI: 16.66 – 16.74) – 4.4 million people and was forecasted to be 30.75% (95% CI: 25.04 – 36.96) – 8.1 million people by the end of March 2022. Approximately 21 million adults were still unvaccinated in December 2021 and, in the absence of accelerated vaccine uptake, over 17.2 million adults may not be vaccinated by end March 2022 nationally. Our results highlight geographic inequalities at sub-national level and are important in targeting and improving vaccination coverage in hard-to-reach populations. Similar mapping efforts could help other countries identify and increase vaccination coverage for such populations.},
keywords = {Africa, covid-19, Kenya, travel time, vaccination},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mapping the prevalence of severe acute malnutrition in Papua, Indonesia by using geostatistical models},
author = {Jasper, Paul and Jochem, Warren C and Lambert-Porter, Emma and Naeem, Umer and Utazi, Chigozie Edson},
doi = {https://doi.org/10.1186/s40795-022-00504-z},
year  = {2022},
date = {2022-02-14},
urldate = {2022-02-14},
journal = {BMC Nutrition},
volume = {8},
number = {13},
abstract = {Severe acute malnutrition (SAM) is the most life-threatening form of malnutrition, and in 2019, approximately 14.3 million children under the age of 5 were considered to have SAM. The prevalence of child malnutrition is recorded through large-scale household surveys run at multi-year intervals. However, these surveys are expensive, yield estimates with high levels of aggregation, are run over large time intervals, and may show gaps in area coverage. Geospatial modelling approaches could address some of these challenges by combining geo-located survey data with geospatial data to produce mapped estimates that predict malnutrition risk in both surveyed and non-surveyed areas.

Methods
A secondary analysis of cluster-level program evaluation data (n = 123 primary sampling units) was performed to map severe acute malnutrition (SAM) in Papuan children under 2 years (0–23 months) of age with a spatial resolution of 1 × 1 km in Papua, Indonesia. The approach used Bayesian geostatistical modelling techniques and publicly available geospatial data layers.

Results
In Papua, Indonesia, SAM was predicted in geostatistical models by using six geospatial covariates related primarily to conditions of remoteness and inaccessibility. The predicted 1-km spatial resolution maps of SAM showed substantial spatial variation across the province. By combining the predicted rates of SAM with estimates of the population under 2 years of age, the prevalence of SAM in late 2018 was estimated to be around 15,000 children (95% CI 10,209–26,252). Further tests of the predicted levels suggested that in most areas of Papua, more than 5% of Papuan children under 2 years of age had SAM, while three districts likely had more than 15% of children with SAM.

Conclusions
Eradication of hunger and malnutrition remains a key development goal, and more spatially detailed data can guide efficient intervention strategies. The application of additional household survey datasets in geostatistical models is one way to improve the monitoring and timely estimation of populations at risk of malnutrition. Importantly, geospatial mapping can yield insights for both surveyed and non-surveyed areas and can be applied in low-income country contexts where data is scarce and data collection is expensive or regions are inaccessible.},
keywords = {Asia, Bayesian geostatistics, Demographic and Health Surveys, Indonesia, malnutrition, Papua},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Influenza’s plummeting during the COVID-19 pandemic: The roles of mask-wearing, mobility change, and SARS-CoV-2 interference},
author = {Shasha Han and Ting Zhang and Yan Lyu and Shengjie Lai and Peixi Dai and Jiandong Zheng and Weizhong Yang and Xiao-Hua Zhou and Luzhao Feng},
doi = {https://doi.org/10.1016/j.eng.2021.12.011},
issn = {2095-8099},
year  = {2022},
date = {2022-02-02},
urldate = {2022-02-02},
journal = {Engineering},
abstract = {Seasonal influenza activity typically peaks in the winter months but plummeted globally during the current coronavirus disease 2019 (COVID-19) pandemic. Unraveling lessons from influenza’s unprecedented low profile is critical in informing preparedness for incoming influenza seasons. Here, we explored a country-specific inference model to estimate the effects of mask-wearing, mobility changes (international and domestic), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) interference in China, England, and the United States. We found that a one-week increase in mask-wearing intervention had a percent reduction of 11.3%–35.2% in influenza activity in these areas. The one-week mobility mitigation had smaller effects for the international (1.7%–6.5%) and the domestic community (1.6%–2.8%). In 2020–2021, the mask-wearing intervention alone could decline percent positivity by 13.3–19.8. The mobility change alone could reduce percent positivity by 5.2–14.0, of which 79.8%–98.2% were attributed to the deflected international travel. Only in 2019–2020, SARS-CoV-2 interference had statistically significant effects. There was a reduction in percent positivity of 7.6 (2.4–14.4) and 10.2 (7.2–13.6) in northern China and England, respectively. Our results have implications for understanding how influenza evolves under non-pharmaceutical interventions and other respiratory diseases and will inform health policy and the design of tailored public health measures.},
keywords = {covid-19, influenza, NPIs},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Towards an Improved Large-Scale Gridded Population Dataset: A Pan-European Study on the Integration of 3D Settlement Data into Population Modelling},
author = {Palacios-Lopez, Daniela and Esch, Thomas and MacManus, Kytt and Marconcini, Mattia and Sorichetta, Alessandro and Yetman, Greg and Zeidler, Julian and Dech, Stefan and Tatem, Andrew J. and and Reinartz, Peter},
doi = {10.3390/rs14020325},
issn = {2072-4292},
year  = {2022},
date = {2022-01-20},
urldate = {2022-01-20},
journal = {Remote Sensing},
volume = {14},
number = {2},
abstract = {Large-scale gridded population datasets available at the global or continental scale have become an important source of information in applications related to sustainable development. In recent years, the emergence of new population models has leveraged the inclusion of more accurate and spatially detailed proxy layers describing the built-up environment (e.g., built-area and building footprint datasets), enhancing the quality, accuracy and spatial resolution of existing products. However, due to the consistent lack of vertical and functional information on the built-up environment, large-scale gridded population datasets that rely on existing built-up land proxies still report large errors of under- and overestimation, especially in areas with predominantly high-rise buildings or industrial/commercial areas, respectively. This research investigates, for the first time, the potential contributions of the new World Settlement Footprint—3D (WSF3D) dataset in the field of large-scale population modelling. First, we combined a Random Forest classifier with spatial metrics derived from the WSF3D to predict the industrial versus non-industrial use of settlement pixels at the Pan-European scale. We then examined the effects of including volume and settlement use information into frameworks of dasymetric population modelling. We found that the proposed classification method can predict industrial and non-industrial areas with overall accuracies and a kappa-coefficient of ~84% and 0.68, respectively. Additionally, we found that both, integrating volume and settlement use information considerably increased the accuracy of population estimates between 10% and 30% over commonly employed models (e.g., based on a binary settlement mask as input), mainly by eliminating systematic large overestimations in industrial/commercial areas. While the proposed method shows strong promise for overcoming some of the main limitations in large-scale population modelling, future research should focus on improving the quality of the WFS3D dataset and the classification method alike, to avoid the false detection of built-up settlements and to reduce misclassification errors of industrial and high-rise buildings.},
keywords = {Europe, Population, Random forest},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Global holiday datasets for understanding seasonal human mobility and population dynamics},
author = {Lai, Shengjie and Sorichetta, Alessandro and Steele, Jessica and Ruktanonchai, Corrine W and Cunningham, Alexander D and Rogers, Grant and Koper, Patrycja and Woods, Dorothea and Bondarenko, Maksym and Ruktanonchai, Nick W and Shi, Weifeng and and Tatem, Andrew J.},
doi = {https://doi.org/10.1038/s41597-022-01120-z},
year  = {2022},
date = {2022-01-20},
urldate = {2022-01-20},
journal = {Scientific Data},
volume = {9},
number = {17},
abstract = {Public and school holidays have important impacts on population mobility and dynamics across multiple spatial and temporal scales, subsequently affecting the transmission dynamics of infectious diseases and many socioeconomic activities. However, worldwide data on public and school holidays for understanding their changes across regions and years have not been assembled into a single, open-source and multitemporal dataset. To address this gap, an open access archive of data on public and school holidays in 2010–2019 across the globe at daily, weekly, and monthly timescales was constructed. Airline passenger volumes across 90 countries from 2010 to 2018 were also assembled to illustrate the usage of the holiday data for understanding the changing spatiotemporal patterns of population movements.},
keywords = {holidays, Mobility, Population},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Etiological and epidemiological features of acute meningitis or encephalitis in China: a nationwide active surveillance study},
author = {Wang, Li-Ping and Yuan, Yang and Liu, Ying-Le and Lu, Qing-Bin and Shi, Lu-Sha and Ren, Xiang and Zhou, Shi-Xia and Zhang, Hai-Yang and Zhang, Xiao-Ai and Wang, Xin and Wang, Yi-Fei and Lin, Sheng-Hong and Zhang, Cui-Hong and Geng, Meng-Jie and Li, Jun and Zhao, Shi-Wen and Yi, Zhi-Gang and Chen, Xiao and Yang, Zuo-Sen and Meng, Lei and Wang, Xin-Hua and Cui, Ai-Li and Lai, Sheng-Jie and and others},
doi = {https://doi.org/10.1016/j.lanwpc.2021.100361},
year  = {2022},
date = {2022-01-03},
urldate = {2022-01-03},
journal = {The Lancet Regional Health-Western Pacific},
volume = {20},
number = {100361},
abstract = {Acute meningitis or encephalitis (AME) results from a neurological infection causing high case fatality and severe sequelae. AME lacked comprehensive surveillance in China.
Methods
Nation-wide surveillance of all-age patients with AME syndromes was conducted in 144 sentinel hospitals of 29 provinces in China. Eleven AME-causative viral and bacterial pathogens were tested with multiple diagnostic methods.
Findings
Between 2009 and 2018, 20,454 AME patients were recruited for tests. Based on 9,079 patients with all-four-virus tested, 28.43% (95% CI: 27.50%‒29.36%) of them had at least one virus-positive detection. Enterovirus was the most frequently determined virus in children <18 years, herpes simplex virus and Japanese encephalitis virus were the most frequently determined in 18−59 and ≥60 years age groups, respectively. Based on 6,802 patients with all-seven-bacteria tested, 4.43% (95% CI: 3.94%‒4.91%) had at least one bacteria-positive detection, Streptococcus pneumoniae and Neisseria meningitidis were the leading bacterium in children aged <5 years and 5−17 years, respectively. Staphylococcus aureus was the most frequently detected in adults aged 18−59 and ≥60 years. The pathogen spectrum also differed statistically significantly between northern and southern China. Joinpoint analysis revealed age-specific positive rates, with enterovirus, herpes simplex virus and mumps virus peaking at 3−6 years old, while Japanese encephalitis virus peaked in the ≥60 years old. As age increased, the positive rate for Streptococcus pneumoniae and Escherichia coli statistically significantly decreased, while for Staphylococcus aureus and Streptococcus suis it increased.
Interpretation
The current findings allow enhanced identification of the predominant AME-related pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures in China, and a possible reassessment of vaccination strategy.},
keywords = {Asia, China, Demographic and Health Surveys, infectious disease},
pubstate = {published},
tppubtype = {article}
}
@online{nokey,
title = {Exploring international travel patterns and connected communities for understanding the spreading risk of VOC Omicron},
author = {Lai, Shengjie and Li, Zhenlong and Cleary, Eimear and Bondarenko, Maksym and and Tatem, Andrew J },
url = {/events/covid_omicron},
year  = {2021},
date = {2021-12-17},
urldate = {2021-12-17},
journal = {WorldPop Research},
abstract = {The novel SARS-CoV-2 variant of concern (VOC) Omicron (lineage B.1.1.529), together with four existing VOC variants, has raised serious concerns about the effectiveness of vaccines and the potential for a new wave of the pandemic. This new strain was first detected in in November 2021 in South Africa and among international cases with a travel history from southern African countries. However, community transmission with associated clusters has now been reported in several countries. According to the COVID-19 Weekly Epidemiological Update published by the WHO, a total of 76 countries have reported confirmed cases of the Omicron variant, as of December 14, 2021 },
keywords = {covid-19, vaccination},
pubstate = {published},
tppubtype = {online}
}
@article{nokey,
title = {Impacts of worldwide individual non-pharmaceutical interventions on COVID-19 transmission across waves and space},
author = {Yong Ge and Wen-Bin Zhang and Haiyan Liu and Corrine W Ruktanonchai and Maogui Hu and Xilin Wu and Yongze Song and Nick W Ruktanonchai and Wei Yan and Eimear Cleary and Luzhao Feng and Zhongjie Li and Weizhong Yang and Mengxiao Liu and Andrew J Tatem and Jin-Feng Wang and and Shengjie Lai},
doi = {https://doi.org/10.1016/j.jag.2021.102649},
year  = {2021},
date = {2021-12-13},
urldate = {2021-12-13},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {106},
abstract = {Governments worldwide have rapidly deployed non-pharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic. However, the effect of these individual NPI measures across space and time has yet to be sufficiently assessed, especially with the increase of policy fatigue and the urge for NPI relaxation in the vaccination era. Using the decay ratio in the suppression of COVID-19 infections and multi-source big data, we investigated the changing performance of different NPIs across waves from global and regional levels (in 133 countries) to national and subnational (in the United States of America [USA]) scales before the implementation of mass vaccination. The synergistic effectiveness of all NPIs for reducing COVID-19 infections declined along waves, from 95.4% in the first wave to 56.0% in the third wave recently at the global level and similarly from 83.3% to 58.7% at the USA national level, while it had fluctuating performance across waves on regional and subnational scales. Regardless of geographical scale, gathering restrictions and facial coverings played significant roles in epidemic mitigation before the vaccine rollout. Our findings have important implications for continued tailoring and implementation of NPI strategies, together with vaccination, to mitigate future COVID-19 waves, caused by new variants, and other emerging respiratory infectious diseases.},
keywords = {covid-19, NPIs},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {The emergence, genomic diversity and global spread of SARS-CoV-2},
author = {Li, Juan and Lai, Shengjie and Gao, George F and and Shi, Weifeng},
doi = {https://doi.org/10.1038/s41586-021-04188-6},
year  = {2021},
date = {2021-12-08},
urldate = {2021-12-08},
journal = {Nature},
volume = {600},
pages = {408–418},
abstract = {Since the first cases of COVID-19 were documented in Wuhan, China in 2019, the world has witnessed a devastating global pandemic, with more than 238 million cases, nearly 5 million fatalities and the daily number of people infected increasing rapidly. Here we describe the currently available data on the emergence of the SARS-CoV-2 virus, the causative agent of COVID-19, outline the early viral spread in Wuhan and its transmission patterns in China and across the rest of the world, and highlight how genomic surveillance, together with other data such as those on human mobility, has helped to trace the spread and genetic variation of the virus and has also comprised a key element for the control of the pandemic. We pay particular attention to characterizing and describing the international spread of the major variants of concern of SARS-CoV-2 that were first identified in late 2020 and demonstrate that virus evolution has entered a new phase. More broadly, we highlight our currently limited understanding of coronavirus diversity in nature, the rapid spread of the virus and its variants in such an increasingly connected world, the reduced protection of vaccines, and the urgent need for coordinated global surveillance using genomic techniques. In summary, we provide important information for the prevention and control of both the ongoing COVID-19 pandemic and any new diseases that will inevitably emerge in the human population in future generations.},
keywords = {covid-19, Mobility},
pubstate = {published},
tppubtype = {article}
}
@conference{nokey,
title = {A bottom-up population modelling approach to complement the population and housing census},
author = {Darin, Edith and Boo, Gianluca and Tatem, Andrew J},
url = {https://ipc2021.popconf.org/abstracts/210325},
year  = {2021},
date = {2021-12-07},
urldate = {2021-12-07},
booktitle = {IUSSP , International Population Conference 2021},
abstract = {The population and housing census provides essential demographic information for decision-making and action at local, national and international levels. However, census data in the most vulnerable countries is often outdated or partial because political instability, conflict and natural disasters prevent a national count. The bottom-up modeling approach helps supplement outdated or incomplete census data by estimating population counts and age/sex structures in approximately 100m grid cells using population data collected over a set fully enumerated places and auxiliary geospatial covariates. We present the modeling effort carried out in the Democratic Republic of Congo — the last census was carried out in 1984 — and in Burkina Faso — the last census was carried out in 2020 but only covered 70% of the country. Both models showed good predictive performance, indicated by R2 values ​​of 0.73 and 0.63 for the respective out-of-sample predictions of population counts. The resulting bottom-up, gridded population estimates are currently used for census support and humanitarian response in both countries. This work highlights the flexibility of the bottom-up modeling approach, in terms of input population data, model specification, and aggregation of population estimates to support specific use cases.},
keywords = {Africa, bottom-up modelling, census, Democratic Republic of Congo},
pubstate = {published},
tppubtype = {conference}
}
@article{nokey,
title = {Domestic and international mobility trends in the United Kingdom during the COVID-19 pandemic: an analysis of facebook data},
author = {Shepherd, Harry ER and Atherden, Florence S and Chan, Ho Man Theophilus and Loveridge, Alexandra and Tatem, Andrew J},
doi = {10.1186/s12942-021-00299-5},
year  = {2021},
date = {2021-12-04},
urldate = {2021-12-04},
journal = {International Journal of Health Geographics},
volume = {20},
number = {46},
abstract = {Since early March 2020, the COVID-19 epidemic across the United Kingdom has led to a range of social distancing policies, which resulted in changes to mobility across different regions. An understanding of how these policies impacted travel patterns over time and at different spatial scales is important for designing effective strategies, future pandemic planning and in providing broader insights on the population geography of the country. Crowd level data on mobile phone usage can be used as a proxy for population mobility patterns and provide a way of quantifying in near-real time the impact of social distancing measures on changes in mobility.

Methods
Here we explore patterns of change in densities, domestic and international flows and co-location of Facebook users in the UK from March 2020 to March 2021.

Results
We find substantial heterogeneities across time and region, with large changes observed compared to pre-pademic patterns. The impacts of periods of lockdown on distances travelled and flow volumes are evident, with each showing variations, but some significant reductions in co-location rates. Clear differences in multiple metrics of mobility are seen in central London compared to the rest of the UK, with each of Scotland, Wales and Northern Ireland showing significant deviations from England at times. Moreover, the impacts of rapid changes in rules on international travel to and from the UK are seen in substantial fluctuations in traveller volumes by destination.

Conclusions
While questions remain about the representativeness of the Facebook data, previous studies have shown strong correspondence with census-based data and alternative mobility measures, suggesting that findings here are valuable for guiding strategies.},
keywords = {covid-19, Europe, Mobility, NPIs, United Kingdom},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Methods and Applications: The Incoming Influenza Season — China, the United Kingdom, and the United States, 2021–2022},
author = {Han, Shasha and Zhang, Ting and Lyu, Yan and Lai, Shengjie and Dai, Peixi and Zheng, Jiandong and Yang, Weizhong and Zhou, Xiaohua and Feng, Luzhao},
doi = {10.46234/ccdcw2021.253},
year  = {2021},
date = {2021-12-03},
urldate = {2021-12-03},
journal = {China CDC Weekly},
volume = {3},
number = {49},
pages = {1039},
abstract = {Seasonal influenza activity has declined globally since the widespread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission. There has been scarce information to understand the future dynamics of influenza — and under different hypothesis on relaxation of non-pharmaceutical interventions (NPIs) in particular — after the disruptions to seasonal patterns.

Methods
We collected data from public sources in China, the United Kingdom, and the United States, and forecasted the influenza dynamics in the incoming 2021–2022 season under different NPIs. We considered Northern China and Southern China separately, due to the sharp difference in the patterns of seasonal influenza. For the United Kingdom, data were collected for England only.

Results
Compared to the epidemics in 2017–2019, longer and blunter influenza outbreaks could occur should NPIs be fully lifted, with percent positivity varying from 10.5 to 18.6 in the studying regions. The rebounds would be smaller if the mask-wearing intervention continued or the international mobility stayed low, but sharper if the mask-wearing intervention was lifted in the middle of influenza season. Further, influenza activity could stay low under a much less stringent mask-wearing intervention coordinated with influenza vaccination.

Conclusions
The results added to our understandings of future influenza dynamics after the global decline during the coronavirus disease 2019 (COVID-19) pandemic. In light of the uncertainty on the incoming circulation strains and the relatively low negative impacts of mask wearing on society, our findings suggested that wearing mask could be considered as an accompanying mitigation measure in influenza prevention and control, especially for seasons after long periods of low-exposure to influenza viruses.},
keywords = {influenza, Mobility, NPIs, Predictive clustering},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Rapid and sustained containment of covid-19 is achievable and worthwhile: implications for pandemic response},
author = {Chen, Qiulan and Rodewald, Lance and Lai, Shengjie and Gao, George F},
doi = {https://doi.org/10.1136/BMJ-2021-066169},
year  = {2021},
date = {2021-12-02},
urldate = {2021-12-02},
journal = {BMJ},
volume = {375},
number = {e066169},
abstract = {In the 20 months since the World Health Organization declared covid-19 to be a pandemic, more than 250 million cases have been confirmed. More than 5 million deathshave occurred disproportionately by country, with 10% of countries reporting 80% of cases.1 In a new pandemic, with no vaccines prepared, non-pharmaceutical interventions are the only response. In the first year of the covid-19 pandemic, several countries that aimed to completely interrupt community transmission—China, Singapore, Australia, and New Zealand—achieved and maintained containment.23 Here, we analyse China’s containment strategy before vaccine roll-out and describe measures for initial and sustained containment and benefits for the country.

Containment of covid-19 means stopping transmission of SARS-CoV-2.4 Core measures of containment include actively finding infected people and treatment in isolation, close contact tracing and quarantine, and restrictions of travel from areas with community transmission. Additional measures that reduce transmission include stay at home orders, cancellation of mass gatherings, closing schools, and use of personal protection measures, such as hand hygiene, respiratory etiquette, keeping at least 1 m distance, and mask wearing.

Sustained containment is maintaining elimination of SARS-CoV-2 by completely stopping all externally introduced outbreaks—a policy of “zero tolerance for local transmission (zero covid).” Responses to an outbreak are based on an assessment of the epidemic risk and use the same measures as those used for the initial containment. These measures are augmented by strict border protection to minimise the number of imported outbreaks and by surveillance for infection using highly sensitive, routine polymerase chain reaction (PCR). Table 1 shows the initial and sustained containment measures based on assessment of the risk of epidemic.

},
keywords = {covid-19, NPIs},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mobility and phone call behavior explain patterns in poverty at high-resolution across multiple settings},
author = {Steele, Jessica E and Pezzulo, Carla and Albert, Maximilian and Brooks, Christopher J and zu Erbach-Schoenberg, Elisabeth and O’Connor, Siobh{'a}n B and Sunds{o}y, P{aa}l R and Eng{o}-Monsen, Kenth and Nilsen, Kristine and Graupe, Bonita and Nyachhyon, Rajesh Lal and Silpakar, Pradeep and and Tatem, Andrew J },
doi = {https://doi.org/10.1057/s41599-021-00953-0},
year  = {2021},
date = {2021-11-22},
journal = {Humanities and Social Sciences Communications},
volume = {8},
number = {1},
abstract = {Call detail records (CDRs) from mobile phone metadata are a promising data source for mapping poverty indicators in low- and middle-income countries. These data provide information on social networks, call behavior, and mobility patterns in a population, which are correlated with measures of socioeconomic status. CDRs are passively collected and provide information with high spatial and temporal resolution. Identifying features from these data that are generalizable and able to predict poverty and wealth beyond a single context could promote broader usage of mobile data, contribute to a reduction in the cost of socioeconomic data collection and processing, as well as complement existing census and survey-based methods of poverty estimation with improved temporal resolution. This is especially important within the context of the sustainable development goals (SDGs), where poverty and related health indicators are to be reduced significantly across subnational geographies by 2030. Here we utilize measures of cell phone user behavior derived from three CDR datasets within a Bayesian modeling framework to map poverty and wealth patterns across Namibia, Nepal, and Bangladesh. We demonstrate five metrics of user mobility and call behavior that are able to explain between 50% and 65% of the variance in socioeconomic status nationally for these three countries. These key metrics prove useful in very different contexts and can be readily provided as part of an existing CDR platform or software package. This paper provides a key contribution in this regard by identifying such metrics relevant to estimating poverty. We highlight the inclusion of ancillary data and local context as an important factor in understanding model outputs when targeting poverty alleviation strategies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Key opportunities and challenges for the use of big data in migration research and policy},
author = {Franklinos, Lydia H V and Parrish, Rebecca and Burns, Rachel J and Caflisch, Andrea and Mallick, Bishawjit and Rahman, Taifur and Routsis, Vasileios and López, , Ana Sebastián and Tatem, Andrew J and and Trigwell, Robert},
doi = {10.14324/111.444/ucloe.000027},
year  = {2021},
date = {2021-10-27},
journal = {UCL Open Environment},
volume = {3},
issue = {1},
abstract = {Migration is one of the defining issues of the 21st century. Better data is required to improve understanding about how and why people are moving, target interventions and support evidence-based migration policy. Big data, defined as large, complex data from diverse sources, is regularly proposed as a solution to help address current gaps in knowledge. The authors participated in a workshop held in London, UK, in July 2019, that brought together experts from the United Nations (UN), humanitarian non-governmental organisations (NGOs), policy and academia to develop a better understanding of how big data could be used for migration research and policy. We identified six key areas regarding the application of big data in migration research and policy: accessing and utilising data; integrating data sources and knowledge; understanding environmental drivers of migration; improving healthcare access for migrant populations; ethical and security concerns around the use of big data; and addressing political narratives. We advocate the need for careful consideration of the challenges faced by the use of big data, as well as increased cross-disciplinary collaborations to advance the use of big data in migration research whilst safeguarding vulnerable migrant communities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Infectious disease in an era of global change},
author = {Baker, Rachel E and Mahmud, Ayesha S and Miller, Ian F and Rajeev, Malavika and Rasambainarivo, Fidisoa and Rice, Benjamin L and Takahashi, Saki and Tatem, Andrew J and Wagner, Caroline E and Wang, Lin-Fa and Wesolowski, Amy and and Metcalf, C. Jessica E.},
doi = {https://doi.org/10.1038/s41579-021-00639-z},
year  = {2021},
date = {2021-10-13},
journal = {Nature Reviews Microbiology},
volume = {20},
pages = {193–205},
abstract = {The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013–2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Characterizing human mobility patterns in rural settings of sub-Saharan Africa},
author = {Meredith, Hannah R and Giles, John R and Perez-Saez, Javier and Mande, Théophile and Rinaldo, Andrea and Mutembo, Simon and Kabalo, Elliot N and Makungo, Kabondo and Buckee, Caroline O and Tatem, Andrew J and Metcalf, C Jessica E and and Wesolowski, Amy},
editor = {Flegg, Jennifer and Walczak, Aleksandra M and Flegg, Jennifer and and Rerolle, Francois},
doi = {https://doi.org/10.7554/eLife.68441},
issn = {2050-084X},
year  = {2021},
date = {2021-10-01},
journal = {eLife},
volume = {10},
abstract = {Human mobility is a core component of human behavior and its quantification is critical for understanding its impact on infectious disease transmission, traffic forecasting, access to resources and care, intervention strategies, and migratory flows. When mobility data are limited, spatial interaction models have been widely used to estimate human travel, but have not been extensively validated in low- and middle-income settings. Geographic, sociodemographic, and infrastructure differences may impact the ability for models to capture these patterns, particularly in rural settings. Here, we analyzed mobility patterns inferred from mobile phone data in four Sub-Saharan African countries to investigate the ability for variants on gravity and radiation models to estimate travel. Adjusting the gravity model such that parameters were fit to different trip types, including travel between more or less populated areas and/or different regions, improved model fit in all four countries. This suggests that alternative models may be more useful in these settings and better able to capture the range of mobility patterns observed.},
keywords = {Human mobility, spatial models, mobile phone data, gravity model, low and middle income countries},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Risk of SARS-CoV-2 Transmission among Air Passengers in China},
author = {Hu, Maogui and Wang, Jinfeng and Lin, Hui and Ruktanonchai, Corrine W and Xu, Chengdong and Meng, Bin and Zhang, Xin and Carioli, Alessandra and Feng, Yuqing and Yin, Qian and Floyd, Jessica R and Ruktanonchai, Nick W and Li, Zhongjie and Yang, Weizhong and Tatem, Andrew J and and Lai, Shengjie},
doi = {https://doi.org/10.1093/cid/ciab836},
issn = {1058-4838},
year  = {2021},
date = {2021-09-21},
journal = {Clinical Infectious Diseases},
abstract = {Background
Modern transportation plays a key role in the spread of SARS-CoV-2 and new variants. However, little is known about the exact transmission risk of the virus on airplanes.

Methods
Using the itinerary and epidemiological data of COVID-19 cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on January 23, 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travellers.

Results
175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95%CI 0.43%-0.84%) and 0.33% (18/5400, 95%CI 0.21%-0.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95%CI 5.7%-14.4%), with a relative risk 27.8 (95%CI 14.4-53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95%CI 0.4%-1.2%). The upper-bound AR increased from 0.7% (95%CI 0.5%-1.0%) to 1.2% (95%CI 0.4%-3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours.

Conclusions
The ARs among travellers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mapping out-of-school adolescents and youths in low- and middle-income countries},
author = {Alegana, VA and Pezzulo, Carla and Tatem, AJ and Omar, B and and Christensen, Andrew},
doi = {https://doi.org/10.1057/s41599-021-00892-w},
year  = {2021},
date = {2021-09-15},
journal = {Humanities and Social Sciences Communications},
volume = {8},
number = {213},
abstract = {Education is a human right and a driver of development, but, is still not accessible for a vast number of adolescents and school-age-youths. Out-of-school adolescents and youth rates (SDG 4.3.1) in lower and middle-income countries have been at a virtual halt for almost a decade. Thus, there is an increasing need to understand geographic variation on accessibility and school attendance to aid in reducing inequalities in education. Here, the aim was to estimate physical accessibility and secondary school non-attendance amongst adolescents and school-age youths in Tanzania, Cambodia, and the Dominican Republic. Community cluster survey data were triangulated with the spatial location of secondary schools, non-proprietary geospatial data and fine-scale population maps to estimate accessibility to all levels of secondary school education and the number of out-of-school. School attendance rates for the three countries were derived from nationally representative household survey data, and a Bayesian model-based geostatistical framework was used to estimate school attendance at high resolution. Results show a sub-national variation in accessibility and secondary school attendance rates for the three countries considered. Attendance was associated with distance to the nearest school (R2 > 70%). These findings suggest increasing the number of secondary schools could reduce the long-distance commuted to school in low-income and middle-income countries. Future work could extend these findings to fine-scale optimisation models for school location, intervention planning, and understanding barriers associated with secondary school non-attendance at the household level.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {A review of geospatial methods for population estimation and their use in constructing reproductive, maternal, newborn, child and adolescent health service indicators},
author = {Nilsen, Kristine and Tejedor-Garavito, Natalia and Leasure, Douglas R and Utazi, C Edson and Ruktanonchai, Corrine W and Wigley, Adelle S and Dooley, Claire A and Matthews, Zoe and and Tatem, Andrew J},
doi = {https://doi.org/10.1186/s12913-021-06370-y},
year  = {2021},
date = {2021-09-13},
urldate = {2021-09-13},
journal = {BMC Health Services Research},
volume = {21},
number = {1},
abstract = {Background
Household survey data are frequently used to measure reproductive, maternal, newborn, child and adolescent health (RMNCAH) service utilisation in low and middle income countries. However, these surveys are typically only undertaken every 5 years and tend to be representative of larger geographical administrative units. Investments in district health management information systems (DHMIS) have increased the capability of countries to collect continuous information on the provision of RMNCAH services at health facilities. However, reliable and recent data on population distributions and demographics at subnational levels necessary to construct RMNCAH coverage indicators are often missing. One solution is to use spatially disaggregated gridded datasets containing modelled estimates of population counts. Here, we provide an overview of various approaches to the production of gridded demographic datasets and outline their potential and their limitations. Further, we show how gridded population estimates can be used as alternative denominators to produce RMNCAH coverage metrics in combination with data from DHMIS, using childhood vaccination as examples.

Methods
We constructed indicators on the percentage of children one year old for diphtheria, pertussis and tetanus vaccine dose 3 (DTP3) and measles vaccine dose (MCV1) in Zambia and Nigeria at district levels. For the numerators, information on vaccines doses was obtained from each country’s respective DHMIS. For the denominators, the number of children was obtained from 3 different sources including national population projections and aggregated gridded estimates derived using top-down and bottom-up geospatial methods.

Results
In Zambia, vaccination estimates utilising the bottom-up approach to population estimation substantially reduced the number of districts with > 100% coverage of DTP3 and MCV1 compared to estimates using population projection and the top-down method. In Nigeria, results were mixed with bottom-up estimates having a higher number of districts > 100% and estimates using population projections performing better particularly in the South.

Conclusions
Gridded demographic data utilising traditional and novel data sources obtained from remote sensing offer new potential in the absence of up to date census information in the estimation of RMNCAH indicators. However, the usefulness of gridded demographic data is dependent on several factors including the availability and detail of input data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mapping access to basic hygiene services in low- and middle-income countries: A cross-sectional case study of geospatial disparities},
author = {Weiyu Yu and Robert E.S. Bain and Jie Yu and Victor Alegana and Winfred Dotse-Gborgbortsi and Yi Lin and and Jim A. Wright},
doi = {https://doi.org/10.1016/j.apgeog.2021.102549},
issn = {0143-6228},
year  = {2021},
date = {2021-09-07},
journal = {Applied Geography},
volume = {135},
abstract = {Handwashing with water and soap is among the most a cost-effective interventions to improve public health. Yet billions of people globally lacking handwashing facilities with water and soap on premises, with gaps particularly found in low- and middle-income countries. Targeted efforts to expand access to basic hygiene services require data at geospatially explicit scales. Drawing on country-specific cross-sectional Demographic and Health Surveys with georeferenced hygiene data, we developed an ensemble machine learning model to predict the prevalence of basic hygiene facilities in Malawi, Nepal, Nigeria, Pakistan and Uganda. The ensemble model was based on a multiple-level stacking structure, where four predictive modelling algorithms were used to produce sub-models, and a random forest model was used to generalise the final predictions. An inverse distance weighted interpolation was incorporated in the random forest model to account for spatial autocorrelation. Local coverage and a local dissimilarity index were calculated to examine the geographic disparities in access. Our methodology produced robust outputs, as evidenced by performance evaluations (all R2 were above 0.8). Among the five study countries, Pakistan had the highest overall coverage, whilst Malawi had the poorest coverage. Apparent disparities in basic hygiene services measured by local coverage were found across geographic locations and between urban and rural settings. Nigeria had the highest level of inequalities in basic hygiene services measured by a dissimilarity index, whilst Malawi showed the least segregation between populations with and without basic hygiene services. Both educational attainment and wealth were important predictors of the geospatial distribution of basic hygiene services. By producing geospatially explicit estimates of the prevalence of handwashing facilities with water and soap, this study provides a means of identifying geographical disparities in basic hygiene services. The method and outputs can be useful tools to identify areas of low coverage and to support efficient and precise targeting of efforts to scale up access to handwashing facilities and shift social and cultural norms on handwashing.}},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {The Role of Earth Observation in Achieving Sustainable Agricultural Production in Arid and Semi-Arid Regions of the World},
author = {Qader, Sarchil Hama and Dash, Jadu and Alegana, Victor A. and Khwarahm, Nabaz R. and Tatem, Andrew J. and and Atkinson, Peter M.},
doi = {https://doi.org/10.3390/rs13173382},
year  = {2021},
date = {2021-08-28},
urldate = {2021-08-28},
journal = {Remote Sensing},
volume = {13},
number = {17},
abstract = {Crop production is a major source of food and livelihood for many people in arid and semi-arid (ASA) regions across the world. However, due to irregular climatic events, ASA regions are affected commonly by frequent droughts that can impact food production. In addition, ASA regions in the Middle East and Africa are often characterised by political instability, which can increase population vulnerability to hunger and ill health. Remote sensing (RS) provides a platform to improve the spatial prediction of crop production and food availability, with the potential to positively impact populations. This paper, firstly, describes some of the important characteristics of agriculture in ASA regions that require monitoring to improve their management. Secondly, it demonstrates how freely available RS data can support decision-making through a cost-effective monitoring system that complements traditional approaches for collecting agricultural data. Thirdly, it illustrates the challenges of employing freely available RS data for mapping and monitoring crop area, crop status and forecasting crop yield in these regions. Finally, existing approaches used in these applications are evaluated, and the challenges associated with their use and possible future improvements are discussed. We demonstrate that agricultural activities can be monitored effectively and both crop area and crop yield can be predicted in advance using RS data. We also discuss the future challenges associated with maintaining food security in ASA regions and explore some recent advances in RS that can be used to monitor cropland and forecast crop production and yield.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {A data driven agent-based model that recommends non-pharmaceutical interventions to suppress Coronavirus disease 2019 resurgence in megacities},
author = {Yin, Ling and Zhang, Hao and Li, Yuan and Liu, Kang and Chen, Tianmu and Luo, Wei and Lai, Shengjie and Li, Ye and Tang, Xiujuan and Ning, Li and Feng, Shengzhong and Wei, Yanjie and Zhao, Zhiyuan and Wen, Ying and Mao, Liang and and Mei, Shujiang},
doi = {https://doi.org/10.1098/rsif.2021.0112},
year  = {2021},
date = {2021-08-25},
journal = {Journal of The Royal Society Interface},
volume = {18},
number = {181},
abstract = {Before herd immunity against Coronavirus disease 2019 (COVID-19) is achieved by mass vaccination, science-based guidelines for non-pharmaceutical interventions are urgently needed to reopen megacities. This study integrated massive mobile phone tracking records, census data and building characteristics into a spatially explicit agent-based model to simulate COVID-19 spread among 11.2 million individuals living in Shenzhen City, China. After validation by local epidemiological observations, the model was used to assess the probability of COVID-19 resurgence if sporadic cases occurred in a fully reopened city. Combined scenarios of three critical non-pharmaceutical interventions (contact tracing, mask wearing and prompt testing) were assessed at various levels of public compliance. Our results show a greater than 50% chance of disease resurgence if the city reopened without contact tracing. However, tracing household contacts, in combination with mandatory mask use and prompt testing, could suppress the probability of resurgence under 5% within four weeks. If household contact tracing could be expanded to work/class group members, the COVID resurgence could be avoided if 80% of the population wear facemasks and 40% comply with prompt testing. Our assessment, including modelling for different scenarios, helps public health practitioners tailor interventions within Shenzhen City and other world megacities under a variety of suppression timelines, risk tolerance, healthcare capacity and public compliance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Etiological and epidemiological features of acute respiratory infections in China},
author = {Li, Zhong-Jie and Zhang, Hai-Yang and Ren, Li-Li and Lu, Qing-Bin and Ren, Xiang and Zhang, Cui-Hong and Wang, Yi-Fei and Lin, Sheng-Hong and Zhang, Xiao-Ai and Li, Jun and Zhao, Shi-Wen and Yi, Zhi-Gang and Chen, Xiao and Yang, Zuo-Sen and Meng, Lei and Wang, Xin-Hua and Liu, Ying-Le and Wang, Xin and Cui, Ai-Li and Lai, Sheng-Jie and and others },
doi = {https://doi.org/10.1038/s41467-021-25120-6},
year  = {2021},
date = {2021-08-18},
urldate = {2021-08-18},
journal = {Nature Communications},
volume = {12},
number = {5026},
abstract = {Nationwide prospective surveillance of all-age patients with acute respiratory infections was conducted in China between 2009‒2019. Here we report the etiological and epidemiological features of the 231,107 eligible patients enrolled in this analysis. Children <5 years old and school-age children have the highest viral positivity rate (46.9%) and bacterial positivity rate (30.9%). Influenza virus, respiratory syncytial virus and human rhinovirus are the three leading viral pathogens with proportions of 28.5%, 16.8% and 16.7%, and Streptococcus pneumoniae, Mycoplasma pneumoniae and Klebsiella pneumoniae are the three leading bacterial pathogens (29.9%, 18.6% and 15.8%). Negative interactions between viruses and positive interactions between viral and bacterial pathogens are common. A Join-Point analysis reveals the age-specific positivity rate and how this varied for individual pathogens. These data indicate that differential priorities for diagnosis, prevention and control should be highlighted in terms of acute respiratory tract infection patients’ demography, geographic locations and season of illness in China.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Mobility in China, 2020: a tale of four phases},
author = {Tan, Suoyi and Lai, Shengjie and Fang, Fan and Cao, Ziqiang and Sai, Bin and Song, Bing and Dai, Bitao and Guo, Shuhui and Liu, Chuchu and Cai, Mengsi and Wang, Tong and Wang, Mengning and Li, Jiaxu and Chen, Saran and Qin, Shuo and Floyd, Jessica R and Cao, Zhidong and Tan, Jing and Sun, Xin and Zhou, Tao and Zhang, Wei and Tatem, Andrew J and Holme, Petter and Chen, Xiaohong and Lu, Xin},
doi = {https://doi.org/10.1093/nsr/nwab148},
year  = {2021},
date = {2021-08-16},
journal = {National Science Review},
volume = {8},
number = {11},
abstract = {2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Trip duration drives shift in travel network structure with implications for the predictability of spatial disease spread},
author = {Giles, John R and Cummings, Derek AT and Grenfell, Bryan T and Tatem, Andrew J and Erbach-Schoenberg, Elisabeth zu and Metcalf, C Jessica E and Wesolowski, Amy},
doi = {https://doi.org/10.1371/journal.pcbi.1009127},
year  = {2021},
date = {2021-08-10},
journal = {PLoS computational biology},
volume = {17},
number = {8},
abstract = {Human travel is one of the primary drivers of infectious disease spread. Models of travel are often used that assume the amount of travel to a specific destination decreases as cost of travel increases with higher travel volumes to more populated destinations. Trip duration, the length of time spent in a destination, can also impact travel patterns. We investigated the spatial patterns of travel conditioned on trip duration and find distinct differences between short and long duration trips. In short-trip duration travel networks, trips are skewed towards urban destinations, compared with long-trip duration networks where travel is more evenly spread among locations. Using gravity models to inform connectivity patterns in simulations of disease transmission, we show that pathogens with shorter generation times exhibit initial patterns of spatial propagation that are more predictable among urban locations. Further, pathogens with a longer generation time have more diffusive patterns of spatial spread reflecting more unpredictable disease dynamics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Implications for Tracking SDG Indicator Metrics with Gridded Population Data},
author = {Tuholske, Cascade and Gaughan, Andrea E. and Sorichetta, Alessandro and de Sherbinin, Alex and Bucherie, Agathe and Hultquist, Carolynne and Stevens, Forrest and Kruczkiewicz, Andrew and Huyck, Charles and Yetman, Greg},
doi = {https://doi.org/10.3390/su13137329},
issn = {2071-1050},
year  = {2021},
date = {2021-07-30},
journal = {Sustainability},
volume = {13},
number = {13},
abstract = {Achieving the seventeen United Nations Sustainable Development Goals (SDGs) requires accurate, consistent, and accessible population data. Yet many low- and middle-income countries lack reliable or recent census data at the sufficiently fine spatial scales needed to monitor SDG progress. While the increasing abundance of Earth observation-derived gridded population products provides analysis-ready population estimates, end users lack clear use criteria to track SDGs indicators. In fact, recent comparisons of gridded population products identify wide variation across gridded population products. Here we present three case studies to illuminate how gridded population datasets compare in measuring and monitoring SDGs to advance the “fitness for use” guidance. Our focus is on SDG 11.5, which aims to reduce the number of people impacted by disasters. We use five gridded population datasets to measure and map hazard exposure for three case studies: the 2015 earthquake in Nepal; Cyclone Idai in Mozambique, Malawi, and Zimbabwe (MMZ) in 2019; and flash flood susceptibility in Ecuador. First, we map and quantify geographic patterns of agreement/disagreement across gridded population products for Nepal, MMZ, and Ecuador, including delineating urban and rural populations estimates. Second, we quantify the populations exposed to each hazard. Across hazards and geographic contexts, there were marked differences in population estimates across the gridded population datasets. As such, it is key that researchers, practitioners, and end users utilize multiple gridded population datasets—an ensemble approach—to capture uncertainty and/or provide range estimates when using gridded population products to track SDG indicators. To this end, we made available code and globally comprehensive datasets that allows for the intercomparison of gridded population products.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Practical geospatial and sociodemographic predictors of human mobility},
author = {Ruktanonchai, Corrine W and Lai, Shengjie and Utazi, Chigozie E and Cunningham, Alex D and Koper, Patrycja and Rogers, Grant E and Ruktanonchai, Nick W and Sadilek, Adam and Woods, Dorothea and Tatem, Andrew J and Steele, Jessica E. and Sorichetta, Alessandro},
doi = {https://doi.org/10.1038/s41598-021-94683-7},
year  = {2021},
date = {2021-07-28},
journal = {Scientific Reports},
volume = {11},
number = {15389},
abstract = {Understanding seasonal human mobility at subnational scales has important implications across sciences, from urban planning efforts to disease modelling and control. Assessing how, when, and where populations move over the course of the year, however, requires spatially and temporally resolved datasets spanning large periods of time, which can be rare, contain sensitive information, or may be proprietary. Here, we aim to explore how a set of broadly available covariates can describe typical seasonal subnational mobility in Kenya pre-COVID-19, therefore enabling better modelling of seasonal mobility across low- and middle-income country (LMIC) settings in non-pandemic settings. To do this, we used the Google Aggregated Mobility Research Dataset, containing anonymized mobility flows aggregated over users who have turned on the Location History setting, which is off by default. We combined this with socioeconomic and geospatial covariates from 2018 to 2019 to quantify seasonal changes in domestic and international mobility patterns across years. We undertook a spatiotemporal analysis within a Bayesian framework to identify relevant geospatial and socioeconomic covariates explaining human movement patterns, while accounting for spatial and temporal autocorrelations. Typical pre-pandemic mobility patterns in Kenya mostly consisted of shorter, within-county trips, followed by longer domestic travel between counties and international travel, which is important in establishing how mobility patterns changed post-pandemic. Mobility peaked in August and December, closely corresponding to school holiday seasons, which was found to be an important predictor in our model. We further found that socioeconomic variables including urbanicity, poverty, and female education strongly explained mobility patterns, in addition to geospatial covariates such as accessibility to major population centres and temperature. These findings derived from novel data sources elucidate broad spatiotemporal patterns of how populations move within and beyond Kenya, and can be easily generalized to other LMIC settings before the COVID-19 pandemic. Understanding such pre-pandemic mobility patterns provides a crucial baseline to interpret both how these patterns have changed as a result of the pandemic, as well as whether human mobility patterns have been permanently altered once the pandemic subsides. Our findings outline key correlates of mobility using broadly available covariates, alleviating the data bottlenecks of highly sensitive and proprietary mobile phone datasets, which many researchers do not have access to. These results further provide novel insight on monitoring mobility proxies in the context of disease surveillance and control efforts through LMIC settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Untangling introductions and persistence in COVID-19 resurgence in Europe},
author = {Lemey, Philippe and Ruktanonchai, Nick and Hong, Samuel L and Colizza, Vittoria and Poletto, Chiara and Van den Broeck, Frederik and Gill, Mandev S and Ji, Xiang and Levasseur, Anthony and Oude Munnink, Bas B and Koopmans, Marion and Sadilek, Adam and Lai, Shengjie and Tatem, Andrew J. and Baele, Guy and Suchard, Marc A. and Dellicour, Simon},
doi = {https://doi.org/10.1038/s41586-021-03754-2},
year  = {2021},
date = {2021-06-30},
journal = {Nature},
volume = {595},
number = {713–717},
abstract = {After the first wave of SARS-CoV-2 infections in spring 2020, Europe experienced a resurgence of the virus starting in late summer 2020 that was deadlier and more difficult to contain1. Relaxed intervention measures and summer travel have been implicated as drivers of the second wave2. Here we build a phylogeographical model to evaluate how newly introduced lineages, as opposed to the rekindling of persistent lineages, contributed to the resurgence of COVID-19 in Europe. We inform this model using genomic, mobility and epidemiological data from 10 European countries and estimate that in many countries more than half of the lineages circulating in late summer resulted from new introductions since 15 June 2020. The success in onward transmission of newly introduced lineages was negatively associated with the local incidence of COVID-19 during this period. The pervasive spread of variants in summer 2020 highlights the threat of viral dissemination when restrictions are lifted, and this needs to be carefully considered in strategies to control the current spread of variants that are more transmissible and/or evade immunity. Our findings indicate that more effective and coordinated measures are required to contain the spread through cross-border travel even as vaccination is reducing disease burden.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys},
author = {Carla Pezzulo and Kristine Nilsen and Alessandra Carioli and Natalia Tejedor-Garavito and Sophie E Hanspal and Theodor Hilber and William H M James and Corrine W Ruktanonchai and Victor Alegana and Alessandro Sorichetta and Adelle S Wigley and Graeme M Hornby and Zoe Matthews and Andrew J Tatem},
doi = {https://doi.org/10.1016/S2214-109X(21)00082-6},
issn = {2214-109X},
year  = {2021},
date = {2021-06-01},
journal = {The Lancet Global Health},
volume = {9},
number = {6},
pages = {e802-e812},
abstract = {Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility. We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster. TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed. Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Impact of COVID-19 outbreaks and interventions on influenza in China and the United States},
author = {Feng, Luzhao and Zhang, Ting and Wang, Qing and Xie, Yiran and Peng, Zhibin and Zheng, Jiandong and Qin, Ying and Zhang, Muli and Lai, Shengjie and Wang, Dayan and Feng, Zijian and Li, Zhongjie and and Gao, George F.},
doi = {https://doi.org/10.1038/s41467-021-23440-1},
year  = {2021},
date = {2021-05-31},
journal = {Nature Communications},
volume = {12},
number = {3249},
issue = {1},
abstract = {Coronavirus disease 2019 (COVID-19) was detected in China during the 2019–2020 seasonal influenza epidemic. Non-pharmaceutical interventions (NPIs) and behavioral changes to mitigate COVID-19 could have affected transmission dynamics of influenza and other respiratory diseases. By comparing 2019–2020 seasonal influenza activity through March 29, 2020 with the 2011–2019 seasons, we found that COVID-19 outbreaks and related NPIs may have reduced influenza in Southern and Northern China and the United States by 79.2% (lower and upper bounds: 48.8%–87.2%), 79.4% (44.9%–87.4%) and 67.2% (11.5%–80.5%). Decreases in influenza virus infection were also associated with the timing of NPIs. Without COVID-19 NPIs, influenza activity in China and the United States would likely have remained high during the 2019–2020 season. Our findings provide evidence that NPIs can partially mitigate seasonal and, potentially, pandemic influenza.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Global COVID-19 lockdown highlights humans as both threats and custodians of the environment},
author = {Amanda E. Bates and Richard B. Primack and Brandy S. Biggar and Tomas J. Bird and Mary E. Clinton and Rylan J. Command and Cerren Richards and Marc Shellard and Nathan R. Geraldi and ... Shengjie Lai and and others.},
doi = {https://doi.org/10.1016/j.biocon.2021.109175},
issn = {0006-3207},
year  = {2021},
date = {2021-05-21},
urldate = {2021-05-21},
journal = {Biological Conservation},
volume = {263},
abstract = {The global lockdown to mitigate COVID-19 pandemic health risks has altered human interactions with nature. Here, we report immediate impacts of changes in human activities on wildlife and environmental threats during the early lockdown months of 2020, based on 877 qualitative reports and 332 quantitative assessments from 89 different studies. Hundreds of reports of unusual species observations from around the world suggest that animals quickly responded to the reductions in human presence. However, negative effects of lockdown on conservation also emerged, as confinement resulted in some park officials being unable to perform conservation, restoration and enforcement tasks, resulting in local increases in illegal activities such as hunting. Overall, there is a complex mixture of positive and negative effects of the pandemic lockdown on nature, all of which have the potential to lead to cascading responses which in turn impact wildlife and nature conservation. While the net effect of the lockdown will need to be assessed over years as data becomes available and persistent effects emerge, immediate responses were detected across the world. Thus, initial qualitative and quantitative data arising from this serendipitous global quasi-experimental perturbation highlights the dual role that humans play in threatening and protecting species and ecosystems. Pathways to favorably tilt this delicate balance include reducing impacts and increasing conservation effectiveness.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Summary and prospect of early warning models and systems for infectious disease outbreaks},
author = {Lai, SJ and Feng, LZ and Leng, ZW and Lyu, X and Li, RY and Yin, L and Luo, W and Li, ZJ and Lan, YJ and Yang, WZ},
doi = {10.3760/cma.j.cn112338-20210512-00391},
issn = {0254-6450},
year  = {2021},
date = {2021-05-12},
journal = {Chinese Journal of Epidemiology},
volume = {42},
number = {8},
pages = {1330—1335},
abstract = {This paper summarizes the basic principles and models of early warning for infectious disease outbreaks, introduces the early warning systems for infectious disease based on different data sources and their applications, and discusses the application potential of big data and their analysing techniques, which have been studied and used in the prevention and control of COVID-19 pandemic, including internet inquiry, social media, mobile positioning, in the early warning of infectious diseases in order to provide reference for the establishment of an intelligent early warning mechanism and platform for infectious diseases based on multi-source big data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{nokey,
title = {An index to map feasibility of social distancing within urban areas},
author = {Chamberlain, H.R. and Lazar, A.N. and and Tatem, A.J},
doi = {https://doi.org/10.5281/zenodo.4670091},
year  = {2021},
date = {2021-05-07},
booktitle = {29th Annual GIS Research UK Conference (GISRUK)},
abstract = {The COVID-19 pandemic has brought factors affecting disease transmission into the spotlight, and required widespread use of public health measures to reduce transmission and contain outbreaks. Urban areas inherently have large concentrations of people, providing high potential for large outbreaks and rapid disease spread, necessitating extensive use of measures to reduce transmission. Social distancing, also called physical distancing, is a public health measure intended to reduce infectious disease transmission, by maintaining physical distance between individuals or households. In the context of the COVID-19 pandemic, populations in many countries around the world have been advised to maintain social distance, with distances of 6ft or 2m commonly advised. The feasibility of social distancing is affected by the availability of space and the number of people, which varies geographically. In locations where social distancing is difficult, a focus on alternative measures to reduce disease transmission should be prioritised. To help identify and map such locations at high spatial resolution, this paper describes an index to quantify ease of social distancing, applied to urban areas across sub-Saharan Africa.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{nokey,
title = {Assessing the Effect of Global Travel and Contact Restrictions on Mitigating the COVID-19 Pandemic},
author = {Shengjie Lai and Nick W. Ruktanonchai and Alessandra Carioli and Corrine W. Ruktanonchai and Jessica R. Floyd and Olivia Prosper and Chi Zhang and Xiangjun Du and Weizhong Yang and Andrew J. Tatem},
doi = {https://doi.org/10.1016/j.eng.2021.03.017},
year  = {2021},
date = {2021-05-07},
journal = {Engineering},
volume = {7},
number = {7},
pages = {914-923},
abstract = {Travel restrictions and physical distancing have been implemented across the world to mitigate the coronavirus disease 2019 (COVID-19) pandemic, but studies are needed to understand their effectiveness across regions and time. Based on the population mobility metrics derived from mobile phone geolocation data across 135 countries or territories during the first wave of the pandemic in 2020, we built a metapopulation epidemiological model to measure the effect of travel and contact restrictions on containing COVID-19 outbreaks across regions. We found that if these interventions had not been deployed, the cumulative number of cases could have shown a 97-fold (interquartile range 79–116) increase, as of May 31, 2020. However, their effectiveness depended upon the timing, duration, and intensity of the interventions, with variations in case severity seen across populations, regions, and seasons. Additionally, before effective vaccines are widely available and herd immunity is achieved, our results emphasize that a certain degree of physical distancing at the relaxation of the intervention stage will likely be needed to avoid rapid resurgences and subsequent lockdowns.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Etiological, epidemiological, and clinical features of acute diarrhoea in China},
author = {Wang, Li-Ping and Zhou, Shi-Xia and Wang, Xin and Lu, Qing-Bin and Shi, Lu-Sha and Ren, Xiang and Zhang, Hai-Yang and Wang, Yi-Fei and Lin, Sheng-Hong and Zhang, Cui-Hong and Geng, Meng-Jie and Zhang, Xiao-Ai and Li, Jun and Zhao, Shi-Wen and Yi, Zhi-Gang and Chen, Xiao and Yang, Zuo-Sen and Meng, Lei and Wang, Xin-Hua and Liu, Ying-Le and Cui, Ai-Li and Lai, Sheng-Jie and and others},
doi = {https://doi.org/10.1038/s41467-021-22551-z},
year  = {2021},
date = {2021-04-29},
urldate = {2021-04-29},
journal = {Nature Communications},
volume = {12},
number = {2464},
issue = {1},
abstract = {National-based prospective surveillance of all-age patients with acute diarrhea was conducted in China between 2009‒2018. Here we report the etiological, epidemiological, and clinical features of the 152,792 eligible patients enrolled in this analysis. Rotavirus A and norovirus are the two leading viral pathogens detected in the patients, followed by adenovirus and astrovirus. Diarrheagenic Escherichia coli and nontyphoidal Salmonella are the two leading bacterial pathogens, followed by Shigella and Vibrio parahaemolyticus. Patients aged <5 years had higher overall positive rate of viral pathogens, while bacterial pathogens were more common in patients aged 18‒45 years. A joinpoint analysis revealed the age-specific positivity rate and how this varied for individual pathogens. Our findings fill crucial gaps of how the distributions of enteropathogens change across China in patients with diarrhea. This allows enhanced identification of the predominant diarrheal pathogen candidates for diagnosis in clinical practice and more targeted application of prevention and control measures.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@conference{nokey,
title = {Potential social-ecological development of coastal Bangladesh through the 21st century},
author = {Lazar, A. N. and Nicholls, R. J. and Hutton, C. W. and Payo, A. and Adams, H. and Haque, A. and Clarke, D. and Salehin, M. and Hunt, A. and Allan, A. and Adger, W. N. and Rahman, M. M.},
doi = {https://doi.org/10.5194/egusphere-egu21-1404},
year  = {2021},
date = {2021-04-28},
urldate = {2021-04-28},
booktitle = {EGU General Assembly 2021},
abstract = {Deltas occupy only 1% of global land surface area, but contain 7% of the global human population (ca. 500 million). The influence of changing and interacting climates, demography, economy, land use and coastal/catchment management on deltaic social-ecological systems is complex and little understood. We apply a new and innovative integrated assessment model: The Delta Dynamic Integrated Emulator Model (ΔDIEM) to coastal Bangladesh to explore a range of plausible future scenarios and quantify the sensitivities of selected environmental and socio-economic outcomes to key external and internal drivers. ΔDIEM is a tightly coupled integrated assessment platform considering climate and environmental change, demographic changes, economic changes, household decision making and governance, and designed to support the delta planning in Bangladesh. ΔDIEM allows the testing of a large number of water-based structural and policy interventions within a robust scenario framework, as well as quantify different development trajectories and their trade-offs. In this sensitivity analysis, we quantified the impact of (i) climate (precipitation, temperature and runoff), (ii) relative sea-level rise, (iii) cyclone frequency, (iv) embankment maintenance, (v) population size, (vi) economic changes at household level such as selling price of crops, cost of food, etc., (vii) land cover, and (viii) farming practices on trajectories of inundated area, soil salinity, rice productivity, poverty, income inequality and GDP/capita, assuming two contrasting scenarios in a more Positive and a more Negative World. Trajectories of these plausible futures showed a clear separation and the long-term trends are greatly influenced by the combinations of scenario assumptions. Our systemic results indicate a diverse potential set of futures for coastal Bangladesh, where good governance and adaptation could effectively mitigate the threat of sea-level rise-induced catastrophic inundation and other adverse impacts of the changing climate. However, societal inequality requires special attention otherwise climate-sensitive population groups may be left behind.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
@article{nokey,
title = {Seasonal association between viral causes of hospitalised acute lower respiratory infections and meteorological factors in China: a retrospective study},
author = {Xu, Bing and Wang, Jinfeng and Li, Zhongjie and Xu, Chengdong and Liao, Yilan and Hu, Maogui and Yang, Jing and Lai, Shengjie and Wang, Liping and Yang, Weizhong },
doi = {https://doi.org/10.1016/S2542-5196(20)30297-7},
year  = {2021},
date = {2021-03-11},
urldate = {2021-03-11},
journal = {The Lancet Planetary Health},
volume = {5},
issue = {3},
pages = {e154-e163},
abstract = {Acute lower respiratory infections (ALRIs) caused by respiratory viruses are common and persistent infectious diseases worldwide and in China, which have pronounced seasonal patterns. Meteorological factors have important roles in the seasonality of some major viruses, especially respiratory syncytial virus (RSV) and influenza virus. Our aim was to identify the dominant meteorological factors and to model their effects on common respiratory viruses in different regions of China. We analysed monthly virus data on patients hospitalised with ALRI from 81 sentinel hospitals in 22 provinces in mainland China from Jan 1, 2009, to Sept 30, 2013. We considered seven common respiratory viruses: RSV, influenza virus, human parainfluenza virus, adenovirus, human metapneumovirus, human bocavirus, and human coronavirus. Meteorological data of the same period were used to analyse relationships between virus seasonality and seven meteorological factors according to region (southern vs northern China). The geographical detector method was used to quantify the explanatory power of each meteorological factor, individually and interacting in pairs, on the respiratory viruses. 28,369 hospitalised patients with ALRI were tested, 10,387 (36·6%) of whom were positive for at least one virus, including RSV (4091 [32·0%] patients), influenza virus (2665 [20·8%]), human parainfluenza virus (2185 [17·1%]), adenovirus (1478 [11·6%]), human bocavirus (1120 [8·8%]), human coronavirus (637 [5·0%]), and human metapneumovirus (615 [4·8%]). RSV and influenza virus had annual peaks in the north and biannual peaks in the south. Human parainfluenza virus and human bocavirus had higher positive rates in the spring–summer months. Human metapneumovirus had an annual peak in winter–spring, especially in the north. Adenovirus and human coronavirus exhibited no clear annual seasonality. Temperature, atmospheric pressure, vapour pressure, and rainfall had most explanatory power on most respiratory viruses in each region. Relative humidity was only dominant in the north, but had no significant explanatory power for most viruses in the south. Hours of sunlight had significant explanatory power for RSV and influenza virus in the north, and for most viruses in the south. Wind speed was the only factor with significant explanatory power for human coronavirus in the south. For all viruses, interactions between any two of the paired factors resulted in enhanced explanatory power, either bivariately or non-linearly. Spatiotemporal heterogeneity was detected for most viruses in this study, and interactions between pairs of meteorological factors were found to enhance their influence on virus variation. These findings might be helpful to guide government planning, such as public health interventions, infection control practice, and timing of passive immunoprophylaxis, and might facilitate the development of future vaccine strategies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Prevalence and factors associated with overweight and obesity in selected health areas in a rural health district in Cameroon: a cross-sectional analysis},
author = {Simo, Larissa Pone and Agbor, Valirie Ndip and Temgoua, Francine Zeuga and Fozeu, Leo Cedric Fosso and Bonghaseh, Divine Tim and Mbonda, Aimé Gilbert Noula and Yurika, Raymond and Dotse-Gborgbortsi, Winfred and Mbanya, Dora},
doi = {https://doi.org/10.1186/s12889-021-10403-w},
year  = {2021},
date = {2021-03-10},
urldate = {2023-03-10},
journal = {BMC Public Health},
volume = {21},
issue = {1},
abstract = {Overweight and obesity are major public health problems worldwide, with projections suggesting a proportional increase in the number of affected individuals in developing countries by the year 2030. Evidence-based preventive strategies are needed to reduce the burden of overweight and obesity in developing countries. We assessed the prevalence of, and factors associated with overweight and obesity in selected health areas in West Cameroon.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Rice2021,
title = {Variation in SARS-CoV-2 outbreaks across sub-Saharan Africa},
author = {Benjamin L. Rice and Akshaya Annapragada and Rachel E. Baker and Marjolein Bruijning and Winfred Dotse-Gborgbortsi and Keitly Mensah and Ian F. Miller and Nkengafac Villyen Motaze and Antso Raherinandrasana and Malavika Rajeev and Julio Rakotonirina and Tanjona Ramiadantsoa and Fidisoa Rasambainarivo and Weiyu Yu and Bryan T. Grenfell and Andrew J. Tatem and C. Jessica E. Metcalf},
url = {https://doi.org/10.1038/s41591-021-01234-8},
doi = {10.1038/s41591-021-01234-8},
issn = {1546-170X},
year  = {2021},
date = {2021-03-01},
journal = {Nature Medicine},
volume = {27},
number = {3},
pages = {447-453},
abstract = {A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (for example, warmer environments1, younger populations2--4) have yet to be framed within a comprehensive analysis. We synthesized factors hypothesized to drive the pace and burden of this pandemic in SSA during the period from 25 February to 20 December 2020, encompassing demographic, comorbidity, climatic, healthcare capacity, intervention efforts and human mobility dimensions. Large diversity in the probable drivers indicates a need for caution in interpreting analyses that aggregate data across low- and middle-income settings. Our simulation shows that climatic variation between SSA population centers has little effect on early outbreak trajectories; however, heterogeneity in connectivity, although rarely considered, is likely an important contributor to variance in the pace of viral spread across SSA. Our synthesis points to the potential benefits of context-specific adaptation of surveillance systems during the ongoing pandemic. In particular, characterizing patterns of severity over age will be a priority in settings with high comorbidity burdens and poor access to care. Understanding the spatial extent of outbreaks warrants emphasis in settings where low connectivity could drive prolonged, asynchronous outbreaks resulting in extended stress to health systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Tools for mapping multi-scale settlement patterns of building footprints: An introduction to the R package foot},
author = {Jochem, W. C. and Tatem, A. J.},
doi = {https://doi.org/10.1371/journal.pone.0247535},
year  = {2021},
date = {2021-02-25},
urldate = {2021-02-25},
journal = {PLoS ONE},
volume = {16},
issue = {2},
abstract = {Spatial datasets of building footprint polygons are becoming more widely available and accessible for many areas in the world. These datasets are important inputs for a range of different analyses, such as understanding the development of cities, identifying areas at risk of disasters, and mapping the distribution of populations. The growth of high spatial resolution imagery and computing power is enabling automated procedures to extract and map building footprints for whole countries. These advances are enabling coverage of building footprint datasets for low and middle income countries which might lack other data on urban land uses. While spatially detailed, many building footprints lack information on structure type, local zoning, or land use, limiting their application. However, morphology metrics can be used to describe characteristics of size, shape, spacing, orientation and patterns of the structures and extract additional information which can be correlated with different structure and settlement types or neighbourhoods. We introduce the foot package, a new set of open-source tools in a flexible R package for calculating morphology metrics for building footprints and summarising them in different spatial scales and spatial representations. In particular our tools can create gridded (or raster) representations of morphology summary metrics which have not been widely supported previously. We demonstrate the tools by creating gridded morphology metrics from all building footprints in England, Scotland and Wales, and then use those layers in an unsupervised cluster analysis to derive a pattern-based settlement typology. We compare our mapped settlement types with two existing settlement classifications. The results suggest that building patterns can help distinguish different urban and rural types. However, intra-urban differences were not well-predicted by building morphology alone. More broadly, though, this case study demonstrates the potential of mapping settlement patterns in the absence of a housing census or other urban planning data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Integrated vaccination and physical distancing interventions to prevent future COVID-19 waves in Chinese cities},
author = {Huang, Bo and Wang, Jionghua and Cai, Jixuan and Yao, Shiqi and Chan, Paul Kay Sheung and Tam, Tony Hong-wing and Hong, Ying-Yi and Ruktanonchai, Corrine W. and Carioli, Alessandra and Floyd, Jessica R. and Ruktanonchai, Nick W. and Yang, Weizhong and Li, Zhongjie and Tatem, Andrew J. and Lai, Shengjie},
doi = {https://doi.org/10.1038/s41562-021-01063-2},
year  = {2021},
date = {2021-02-18},
urldate = {2021-02-18},
journal = {Nature Human Behaviour},
volume = {5},
pages = {695–705},
abstract = {The coronavirus disease 2019 (COVID-19) pandemic has posed substantial challenges to the formulation of preventive interventions, particularly since the effects of physical distancing measures and upcoming vaccines on reducing susceptible social contacts and eventually halting transmission remain unclear. Here, using anonymized mobile geolocation data in China, we devise a mobility-associated social contact index to quantify the impact of both physical distancing and vaccination measures in a unified way. Building on this index, our epidemiological model reveals that vaccination combined with physical distancing can contain resurgences without relying on stay-at-home restrictions, whereas a gradual vaccination process alone cannot achieve this. Further, for cities with medium population density, vaccination can reduce the duration of physical distancing by 36% to 78%, whereas for cities with high population density, infection numbers can be well-controlled through moderate physical distancing. These findings improve our understanding of the joint effects of vaccination and physical distancing with respect to a city’s population density and social contact patterns.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {District-level estimation of vaccination coverage: Discrete vs continuous spatial models},
author = {Utazi, C. Edson and Nilsen, Kristine and Pannell, Oliver and Dotse-Gborgbortsi, Winfred and Tatem, Andrew J.},
doi = {https://doi.org/10.1002/sim.8897},
year  = {2021},
date = {2021-02-04},
journal = {Statistics in Medicine},
volume = {40},
number = {9},
pages = {2197-2211},
abstract = {Health and development indicators (HDIs) such as vaccination coverage are regularly measured in many low- and middle-income countries using household surveys, often due to the unreliability or incompleteness of routine data collection systems. Recently, the development of model-based approaches for producing subnational estimates of HDIs using survey data, particularly cluster-level data, has been an active area of research. This is mostly driven by the increasing demand for estimates at certain administrative levels, for example, districts, at which many development goals are set and evaluated. In this study, we explore spatial modeling approaches for producing district-level estimates of vaccination coverage. Specifically, we compare discrete spatial smoothing models which directly model district-level data with continuous Gaussian process (GP) models that utilize geolocated cluster-level data. We adopt a fully Bayesian framework, implemented using the INLA and SPDE approaches. We compare the predictive performance of the models by analyzing vaccination coverage using data from two Demographic and Health Surveys (DHS), namely the 2014 Kenya DHS and the 2015-16 Malawi DHS. We find that the continuous GP models performed well, offering a credible alternative to traditional discrete spatial smoothing models. Our analysis also revealed that accounting for between-cluster variation in the continuous GP models did not have any real effect on the district-level estimates. Our results provide guidance to practitioners on the reliability of these model-based approaches for producing estimates of vaccination coverage and other HDIs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Vaccination strategies for measles control and elimination: time to strengthen local initiatives},
author = {F. T. Cutts and M. J. Ferrari and L. K. Krause and A. J. Tatem and J. F. Mosser},
url = {https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-020-01843-z},
doi = {10.1186/s12916-020-01843-z},
year  = {2021},
date = {2021-01-05},
journal = {BMC Med},
volume = {19},
issue = {2},
abstract = {Background
Through a combination of strong routine immunization (RI), strategic supplemental immunization activities (SIA) and robust surveillance, numerous countries have been able to approach or achieve measles elimination. The fragility of these achievements has been shown, however, by the resurgence of measles since 2016. We describe trends in routine measles vaccine coverage at national and district level, SIA performance and demographic changes in the three regions with the highest measles burden.

Findings
WHO-UNICEF estimates of immunization coverage show that global coverage of the first dose of measles vaccine has stabilized at 85% from 2015 to 19. In 2000, 17 countries in the WHO African and Eastern Mediterranean regions had measles vaccine coverage below 50%, and although all increased coverage by 2019, at a median of 60%, it remained far below levels needed for elimination. Geospatial estimates show many low coverage districts across Africa and much of the Eastern Mediterranean and southeast Asian regions. A large proportion of children unvaccinated for MCV live in conflict-affected areas with remote rural areas and some urban areas also at risk. Countries with low RI coverage use SIAs frequently, yet the ideal timing and target age range for SIAs vary within countries, and the impact of SIAs has often been mitigated by delays or disruptions. SIAs have not been sufficient to achieve or sustain measles elimination in the countries with weakest routine systems. Demographic changes also affect measles transmission, and their variation between and within countries should be incorporated into strategic planning.

Conclusions
Rebuilding services after the COVID-19 pandemic provides a need and an opportunity to increase community engagement in planning and monitoring services. A broader suite of interventions is needed beyond SIAs. Improved methods for tracking coverage at the individual and community level are needed together with enhanced surveillance. Decision-making needs to be decentralized to develop locally-driven, sustainable strategies for measles control and elimination.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = { Semi-automatic mapping of pre-census enumeration areas and population sampling frames},
author = {Sarchil Qader and Veronique Lefebvre and Andrew Tatem and Utz Pape and Kristen Himelein and Amy Ninneman and Linus Bengtsson and Tomas Bird },
url = {https://www.nature.com/articles/s41599-020-00670-0},
doi = {10.1057/s41599-020-00670-0},
year  = {2021},
date = {2021-01-04},
urldate = {2021-01-04},
journal = {Humanit Soc Sci Commun},
volume = {8},
issue = {3},
abstract = {Enumeration Areas (EAs) are the operational geographic units for the collection and dissemination of census data and are often used as a national sampling frame for various types of surveys. In many poor or conflict-affected countries, EA demarcations are incomplete, outdated, or missing. Even for countries that are stable and prosperous, creating and updating EAs is one of the most challenging yet essential tasks in the preparation for a national census. Commonly, EAs are created by manually digitising small geographic units on high-resolution satellite imagery or physically walking the boundaries of units, both of which are highly time, cost, and labour intensive. In addition, creating EAs requires considering population and area size within each unit. This is an optimisation problem that can best be solved by a computer. Here, for the first time, we produce a semi-automatic mapping of pre-defined census EAs based on high-resolution gridded population and settlement datasets and using publicly available natural and administrative boundaries. We demonstrate the approach in generating rural EAs for Somalia where such mapping is not existent. In addition, we compare our automated approach against manually digitised EAs created in urban areas of Mogadishu and Hargeysa. Our semi-automatically generated EAs are consistent with standard EAs, including having identifiable boundaries for field teams to follow on the ground, and appropriate sizing and population for coverage by an enumerator. Furthermore, our semi-automated urban EAs have no gaps, in contrast, to manually drawn urban EAs. Our work shows the time, labour and cost-saving value of automated EA delineation and points to the potential for broadly available tools suitable for low-income and data-poor settings but applicable to potentially wider contexts.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0245297,
title = {The influence of travel time to health facilities on stillbirths: A geospatial case-control analysis of facility-based data in Gombe, Nigeria},
author = {Oghenebrume Wariri and Egwu Onuwabuchi and Jacob Albin Korem Alhassan and Eseoghene Dase and Iliya Jalo and Christopher Hassan Laima and Halima Usman Farouk and Aliyu U. El-Nafaty and Uduak Okomo and Winfred Dotse-Gborgbortsi},
url = {https://doi.org/10.1371/journal.pone.0245297},
doi = {10.1371/journal.pone.0245297},
year  = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {PLOS ONE},
volume = {16},
number = {1},
pages = {1-18},
publisher = {Public Library of Science},
abstract = {Access to quality emergency obstetric and newborn care (EmONC); having a skilled attendant at birth (SBA); adequate antenatal care; and efficient referral systems are considered the most effective interventions in preventing stillbirths. We determined the influence of travel time from mother’s area of residence to a tertiary health facility where women sought care on the likelihood of delivering a stillbirth. We carried out a prospective matched case-control study between 1st January 2019 and 31st December 2019 at the Federal Teaching Hospital Gombe (FTHG), Nigeria. All women who experienced a stillbirth after hospital admission during the study period were included as cases while controls were consecutive age-matched (ratio 1:1) women who experienced a live birth. We modelled travel time to health facilities. To determine how travel time to the nearest health facility and the FTHG were predictive of the likelihood of stillbirths, we fitted a conditional logistic regression model. A total of 318 women, including 159 who had stillborn babies (cases) and 159 age-matched women who had live births (controls) were included. We did not observe any significant difference in the mean travel time to the nearest government health facility for women who had experienced a stillbirth compared to those who had a live birth [9.3 mins (SD 7.3, 11.2) vs 6.9 mins (SD 5.1, 8.7) respectively},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PEZZULO2021e802,
title = {Geographical distribution of fertility rates in 70 low-income, lower-middle-income, and upper-middle-income countries, 2010–16: a subnational analysis of cross-sectional surveys},
author = {Carla Pezzulo and Kristine Nilsen and Alessandra Carioli and Natalia Tejedor-Garavito and Sophie E Hanspal and Theodor Hilber and William H M James and Corrine W Ruktanonchai and Victor Alegana and Alessandro Sorichetta and Adelle S Wigley and Graeme M Hornby and Zoe Matthews and Andrew J Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S2214109X21000826},
doi = {https://doi.org/10.1016/S2214-109X(21)00082-6},
issn = {2214-109X},
year  = {2021},
date = {2021-01-01},
journal = {The Lancet Global Health},
volume = {9},
number = {6},
pages = {e802-e812},
abstract = {Summary 
Background 
Understanding subnational variation in age-specific fertility rates (ASFRs) and total fertility rates (TFRs), and geographical clustering of high fertility and its determinants in low-income and middle-income countries, is increasingly needed for geographical targeting and prioritising of policy. We aimed to identify variation in fertility rates, to describe patterns of key selected fertility determinants in areas of high fertility. 
Methods 
We did a subnational analysis of ASFRs and TFRs from the most recent publicly available and nationally representative cross-sectional Demographic and Health Surveys and Multiple Indicator Cluster Surveys collected between 2010 and 2016 for 70 low-income, lower-middle-income, and upper-middle-income countries, across 932 administrative units. We assessed the degree of global spatial autocorrelation by using Moran's I statistic and did a spatial cluster analysis using the Getis-Ord Gi* local statistic to examine the geographical clustering of fertility and key selected fertility determinants. Descriptive analysis was used to investigate the distribution of ASFRs and of selected determinants in each cluster. 
Findings 
TFR varied from below replacement (2·1 children per women) in 36 of the 932 subnational regions (mainly located in India, Myanmar, Colombia, and Armenia), to rates of 8 and higher in 14 subnational regions, located in sub-Saharan Africa and Afghanistan. Areas with high-fertility clusters were mostly associated with areas of low prevalence of women with secondary or higher education, low use of contraception, and high unmet needs for family planning, although exceptions existed. 
Interpretation 
Substantial within-country variation in the distribution of fertility rates highlights the need for tailored programmes and strategies in high-fertility cluster areas to increase the use of contraception and access to secondary education, and to reduce unmet need for family planning. 
Funding 
Wellcome Trust, the UK Foreign, Commonwealth and Development Office, and the Bill & Melinda Gates Foundation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1177/2399808320921208,
title = {Classifying settlement types from multi-scale spatial patterns of building footprints},
author = {Warren C Jochem and Douglas R Leasure and Oliver Pannell and Heather R Chamberlain and Patricia Jones and Andrew J Tatem},
url = {https://doi.org/10.1177/2399808320921208},
doi = {10.1177/2399808320921208},
year  = {2021},
date = {2021-01-01},
journal = {Environment and Planning B: Urban Analytics and City Science},
volume = {48},
number = {5},
pages = {1161-1179},
abstract = {Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery (<1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery. These settlement data provide information on the changes occurring in cities, particularly in countries which may lack other data on urbanisation. While spatially detailed, extracted building footprints typically lack other information that identify building types or can be used to differentiate intra-urban land uses or neighbourhood types. This work demonstrates an approach to classifying settlement types through multi-scale spatial patterns of urban morphology visible in building footprint data extracted from very high resolution imagery. The work uses a Gaussian mixture modelling approach to select and hierarchically merge components into clusters. The results are maps classifying settlement types on a high spatial resolution (100 m) grid. The approach is applied in Kaduna, Nigeria; Kinshasa, Democratic Republic of the Congo; and Maputo, Mozambique and demonstrates the potential of computational methods to take advantage of large spatial datasets and extract meaningful information to support monitoring of urban areas. The model-based approach produces a hierarchy of potential clustering solutions, and we suggest that this can be used in partnership with local knowledge of the context when creating settlement typologies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Thomson2020,
title = {Gridded population survey sampling: a systematic scoping review of the field and strategic research agenda},
author = {Dana R. Thomson and Dale A. Rhoda and Andrew J. Tatem and Marcia C. Castro},
url = {https://doi.org/10.1186/s12942-020-00230-4},
doi = {10.1186/s12942-020-00230-4},
issn = {1476-072X},
year  = {2020},
date = {2020-09-09},
journal = {International Journal of Health Geographics},
volume = {19},
number = {1},
pages = {34},
abstract = {In low- and middle-income countries (LMICs), household survey data are a main source of information for planning, evaluation, and decision-making. Standard surveys are based on censuses, however, for many LMICs it has been more than 10 years since their last census and they face high urban growth rates. Over the last decade, survey designers have begun to use modelled gridded population estimates as sample frames. We summarize the state of the emerging field of gridded population survey sampling, focussing on LMICs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Wigley2020,
title = {Measuring the availability and geographical accessibility of maternal health services across sub-Saharan Africa},
author = {A. S. Wigley and N. Tejedor-Garavito and V. Alegana and A. Carioli and C. W. Ruktanonchai and C. Pezzulo and Z. Matthews and A. J. Tatem and K. Nilsen},
url = {https://doi.org/10.1186/s12916-020-01707-6},
doi = {10.1186/s12916-020-01707-6},
issn = {1741-7015},
year  = {2020},
date = {2020-09-08},
journal = {BMC Medicine},
volume = {18},
number = {1},
pages = {237},
abstract = {With universal health coverage a key component of the 2030 Sustainable Development Goals, targeted monitoring is crucial for reducing inequalities in the provision of services. However, monitoring largely occurs at the national level, masking sub-national variation. Here, we estimate indicators for measuring the availability and geographical accessibility of services, at national and sub-national levels across sub-Saharan Africa, to show how data at varying spatial scales and input data can considerably impact monitoring outcomes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lai2020,
title = {Effect of non-pharmaceutical interventions to contain COVID-19 in China},
author = {Shengjie Lai and Nick W. Ruktanonchai and Liangcai Zhou and Olivia Prosper and Wei Luo and Jessica R. Floyd and Amy Wesolowski and Mauricio Santillana and Chi Zhang and Xiangjun Du and Hongjie Yu and Andrew J. Tatem},
url = {https://doi.org/10.1038/s41586-020-2293-x},
doi = {10.1038/s41586-020-2293-x},
issn = {1476-4687},
year  = {2020},
date = {2020-09-01},
journal = {Nature},
volume = {585},
number = {7825},
pages = {410-413},
abstract = {On 11 March 2020, the World Health Organization (WHO) declared coronavirus disease 2019 (COVID-19) a pandemic1. The strategies based on non-pharmaceutical interventions that were used to contain the outbreak in China appear to be effective2, but quantitative research is still needed to assess the efficacy of non-pharmaceutical interventions and their timings3. Here, using epidemiological data on COVID-19 and anonymized data on human movement4,5, we develop a modelling framework that uses daily travel networks to simulate different outbreak and intervention scenarios across China. We estimate that there were a total of 114,325 cases of COVID-19 (interquartile range 76,776--164,576) in mainland China as of 29 February 2020. Without non-pharmaceutical interventions, we predict that the number of cases would have been 67-fold higher (interquartile range 44--94-fold) by 29 February 2020, and we find that the effectiveness of different interventions varied. We estimate that early detection and isolation of cases prevented more infections than did travel restrictions and contact reductions, but that a combination of non-pharmaceutical interventions achieved the strongest and most rapid effect. According to our model, the lifting of travel restrictions from 17 February 2020 does not lead to an increase in cases across China if social distancing interventions can be maintained, even at a limited level of an on average 25% reduction in contact between individuals that continues until late April. These findings improve our understanding of the effects of non-pharmaceutical interventions on COVID-19, and will inform response efforts across the world.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{nokey,
title = {Measuring the Contribution of Built-Settlement Data to Global Population Mapping},
author = {Nieves, Jeremiah J. and Bondarenko, Maksym and Kerr, David and Ves, Nikolas and Yetman, Greg and Sinha, Parmanand and Clarke, Donna J. and Sorichetta, Alessandro and Stevens, Forrest and Gaughan, Andrea E. and Tatem, Andrew J.},
url = {https://ssrn.com/abstract=3599775},
doi = {10.2139/ssrn.3599775},
year  = {2020},
date = {2020-05-20},
journal = {Social Sciences & Humanities Open},
abstract = {Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. Here we have modelled built-settlement extents between 2000 and 2012 and demonstrate the applied utility and information provided by these annually modelled data for the application of annually modelling population across 172 countries. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Dunnett2020,
title = {Harmonised global datasets of wind and solar farm locations and power},
author = {Sebastian Dunnett and Alessandro Sorichetta and Gail Taylor and Felix Eigenbrod},
url = {https://doi.org/10.1038/s41597-020-0469-8},
doi = {10.1038/s41597-020-0469-8},
issn = {2052-4463},
year  = {2020},
date = {2020-04-29},
journal = {Scientific Data},
volume = {7},
number = {1},
pages = {130},
abstract = {Energy systems need decarbonisation in order to limit global warming to within safe limits. While global land planners are promising more of the planet's limited space to wind and solar photovoltaic, there is little information on where current infrastructure is located. The majority of recent studies use land suitability for wind and solar, coupled with technical and socioeconomic constraints, as a proxy for actual location data. Here, we address this shortcoming. Using readily accessible OpenStreetMap data we present, to our knowledge, the first global, open-access, harmonised spatial datasets of wind and solar installations. We also include user friendly code to enable users to easily create newer versions of the dataset. Finally, we include first order estimates of power capacities of installations. We anticipate these data will be of widespread interest within global studies of the future potential and trade-offs associated with the global decarbonisation of energy systems.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.7717/peerj.8798,
title = {Activity-specific mobility of adults in a rural region of western Kenya},
author = {Jessica R. Floyd and Joseph Ogola and Eric M. Fèvre and Nicola Wardrop and Andrew J. Tatem and Nick W. Ruktanonchai},
url = {https://doi.org/10.7717/peerj.8798},
doi = {10.7717/peerj.8798},
issn = {2167-8359},
year  = {2020},
date = {2020-04-01},
journal = {PeerJ},
volume = {8},
pages = {e8798},
abstract = {Improving rural household access to resources such as markets, schools and healthcare can help alleviate poverty in low-income settings. Current models of geographic accessibility to various resources rarely take individual variation into account due to a lack of appropriate data, yet understanding mobility at an individual level is key to knowing how people access their local resources. Our study used both an activity-specific survey and GPS trackers to evaluate how adults in a rural area of western Kenya accessed local resources. We calculated the travel time and time spent at six different types of resource and compared the GPS and survey data to see how well they matched. We found links between several demographic characteristics and the time spent at different resources, and that the GPS data reflected the survey data well for time spent at some types of resource, but poorly for others. We conclude that demography and activity are important drivers of mobility, and a better understanding of individual variation in mobility could be obtained through the use of GPS trackers on a wider scale.},
keywords = {Activity, GPS, Mobility, Resource access},
pubstate = {published},
tppubtype = {article}
}
@article{Qader2020,
title = {Using gridded population and quadtree sampling units to support survey sample design in low-income settings},
author = {Sarchil Hama Qader and Veronique Lefebvre and Andrew J. Tatem and Utz Pape and Warren Jochem and Kristen Himelein and Amy Ninneman and Philip Wolburg and Gonzalo Nunez-Chaim and Linus Bengtsson and Tomas Bird},
url = {https://doi.org/10.1186/s12942-020-00205-5},
doi = {10.1186/s12942-020-00205-5},
issn = {1476-072X},
year  = {2020},
date = {2020-03-26},
journal = {International Journal of Health Geographics},
volume = {19},
number = {1},
pages = {10},
abstract = {Household surveys are the main source of demographic, health and socio-economic data in low- and middle-income countries (LMICs). To conduct such a survey, census population information mapped into enumeration areas (EAs) typically serves a sampling frame from which to generate a random sample. However, the use of census information to generate this sample frame can be problematic as in many LMIC contexts, such data are often outdated or incomplete, potentially introducing coverage issues into the sample frame. Increasingly, where census data are outdated or unavailable, modelled population datasets in the gridded form are being used to create household survey sampling frames.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{rs12040652,
title = {Transformative Urban Changes of Beijing in the Decade of the 2000s},
author = {Alessandro Sorichetta and Son V. Nghiem and Marco Masetti and Catherine Linard and Andreas Richter},
url = {https://www.mdpi.com/2072-4292/12/4/652},
doi = {10.3390/rs12040652},
issn = {2072-4292},
year  = {2020},
date = {2020-01-01},
journal = {Remote Sensing},
volume = {12},
number = {4},
abstract = {The rapid economic growth, the exodus from rural to urban areas, and the associated extreme urban development that occurred in China in the decade of the 2000s have severely impacted the environment in Beijing, its vicinity, and beyond. This article presents an innovative approach for assessing mega-urban changes and their impact on the environment based on the use of decadal QuikSCAT (QSCAT) satellite data, acquired globally by the SeaWinds scatterometer over that period. The Dense Sampling Method (DSM) is applied to QSCAT data to obtain reliable annual infrastructure-based urban observations at a posting of ~1 km. The DSM-QSCAT data, along with different DSM-based change indices, were used to delineate the extent of the Beijing infrastructure-based urban area in each year between 2000 and 2009, and assess its development over time, enabling a physical quantification of its urbanization which reflects the implementation of various development policies during the same time period. Eventually, as a proxy for the impact of Beijing urbanization on the environment, the decadal trend of its infrastructure-based urbanization is compared with that of the corresponding tropospheric nitrogen dioxide (NO2) column densities as observed from the Global Ozone Monitoring Experiment (GOME) instrument aboard the second European Remote Sensing satellite (ERS-2) between 2000 and 2002, and from the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY aboard of the ESA’s ENVIronmental SATellite (SCIAMACHY /ENVISAT) between 2003 and 2009. Results reveal a threefold increase of the yearly tropospheric NO2 column density within the Beijing infrastructure-based urban area extent in 2009, which had quadrupled since 2000.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Ruktanonchaie002092,
title = {Estimating uncertainty in geospatial modelling at multiple spatial resolutions: the pattern of delivery via caesarean section in Tanzania},
author = {Corrine Warren Ruktanonchai and Jeremiah J Nieves and Nick W Ruktanonchai and Kristine Nilsen and Jessica E Steele and Zoe Matthews and Andrew J Tatem},
url = {https://gh.bmj.com/content/4/Suppl_5/e002092},
doi = {10.1136/bmjgh-2019-002092},
year  = {2020},
date = {2020-01-01},
journal = {BMJ Global Health},
volume = {4},
number = {Suppl 5},
publisher = {BMJ Specialist Journals},
abstract = {Visualising maternal and newborn health (MNH) outcomes at fine spatial resolutions is crucial to ensuring the most vulnerable women and children are not left behind in improving health. Disaggregated data on life-saving MNH interventions remain difficult to obtain, however, necessitating the use of Bayesian geostatistical models to map outcomes at small geographical areas. While these methods have improved model parameter estimates and precision among spatially correlated health outcomes and allowed for the quantification of uncertainty, few studies have examined the trade-off between higher spatial resolution modelling and how associated uncertainty propagates. Here, we explored the trade-off between model outcomes and associated uncertainty at increasing spatial resolutions by quantifying the posterior distribution of delivery via caesarean section (c-section) in Tanzania. Overall, in modelling delivery via c-section at multiple spatial resolutions, we demonstrated poverty to be negatively correlated across spatial resolutions, suggesting important disparities in obtaining life-saving obstetric surgery persist across sociodemographic factors. Lastly, we found that while uncertainty increased with higher spatial resolution input, model precision was best approximated at the highest spatial resolution, suggesting an important policy trade-off between identifying concealed spatial heterogeneities in health indicators.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Dotse-Gborgbortsie002020,
title = {The influence of distance and quality on utilisation of birthing services at health facilities in Eastern Region, Ghana},
author = {Winfred Dotse-Gborgbortsi and Duah Dwomoh and Victor Alegana and Allan Hill and Andrew J Tatem and Jim Wright},
url = {https://gh.bmj.com/content/4/Suppl_5/e002020},
doi = {10.1136/bmjgh-2019-002020},
year  = {2020},
date = {2020-01-01},
journal = {BMJ Global Health},
volume = {4},
number = {Suppl 5},
publisher = {BMJ Specialist Journals},
abstract = {Objectives Skilled birth attendance is the single most important intervention to reduce maternal mortality. However, studies have not used routinely collected health service birth data at named health facilities to understand the influence of distance and quality of care on childbirth service utilisation. Thus, this paper aims to quantify the influence of distance and quality of healthcare on utilisation of birthing services using routine health data in Eastern Region, Ghana.Methods We used a spatial interaction model (a model that predicts movement from one place to another) drawing on routine birth data, emergency obstetric care surveys, gridded estimates of number of pregnancies and health facility location. We compared travel distances by sociodemographic characteristics and mapped movement patterns.Results A kilometre increase in distance significantly reduced the prevalence rate of the number of women giving birth in health facilities by 6.7%. Although quality care increased the number of women giving birth in health facilities, its association was insignificant. Women travelled further than expected to give birth at facilities, on average journeying 4.7 km beyond the nearest facility with a recorded birth. Women in rural areas travelled 4 km more than urban women to reach a hospital. We also observed that 56% of women bypassed the nearest hospital to their community.Conclusion This analysis provides substantial opportunities for health planners and managers to understand further patterns of skilled birth service utilisation, and demonstrates the value of routine health data. Also, it provides evidence-based information for improving maternal health service provision by targeting specific communities and health facilities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lai2020.02.04.20020479,
title = {Assessing spread risk of Wuhan novel coronavirus within and beyond China, January-April 2020: a travel network-based modelling study},
author = {Shengjie Lai and Isaac I. Bogoch and Nick W Ruktanonchai and Alexander Watts and Xin Lu and Weizhong Yang and Hongjie Yu and Kamran Khan and Andrew J Tatem},
url = {https://www.medrxiv.org/content/early/2020/03/09/2020.02.04.20020479},
doi = {10.1101/2020.02.04.20020479},
year  = {2020},
date = {2020-01-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Background A novel coronavirus (2019-nCoV) emerged in Wuhan City, China, at the end of 2019 and has caused an outbreak of human-to-human transmission with a Public Health Emergency of International Concern declared by the World Health Organization on January 30, 2020.Aim We aimed to estimate the potential risk and geographic range of Wuhan novel coronavirus (2019-nCoV) spread within and beyond China from January through to April, 2020.Methods A series of domestic and international travel network-based connectivity and risk analyses were performed, by using de-identified and aggregated mobile phone data, air passenger itinerary data, and case reports.Results The cordon sanitaire of Wuhan is likely to have occurred during the latter stages of peak population numbers leaving the city before Lunar New Year (LNY), with travellers departing into neighbouring cities and other megacities in China. We estimated that 59,912 air passengers, of which 834 (95% UI: 478 - 1349) had 2019-nCoV infection, travelled from Wuhan to 382 cities outside of mainland China during the two weeks prior to Wuhan’s lockdown. The majority of these cities were in Asia, but major hubs in Europe, the US and Australia were also prominent, with strong correlation seen between predicted importation risks and reported cases. Because significant spread has already occurred, a large number of airline travellers (3.3 million under the scenario of 75% travel reduction from normal volumes) may be required to be screened at origin high-risk cities in China and destinations across the globe for the following three months of February to April, 2020 to effectively limit spread beyond its current extent.Conclusion Further spread of 2019-nCoV within China and international exportation is likely to occur. All countries, especially vulnerable regions, should be prepared for efforts to contain the 2019-nCoV infection.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis study was supported by the grants from the Bill & Melinda Gates Foundation (OPP1134076); the European Union Horizon 2020 (MOOD 874850); the National Natural Science Fund of China (81773498, 71771213, 91846301); National Science and Technology Major Project of China (2016ZX10004222-009); Program of Shanghai Academic/Technology Research Leader (18XD1400300); Hunan Science and Technology Plan Project (2017RS3040, 2018JJ1034). AJT is supported by funding from the Bill & Melinda Gates Foundation (OPP1106427, OPP1032350, OPP1134076, OPP1094793), the Clinton Health Access Initiative, the UK Department for International Development (DFID) and the Wellcome Trust (106866/Z/15/Z, 204613/Z/16/Z). HY is supported by funding from the National Natural Science Fund for Distinguished Young Scholars of China (No. 81525023); Program of Shanghai Academic/Technology Research Leader (No. 18XD1400300); and the United States National Institutes of Health (Comprehensive International Program for Research on AIDS grant U19 AI51915). The research team members were independent from the funding agencies. The funders had no role in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; and the preparation, review, or approval of the manuscript.Author DeclarationsAll relevant ethical guidelines have been followed; any necessary IRB and/or ethics committee approvals have been obtained and details of the IRB/oversight body are included in the manuscript.YesAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesThe datasets on monthly air passenger data in February – April, 2018 used in this study are available from Dr. Kamran Khan (Kamranatbluedot.global). The case data are available from Dr. Shengjie Lai (Shengjie.Laiatsoton.ac.uk). The datasets on holidays and air travel statistics from 2010 through 2018 used for validation are available on the WorldPop website (www.worldpop.org). The mobile phone datasets analysed during the current study are not publicly available since this would compromise the agreement with the data provider, but information on the process of requesting access to the data that support the findings of this study are available from Dr. Shengjie Lai (Shengjie.Laiatsoton.ac.uk).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.12688/gatesopenres.13107.1,
title = {A grid-based sample design framework for household surveys [version 1; peer review: 2 approved, 1 approved with reservations]},
author = {G Boo and E Darin and DR Thomson and AJ Tatem},
doi = {10.12688/gatesopenres.13107.1},
year  = {2020},
date = {2020-01-01},
journal = {Gates Open Research},
volume = {4},
number = {13},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{su12041501,
title = {Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges},
author = {Sébastien Dujardin and Damien Jacques and Jessica Steele and Catherine Linard},
url = {https://www.mdpi.com/2071-1050/12/4/1501},
doi = {10.3390/su12041501},
issn = {2071-1050},
year  = {2020},
date = {2020-01-01},
journal = {Sustainability},
volume = {12},
number = {4},
abstract = {Climate change places cities at increasing risk and poses a serious challenge for adaptation. As a response, novel sources of data combined with data-driven logics and advanced spatial modelling techniques have the potential for transformative change in the role of information in urban planning. However, little practical guidance exists on the potential opportunities offered by mobile phone data for enhancing adaptive capacities in urban areas. Building upon a review of spatial studies mobilizing mobile phone data, this paper explores the opportunities offered by such digital information for providing spatially-explicit assessments of urban vulnerability, and shows the ways these can help developing more dynamic strategies and tools for urban planning and disaster risk management. Finally, building upon the limitations of mobile phone data analysis, it discusses the key urban governance challenges that need to be addressed for supporting the emergence of transformative change in current planning frameworks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{UTAZI20203062,
title = {Geospatial variation in measles vaccine coverage through routine and campaign strategies in Nigeria: Analysis of recent household surveys},
author = {C. Edson Utazi and John Wagai and Oliver Pannell and Felicity T. Cutts and Dale A. Rhoda and Matthew J. Ferrari and Boubacar Dieng and Joseph Oteri and M. Carolina Danovaro-Holliday and Adeyemi Adeniran and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S0264410X20303017},
doi = {https://doi.org/10.1016/j.vaccine.2020.02.070},
issn = {0264-410X},
year  = {2020},
date = {2020-01-01},
journal = {Vaccine},
volume = {38},
number = {14},
pages = {3062-3071},
abstract = {Measles vaccination campaigns are conducted regularly in many low- and middle-income countries to boost measles control efforts and accelerate progress towards elimination. National and sometimes first-level administrative division campaign coverage may be estimated through post-campaign coverage surveys (PCCS). However, these large-area estimates mask significant geographic inequities in coverage at more granular levels. Here, we undertake a geospatial analysis of the Nigeria 2017–18 PCCS data to produce coverage estimates at 1 × 1 km resolution and the district level using binomial spatial regression models built on a suite of geospatial covariates and implemented in a Bayesian framework via the INLA-SPDE approach. We investigate the individual and combined performance of the campaign and routine immunization (RI) by mapping various indicators of coverage for children aged 9–59 months. Additionally, we compare estimated coverage before the campaign at 1 × 1 km and the district level with predicted coverage maps produced using other surveys conducted in 2013 and 2016–17. Coverage during the campaign was generally higher and more homogeneous than RI coverage but geospatial differences in the campaign’s reach of previously unvaccinated children are shown. Persistent areas of low coverage highlight the need for improved RI performance. The results can help to guide the conduct of future campaigns, improve vaccination monitoring and measles elimination efforts. Moreover, the approaches used here can be readily extended to other countries.},
keywords = {Geospatial analysis, Measles vaccine, Post-campaign coverage survey, Routine immunization, Supplementary immunization activities},
pubstate = {published},
tppubtype = {article}
}
@article{JIA2020235,
title = {Spatial Lifecourse Epidemiology and Infectious Disease Research},
author = {Peng Jia and Weihua Dong and Shujuan Yang and Zhicheng Zhan and La Tu and Shengjie Lai},
url = {https://www.sciencedirect.com/science/article/pii/S1471492220300052},
doi = {https://doi.org/10.1016/j.pt.2019.12.012},
issn = {1471-4922},
year  = {2020},
date = {2020-01-01},
journal = {Trends in Parasitology},
volume = {36},
number = {3},
pages = {235-238},
abstract = {Spatial lifecourse epidemiology aims to utilize advanced spatial, location-aware, and artificial intelligence technologies to investigate long-term effects of measurable biological, environmental, behavioral, and psychosocial factors on individual risk for chronic diseases. It could also further the research on infectious disease dynamics, risks, and consequences across the life course.},
keywords = {infectious disease, spatial analysis, spatial lifecourse epidemiology},
pubstate = {published},
tppubtype = {article}
}
@article{su12041501b,
title = {Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges},
author = {Sébastien Dujardin and Damien Jacques and Jessica Steele and Catherine Linard},
url = {https://www.mdpi.com/2071-1050/12/4/1501},
doi = {10.3390/su12041501},
issn = {2071-1050},
year  = {2020},
date = {2020-01-01},
journal = {Sustainability},
volume = {12},
number = {4},
abstract = {Climate change places cities at increasing risk and poses a serious challenge for adaptation. As a response, novel sources of data combined with data-driven logics and advanced spatial modelling techniques have the potential for transformative change in the role of information in urban planning. However, little practical guidance exists on the potential opportunities offered by mobile phone data for enhancing adaptive capacities in urban areas. Building upon a review of spatial studies mobilizing mobile phone data, this paper explores the opportunities offered by such digital information for providing spatially-explicit assessments of urban vulnerability, and shows the ways these can help developing more dynamic strategies and tools for urban planning and disaster risk management. Finally, building upon the limitations of mobile phone data analysis, it discusses the key urban governance challenges that need to be addressed for supporting the emergence of transformative change in current planning frameworks.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{rs12101545,
title = {Predicting Near-Future Built-Settlement Expansion Using Relative Changes in Small Area Populations},
author = {Jeremiah J. Nieves and Maksym Bondarenko and Alessandro Sorichetta and Jessica E. Steele and David Kerr and Alessandra Carioli and Forrest R. Stevens and Andrea E. Gaughan and Andrew J. Tatem},
url = {https://www.mdpi.com/2072-4292/12/10/1545},
doi = {10.3390/rs12101545},
issn = {2072-4292},
year  = {2020},
date = {2020-01-01},
journal = {Remote Sensing},
volume = {12},
number = {10},
abstract = {Advances in the availability of multi-temporal, remote sensing-derived global built-/human-settlements datasets can now provide globally consistent definitions of “human-settlement” at unprecedented spatial fineness. Yet, these data only provide a time-series of past extents and urban growth/expansion models have not had parallel advances at high-spatial resolution. Here our goal was to present a globally applicable predictive modelling framework, as informed by a short, preceding time-series of built-settlement extents, capable of producing annual, near-future built-settlement extents. To do so, we integrated a random forest, dasymetric redistribution, and autoregressive temporal models with open and globally available subnational data, estimates of built-settlement population, and environmental covariates. Using this approach, we trained the model on a 11 year time-series (2000-2010) of European Space Agency (ESA) Climate Change Initiative (CCI) Land Cover “Urban Areas” class and predicted annual, 100m resolution, binary settlement extents five years beyond the last observations (2011-2015) within varying environmental, urban morphological, and data quality contexts. We found that our model framework performed consistently across all sampled countries and, when compared to time-specific imagery, demonstrated the capacity to capture human-settlement missed by the input time-series and the withheld validation settlement extents. When comparing manually delineated building footprints of small settlements to the modelled extents, we saw that the modelling framework had a 12 percent increase in accuracy compared to withheld validation settlement extents. However, how this framework performs when using different input definitions of “urban” or settlement remains unknown. While this model framework is predictive and not explanatory in nature, it shows that globally available “off-the-shelf” datasets and relative changes in subnational population can be sufficient for accurate prediction of future settlement expansion. Further, this framework shows promise for predicting near-future settlement extents and provides a foundation for forecasts further into the future.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lai2020.06.17.20133843,
title = {Assessing the effect of global travel and contact reductions to mitigate the COVID-19 pandemic and resurgence},
author = {Shengjie Lai and Nick W Ruktanonchai and Alessandra Carioli and Corrine W Ruktanonchai and Jessica R Floyd and Olivia Prosper and Chi Zhang and Xiangjun Du and Weizhong Yang and Andrew J Tatem},
url = {https://www.medrxiv.org/content/early/2020/06/19/2020.06.17.20133843},
doi = {10.1101/2020.06.17.20133843},
year  = {2020},
date = {2020-01-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {Travel and physical distancing interventions have been implemented across the World to mitigate the COVID-19 pandemic, but studies are needed to quantify the effectiveness of these measures across regions and time. Timely population mobility data were obtained to measure travel and contact reductions in 135 countries or territories. During the 10 weeks of March 22 – May 30, 2020, domestic travel in study regions has dramatically reduced to a median of 59% (interquartile range [IQR] 43% - 73%) of normal levels seen before the outbreak, with international travel down to 26% (IQR 12% - 35%). If these travel and physical distancing interventions had not been deployed across the World, the cumulative number of cases might have shown a 97-fold (IQR 79 – 116) increase, as of May 31, 2020. However, effectiveness differed by the duration and intensity of interventions and relaxation scenarios, with variations in case severity seen across populations, regions, and seasons.One Sentence Summary Travel and physical distancing interventions across the World are key to mitigate the COVID-19 pandemic and resurgence.Competing Interest StatementThe authors have declared no competing interest.Funding StatementThis study was supported by the grants from the Bill & Melinda Gates Foundation (OPP1134076); the European Union Horizon 2020 (MOOD 874850). N.R. is supported by funding from the Bill & Melinda Gates Foundation (OPP1170969). O.P. is supported by the National Science Foundation (1816075). A.J.T. is supported by funding from the Bill & Melinda Gates Foundation (OPP1106427, OPP1032350, OPP1134076, OPP1094793), the Clinton Health Access Initiative, the UK Department for International Development (DFID) and the Wellcome Trust (106866/Z/15/Z, 204613/Z/16/Z).Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Ethical clearance for collecting and using secondary population mobility data was granted by the institutional review board of the University of Southampton (No. 48002). All data were supplied and analyzed in an anonymous format, without access to personal identifying information.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesCode for the model simulations is available at the following GitHub repository: https://github.com/wpgp/BEARmod. The data on COVID-19 cases and interventions reported by country are available from the data sources listed in Supplementary Materials. The parameters and population data for running simulations and estimating the severity are listed in Supplementary Data S1 to S2. The population movement data obtained from Baidu are available at: https://qianxi.baidu.com/. The Google COVID-19 Aggregated Mobility Research Dataset used for this study is available with permission of Google, LLC.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Rice2020.07.23.20161208,
title = {High variation expected in the pace and burden of SARS-CoV-2 outbreaks across sub-Saharan Africa},
author = {Benjamin L. Rice and Akshaya Annapragada and Rachel E. Baker and Marjolein Bruijning and Winfred Dotse-Gborgbortsi and Keitly Mensah and Ian F. Miller and Nkengafac Villyen Motaze and Antso Raherinandrasana and Malavika Rajeev and Julio Rakotonirina and Tanjona Ramiadantsoa and Fidisoa Rasambainarivo and Weiyu Yu and Bryan T. Grenfell and Andrew J. Tatem and C. Jessica E. Metcalf},
url = {https://www.medrxiv.org/content/early/2020/07/29/2020.07.23.20161208},
doi = {10.1101/2020.07.23.20161208},
year  = {2020},
date = {2020-01-01},
journal = {medRxiv},
publisher = {Cold Spring Harbor Laboratory Press},
abstract = {A surprising feature of the SARS-CoV-2 pandemic to date is the low burdens reported in sub-Saharan Africa (SSA) countries relative to other global regions. Potential explanations (e.g., warmer environments1, younger populations2–4) have yet to be framed within a comprehensive analysis accounting for factors that may offset the effects of climate and demography. Here, we synthesize factors hypothesized to shape the pace of this pandemic and its burden as it moves across SSA, encompassing demographic, comorbidity, climatic, healthcare and intervention capacity, and human mobility dimensions of risk. We find large scale diversity in probable drivers, such that outcomes are likely to be highly variable among SSA countries. While simulation shows that extensive climatic variation among SSA population centers has little effect on early outbreak trajectories, heterogeneity in connectivity is likely to play a large role in shaping the pace of viral spread. The prolonged, asynchronous outbreaks expected in weakly connected settings may result in extended stress to health systems. In addition, the observed variability in comorbidities and access to care will likely modulate the severity of infection: We show that even small shifts in the infection fatality ratio towards younger ages, which are likely in high risk settings, can eliminate the protective effect of younger populations. We highlight countries with elevated risk of ‘slow pace’, high burden outbreaks. Empirical data on the spatial extent of outbreaks within SSA countries, their patterns in severity over age, and the relationship between epidemic pace and health system disruptions are urgently needed to guide efforts to mitigate the high burden scenarios explored here.Competing Interest StatementThe authors have declared no competing interest.Funding StatementREB is supported by the Cooperative Institute for Modeling the Earth System (CIMES). AA acknowledges support from the NIH Medical Scientist Training Program 1T32GM136577. AJT is funded by the BMGF (OPP1182425, OPP1134076 and INV-002697). MB is funded by NWO Rubicon grant 019.192EN.017.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:NAAll necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesData and code have been deposited into a publicly available GitHub repository: https://github.com/labmetcalf/SSA-SARS-CoV-2 High resolution maps and further visualizations of the risk indicators and simulations studied here can be accessed online through an interactive tool: https://labmetcalf.shinyapps.io/covid19-burden-africa/ https://labmetcalf.shinyapps.io/covid19-burden-africa/},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{https://doi.org/10.1111/tmi.13460,
title = {Spatial inequalities in skilled attendance at birth in Ghana: a multilevel analysis integrating health facility databases with household survey data},
author = {Winfred Dotse-Gborgbortsi and Andrew J. Tatem and Victor Alegana and C. Edson Utazi and Corrine Warren Ruktanonchai and Jim Wright},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tmi.13460},
doi = {https://doi.org/10.1111/tmi.13460},
year  = {2020},
date = {2020-01-01},
journal = {Tropical Medicine & International Health},
volume = {25},
number = {9},
pages = {1044-1054},
abstract = {Abstract Objective This study aimed at using survey data to predict skilled attendance at birth (SBA) across Ghana from healthcare quality and health facility accessibility. Methods Through a cross-sectional, observational study, we used a random intercept mixed effects multilevel logistic modelling approach to estimate the odds of having SBA and then applied model estimates to spatial layers to assess the probability of SBA at high-spatial resolution across Ghana. We combined data from the Demographic and Health Survey (DHS), routine birth registers, a service provision assessment of emergency obstetric care services, gridded population estimates and modelled travel time to health facilities. Results Within an hour's travel, 97.1% of women sampled in the DHS could access any health facility, 96.6% could reach a facility providing birthing services, and 86.2% could reach a secondary hospital. After controlling for characteristics of individual women, living in an urban area and close proximity to a health facility with high-quality services were significant positive determinants of SBA uptake. The estimated variance suggests significant effects of cluster and region on SBA as 7.1% of the residual variation in the propensity to use SBA is attributed to unobserved regional characteristics and 16.5% between clusters within regions. Conclusion Given the expansion of primary care facilities in Ghana, this study suggests that higher quality healthcare services, as opposed to closer proximity of facilities to women, is needed to widen SBA uptake and improve maternal health.},
keywords = {accouchement qualifié, EmONC, financement EmONC, GIS, maternal health, quality care, santé maternelle, skilled birth attendance, soins de qualité, temps de trajet, travel time},
pubstate = {published},
tppubtype = {article}
}
@article{10.1093/cid/ciaa1057,
title = {Risk of Coronavirus Disease 2019 Transmission in Train Passengers: an Epidemiological and Modeling Study},
author = {Maogui Hu and Hui Lin and Jinfeng Wang and Chengdong Xu and Andrew J Tatem and Bin Meng and Xin Zhang and Yifeng Liu and Pengda Wang and Guizhen Wu and Haiyong Xie and Shengjie Lai},
url = {https://doi.org/10.1093/cid/ciaa1057},
doi = {10.1093/cid/ciaa1057},
issn = {1058-4838},
year  = {2020},
date = {2020-01-01},
journal = {Clinical Infectious Diseases},
volume = {72},
number = {4},
pages = {604-610},
abstract = {Train travel is a common mode of public transport across the globe; however, the risk of coronavirus disease 2019 (COVID-19) transmission among individual train passengers remains unclear.We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2334 index patients and 72 093 close contacts who had co-travel times of 0–8 hours from 19 December 2019 through 6 March 2020 in China. We analyzed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time.The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI], 5.3%–19.0%), with a mean of 0.32% (95% CI, .29%–.37%). Passengers in seats on the same row (including the adjacent passengers to the index patient) as the index patient had an average attack rate of 1.5% (95% CI, 1.3%–1.8%), higher than that in other rows (0.14% [95% CI, .11%–.17%]), with a relative risk (RR) of 11.2 (95% CI, 8.6–14.6). Travelers adjacent to the index patient had the highest attack rate (3.5% [95% CI, 2.9%–4.3%]) of COVID-19 infection (RR, 18.0 [95% CI, 13.9–23.4]) among all seats. The attack rate decreased with increasing distance, but increased with increasing co-travel time. The attack rate increased on average by 0.15% (P = .005) per hour of co-travel; for passengers in adjacent seats, this increase was 1.3% (P = .008), the highest among all seats considered.COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when traveling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{<LineBreak>doi:10.1126/science.abc5096,
title = {Assessing the impact of coordinated COVID-19 exit strategies across Europe},
author = {N. W. Ruktanonchai and J. R. Floyd and S. Lai and C. W. Ruktanonchai and A. Sadilek and P. Rente-Lourenco and X. Ben and A. Carioli and J. Gwinn and J. E. Steele and O. Prosper and A. Schneider and A. Oplinger and P. Eastham and A. J. Tatem},
url = {https://www.science.org/doi/abs/10.1126/science.abc5096},
doi = {10.1126/science.abc5096},
year  = {2020},
date = {2020-01-01},
journal = {Science},
volume = {369},
number = {6510},
pages = {1465-1470},
abstract = {Even during a pandemic, all countries—even islands—are dependent in one way or another on their neighbors. Without coordinated relaxation of nonpharmaceutical interventions (NPIs) among the most closely connected countries, it is difficult to envisage maintaining control of infectious viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Ruktanonchai et al. used mobility data from smartphones to estimate movements between administrative units across Europe before and after the implementation of NPIs for coronavirus disease 2019 (COVID-19). Modeling disease dynamics under alternative scenarios of countries releasing NPIs, in particular stay-at-home orders, showed that if countries do not coordinate their NPIs when they relax lockdown, resurgence of disease occurs sooner. Coordination of on-off NPIs would significantly increase their effectiveness at reducing transmission across Europe. Science, this issue p. 1465 Coordination among countries in easing restrictions is key to preventing resurgent COVID-19 outbreaks and stopping community transmission. As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1073/pnas.1922663117,
title = {The duration of travel impacts the spatial dynamics of infectious diseases},
author = {John R. Giles and Elisabeth Erbach-Schoenberg and Andrew J. Tatem and Lauren Gardner and Ottar N. Bjørnstad and C. J. E. Metcalf and Amy Wesolowski},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1922663117},
doi = {10.1073/pnas.1922663117},
year  = {2020},
date = {2020-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {117},
number = {36},
pages = {22572-22579},
abstract = {The spatial dynamics of infectious-disease spread are driven by the biology of the pathogen and the connectivity patterns among human populations. Models of disease spread often use mobile-phone calling records to calculate the number of trips made among locations in the population, which is used as a proxy for population connectivity. However, the amount of time people spend in a destination (trip duration) also impacts the probability of onward disease transmission among locations. Here, we developed models that incorporate trip duration into the mechanism of disease spread, which helps us understand how fast and how far a pathogen might spread in a human population. Humans can impact the spatial transmission dynamics of infectious diseases by introducing pathogens into susceptible environments. The rate at which this occurs depends in part on human-mobility patterns. Increasingly, mobile-phone usage data are used to quantify human mobility and investigate the impact on disease dynamics. Although the number of trips between locations and the duration of those trips could both affect infectious-disease dynamics, there has been limited work to quantify and model the duration of travel in the context of disease transmission. Using mobility data inferred from mobile-phone calling records in Namibia, we calculated both the number of trips between districts and the duration of these trips from 2010 to 2014. We fit hierarchical Bayesian models to these data to describe both the mean trip number and duration. Results indicate that trip duration is positively related to trip distance, but negatively related to the destination population density. The highest volume of trips and shortest trip durations were among high-density districts, whereas trips among low-density districts had lower volume with longer duration. We also analyzed the impact of including trip duration in spatial-transmission models for a range of pathogens and introduction locations. We found that inclusion of trip duration generally delays the rate of introduction, regardless of pathogen, and that the variance and uncertainty around spatial spread increases proportionally with pathogen-generation time. These results enhance our understanding of disease-dispersal dynamics driven by human mobility, which has potential to elucidate optimal spatial and temporal scales for epidemic interventions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0232702,
title = {Modeling human migration across spatial scales in Colombia},
author = {Amir S. Siraj and Alessandro Sorichetta and Guido España and Andrew J. Tatem and T. Alex Perkins},
url = {https://doi.org/10.1371/journal.pone.0232702},
doi = {10.1371/journal.pone.0232702},
year  = {2020},
date = {2020-01-01},
journal = {PLOS ONE},
volume = {15},
number = {5},
pages = {1-20},
publisher = {Public Library of Science},
abstract = {Human mobility, both short and long term, are important considerations in the study of numerous systems. Economic and technological advances have led to a more interconnected global community, further increasing the need for considerations of human mobility. While data on human mobility are better recorded in many developed countries, availability of such data remains limited in many low- and middle-income countries around the world, particularly at the fine temporal and spatial scales required by many applications. In this study, we used 5-year census-based internal migration microdata for 32 departments in Colombia (i.e., Admin-1 level) to develop a novel spatial interaction modeling approach for estimating migration, at a finer spatial scale, among the 1,122 municipalities in the country (i.e., Admin-2 level). Our modeling approach addresses a significant lack of migration data at administrative unit levels finer than those at which migration data are typically recorded. Due to the widespread availability of census-based migration microdata at the Admin-1 level, our modeling approach opens up for the possibilities of modeling migration patterns at Admin-2 and Admin-3 levels across many other countries where such data are currently lacking.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1073/pnas.1913050117,
title = {National population mapping from sparse survey data: A hierarchical Bayesian modeling framework to account for uncertainty},
author = {Douglas R. Leasure and Warren C. Jochem and Eric M. Weber and Vincent Seaman and Andrew J. Tatem},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1913050117},
doi = {10.1073/pnas.1913050117},
year  = {2020},
date = {2020-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {117},
number = {39},
pages = {24173-24179},
abstract = {High-resolution population estimates are essential for government planning, development projects, and public health campaigns, but countries where this information is most needed are often where recent national census data are least available. We present a modeling framework that combines recent neighborhood-scale microcensus surveys with national-scale data from satellite images and digital maps to estimate population sizes for every 100-m grid square nationally. We present a case study from Nigeria where population estimates with national coverage were produced using household survey data from 1,141 locations. This work represents a significant step toward achieving high-resolution population estimates with national coverage from sparse population data while providing reliable estimates of uncertainty at any spatial scale. Population estimates are critical for government services, development projects, and public health campaigns. Such data are typically obtained through a national population and housing census. However, population estimates can quickly become inaccurate in localized areas, particularly where migration or displacement has occurred. Some conflict-affected and resource-poor countries have not conducted a census in over 10 y. We developed a hierarchical Bayesian model to estimate population numbers in small areas based on enumeration data from sample areas and nationwide information about administrative boundaries, building locations, settlement types, and other factors related to population density. We demonstrated this model by estimating population sizes in every 10- m grid cell in Nigeria with national coverage. These gridded population estimates and areal population totals derived from them are accompanied by estimates of uncertainty based on Bayesian posterior probabilities. The model had an overall error rate of 67 people per hectare (mean of absolute residuals) or 43% (using scaled residuals) for predictions in out-of-sample survey areas (approximately 3 ha each), with increased precision expected for aggregated population totals in larger areas. This statistical approach represents a significant step toward estimating populations at high resolution with national coverage in the absence of a complete and recent census, while also providing reliable estimates of uncertainty to support informed decision making.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{rs12233847,
title = {Using GIS and Machine Learning to Classify Residential Status of Urban Buildings in Low and Middle Income Settings},
author = {Christopher T. Lloyd and Hugh J. W. Sturrock and Douglas R. Leasure and Warren C. Jochem and Attila N. Lázár and Andrew J. Tatem},
url = {https://www.mdpi.com/2072-4292/12/23/3847},
doi = {10.3390/rs12233847},
issn = {2072-4292},
year  = {2020},
date = {2020-01-01},
journal = {Remote Sensing},
volume = {12},
number = {23},
abstract = {Utilising satellite images for planning and development is becoming a common practice as computational power and machine learning capabilities expand. In this paper, we explore the use of satellite image derived building footprint data to classify the residential status of urban buildings in low and middle income countries. A recently developed ensemble machine learning building classification model is applied for the first time to the Democratic Republic of the Congo, and to Nigeria. The model is informed by building footprint and label data of greater completeness and attribute consistency than have previously been available for these countries. A GIS workflow is described that semiautomates the preparation of data for input to the model. The workflow is designed to be particularly useful to those who apply the model to additional countries and use input data from diverse sources. Results show that the ensemble model correctly classifies between 85% and 93% of structures as residential and nonresidential across both countries. The classification outputs are likely to be valuable in the modelling of human population distributions, as well as in a range of related applications such as urban planning, resource allocation, and service delivery.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1093/jtm/taaa200,
title = {Uncovering two phases of early intercontinental COVID-19 transmission dynamics},
author = {Jing Yang and Juan Li and Shengjie Lai and Corrine W Ruktanonchai and Weijia Xing and Alessandra Carioli and Peihan Wang and Nick W Ruktanonchai and Ruiyun Li and Jessica R Floyd and Liang Wang and Yuhai Bi and Weifeng Shi and Andrew J Tatem},
url = {https://doi.org/10.1093/jtm/taaa200},
doi = {10.1093/jtm/taaa200},
issn = {1708-8305},
year  = {2020},
date = {2020-01-01},
journal = {Journal of Travel Medicine},
volume = {27},
number = {8},
abstract = {The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies.We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19.Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission.Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.},
note = {taaa200},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pntd.0008757,
title = {Risk mapping of scrub typhus infections in Qingdao city, China},
author = {Hualei Xin and Peng Fu and Junling Sun and Shengjie Lai and Wenbiao Hu and Archie C. A. Clements and Jianping Sun and Jing Cui and Simon I. Hay and Xiaojing Li and Zhongjie Li},
url = {https://doi.org/10.1371/journal.pntd.0008757},
doi = {10.1371/journal.pntd.0008757},
year  = {2020},
date = {2020-01-01},
journal = {PLOS Neglected Tropical Diseases},
volume = {14},
number = {12},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Background The emergence and re-emergence of scrub typhus has been reported in the past decade in many global regions. In this study, we aim to identify potential scrub typhus infection risk zones with high spatial resolution in Qingdao city, in which scrub typhus is endemic, to guide local prevention and control strategies. Methodology/Principal findings Scrub typhus cases in Qingdao city during 2006–2018 were retrieved from the Chinese National Infectious Diseases Reporting System. We divided Qingdao city into 1,101 gridded squares and classified them into two categories: areas with and without recorded scrub typhus cases. A boosted regression tree model was used to explore environmental and socioeconomic covariates associated with scrub typhus occurrence and predict the risk of scrub typhus infection across the whole area of Qingdao city. A total of 989 scrub typhus cases were reported in Qingdao from 2006–2018, with most cases located in rural and suburban areas. The predicted risk map generated by the boosted regression tree models indicated that the highest infection risk areas were mainly concentrated in the mid-east and northeast regions of Qingdao, with gross domestic product (20.9%±1.8% standard error) and annual cumulative precipitation (20.3%±1.1%) contributing the most to the variation in the models. By using a threshold environmental suitability value of 0.26, we identified 757 squares (68.7% of the total) with a favourable environment for scrub typhus infection; 66.2% (501/757) of the squares had not yet recorded cases. It is estimated that 6.32 million people (72.5% of the total population) reside in areas with a high risk of scrub typhus infection. Conclusions/Significance Many locations in Qingdao city with no recorded scrub typhus cases were identified as being at risk for scrub typhus occurrence. In these at-risk areas, awareness and capacity for case diagnosis and treatment should be enhanced in the local medical service institutes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1080/17538947.2019.1633424,
title = {Comparisons of two global built area land cover datasets in methods to disaggregate human population in eleven countries from the global South},
author = {Forrest R. Stevens and Andrea E. Gaughan and Jeremiah J. Nieves and Adam King and Alessandro Sorichetta and Catherine Linard and Andrew J. Tatem},
url = {https://doi.org/10.1080/17538947.2019.1633424},
doi = {10.1080/17538947.2019.1633424},
year  = {2020},
date = {2020-01-01},
journal = {International Journal of Digital Earth},
volume = {13},
number = {1},
pages = {78-100},
publisher = {Taylor & Francis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{NIEVES2020101444,
title = {Annually modelling built-settlements between remotely-sensed observations using relative changes in subnational populations and lights at night},
author = {Jeremiah J. Nieves and Alessandro Sorichetta and Catherine Linard and Maksym Bondarenko and Jessica E. Steele and Forrest R. Stevens and Andrea E. Gaughan and Alessandra Carioli and Donna J. Clarke and Thomas Esch and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S019897151930290X},
doi = {https://doi.org/10.1016/j.compenvurbsys.2019.101444},
issn = {0198-9715},
year  = {2020},
date = {2020-01-01},
journal = {Computers, Environment and Urban Systems},
volume = {80},
pages = {101444},
abstract = {Mapping urban features/human built-settlement extents at the annual time step has a wide variety of applications in demography, public health, sustainable development, and many other fields. Recently, while more multitemporal urban features/human built-settlement datasets have become available, issues still exist in remotely-sensed imagery due to spatial and temporal coverage, adverse atmospheric conditions, and expenses involved in producing such datasets. Remotely-sensed annual time-series of urban/built-settlement extents therefore do not yet exist and cover more than specific local areas or city-based regions. Moreover, while a few high-resolution global datasets of urban/built-settlement extents exist for key years, the observed date often deviates many years from the assigned one. These challenges make it difficult to increase temporal coverage while maintaining high fidelity in the spatial resolution. Here we describe an interpolative and flexible modelling framework for producing annual built-settlement extents. We use a combined technique of random forest and spatio-temporal dasymetric modelling with open source subnational data to produce annual 100 m × 100 m resolution binary built-settlement datasets in four test countries located in varying environmental and developmental contexts for test periods of five-year gaps. We find that in the majority of years, across all study areas, the model correctly identified between 85 and 99% of pixels that transition to built-settlement. Additionally, with few exceptions, the model substantially out performed a model that gave every pixel equal chance of transitioning to built-settlement in each year. This modelling framework shows strong promise for filling gaps in cross-sectional urban features/built-settlement datasets derived from remotely-sensed imagery, provides a base upon which to create urban future/built-settlement extent projections, and enables further exploration of the relationships between urban/built-settlement area and population dynamics.},
keywords = {Built-settlements, Dasymetric modelling, Population, Random forest, Spatial growth, Urban features},
pubstate = {published},
tppubtype = {article}
}
@article{CUTTS2020979,
title = {Using models to shape measles control and elimination strategies in low- and middle-income countries: A review of recent applications},
author = {F. T. Cutts and E. Dansereau and M. J. Ferrari and M. Hanson and K. A. McCarthy and C. J. E. Metcalf and S. Takahashi and A. J. Tatem and N. Thakkar and S. Truelove and E. Utazi and A. Wesolowski and A. K. Winter},
url = {https://www.sciencedirect.com/science/article/pii/S0264410X19315439},
doi = {https://doi.org/10.1016/j.vaccine.2019.11.020},
issn = {0264-410X},
year  = {2020},
date = {2020-01-01},
journal = {Vaccine},
volume = {38},
number = {5},
pages = {979-992},
abstract = {After many decades of vaccination, measles epidemiology varies greatly between and within countries. National immunization programs are therefore encouraged to conduct regular situation analyses and to leverage models to adapt interventions to local needs. Here, we review applications of models to develop locally tailored interventions to support control and elimination efforts. In general, statistical and semi-mechanistic transmission models can be used to synthesize information from vaccination coverage, measles incidence, demographic, and/or serological data, offering a means to estimate the spatial and age-specific distribution of measles susceptibility. These estimates complete the picture provided by vaccination coverage alone, by accounting for natural immunity. Dynamic transmission models can then be used to evaluate the relative impact of candidate interventions for measles control and elimination and the expected future epidemiology. In most countries, models predict substantial numbers of susceptible individuals outside the age range of routine vaccination, which affects outbreak risk and necessitates additional intervention to achieve elimination. More effective use of models to inform both vaccination program planning and evaluation requires the development of training to enhance broader understanding of models and where feasible, building capacity for modelling in-country, pipelines for rapid evaluation of model predictions using surveillance data, and clear protocols for incorporating model results into decision-making.},
keywords = {Elimination, Epidemiology, Mathematical models, Measles, Measles vaccination, Rubella},
pubstate = {published},
tppubtype = {article}
}
@article{Kugler2019,
title = {People and Pixels 20 years later: the current data landscape and research trends blending population and environmental data},
author = {Tracy A. Kugler and Kathryn Grace and David J. Wrathall and Alex Sherbinin and David Van Riper and Christoph Aubrecht and Douglas Comer and Susana B. Adamo and Guido Cervone and Ryan Engstrom and Carolynne Hultquist and Andrea E. Gaughan and Catherine Linard and Emilio Moran and Forrest Stevens and Andrew J. Tatem and Beth Tellman and Jamon Van Den Hoek},
url = {https://doi.org/10.1007/s11111-019-00326-5},
doi = {10.1007/s11111-019-00326-5},
issn = {1573-7810},
year  = {2019},
date = {2019-12-01},
journal = {Population and Environment},
volume = {41},
number = {2},
pages = {209-234},
abstract = {In 1998, the National Research Council published People and Pixels: Linking Remote Sensing and Social Science. The volume focused on emerging research linking changes in human populations and land use/land cover to shed light on issues of sustainability, human livelihoods, and conservation, and led to practical innovations in agricultural planning, hazard impact analysis, and drought monitoring. Since then, new research opportunities have emerged thanks to the growing variety of remotely sensed data sources, an increasing array of georeferenced social science data, including data from mobile devices, and access to powerful computation cyberinfrastructure. In this article, we outline the key extensions of the People and Pixels foundation since 1998 and highlight several breakthroughs in research on human--environment interactions. We also identify pressing research problems---disaster, famine, drought, war, poverty, climate change---and explore how interdisciplinary approaches integrating people and pixels are being used to address them.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Zhang2019,
title = {Patterns of human social contact and contact with animals in Shanghai, China},
author = {Juanjuan Zhang and Petra Klepac and Jonathan M. Read and Alicia Rosello and Xiling Wang and Shengjie Lai and Meng Li and Yujian Song and Qingzhen Wei and Hao Jiang and Juan Yang and Henry Lynn and Stefan Flasche and Mark Jit and Hongjie Yu},
url = {https://doi.org/10.1038/s41598-019-51609-8},
doi = {10.1038/s41598-019-51609-8},
issn = {2045-2322},
year  = {2019},
date = {2019-10-22},
journal = {Scientific Reports},
volume = {9},
number = {1},
pages = {15141},
abstract = {East Asia is as a principal hotspot for emerging zoonotic infections. Understanding the likely pathways for their emergence and spread requires knowledge on human-human and human-animal contacts, but such studies are rare. We used self-completed and interviewer-completed contact diaries to quantify patterns of these contacts for 965 individuals in 2017/2018 in a high-income densely-populated area of China, Shanghai City. Interviewer-completed diaries recorded more social contacts (19.3 vs. 18.0) and longer social contact duration (35.0 vs. 29.1thinspacehours) than self-reporting. Strong age-assortativity was observed in all age groups especially among young participants (aged 7--20) and middle aged participants (25--55 years). 17.7% of participants reported touching animals (15.3% (pets), 0.0% (poultry) and 0.1% (livestock)). Human-human contact was very frequent but contact with animals (especially poultry) was rare although associated with frequent human-human contact. Hence, this densely populated area is more likely to act as an accelerator for human-human spread but less likely to be at the source of a zoonosis outbreak. We also propose that telephone interview at the end of reporting day is a potential improvement of the design of future contact surveys.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lourenço2019,
title = {Strengthening surveillance systems for malaria elimination: a global landscaping of system performance, 2015--2017},
author = {Christopher Lourenço and Andrew J. Tatem and Peter M. Atkinson and Justin M. Cohen and Deepa Pindolia and Darlene Bhavnani and Arnaud Le Menach},
url = {https://doi.org/10.1186/s12936-019-2960-2},
doi = {10.1186/s12936-019-2960-2},
issn = {1475-2875},
year  = {2019},
date = {2019-09-18},
journal = {Malaria Journal},
volume = {18},
number = {1},
pages = {315},
abstract = {Surveillance is a core component of an effective system to support malaria elimination. Poor surveillance data will prevent countries from monitoring progress towards elimination and targeting interventions to the last remaining at-risk places. An evaluation of the performance of surveillance systems in 16 countries was conducted to identify key gaps which could be addressed to build effective systems for malaria elimination.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Thomson2019,
title = {Extending Data for Urban Health Decision-Making: a Menu of New and Potential Neighborhood-Level Health Determinants Datasets in LMICs},
author = {Dana R. Thomson and Catherine Linard and Sabine Vanhuysse and Jessica E. Steele and Michal Shimoni and José Siri and Waleska Teixeira Caiaffa and Megumi Rosenberg and Eléonore Wolff and Ta"is Grippa and Stefanos Georganos and Helen Elsey},
url = {https://doi.org/10.1007/s11524-019-00363-3},
doi = {10.1007/s11524-019-00363-3},
issn = {1468-2869},
year  = {2019},
date = {2019-08-01},
journal = {Journal of Urban Health},
volume = {96},
number = {4},
pages = {514-536},
abstract = {Area-level indicators of the determinants of health are vital to plan and monitor progress toward targets such as the Sustainable Development Goals (SDGs). Tools such as the Urban Health Equity Assessment and Response Tool (Urban HEART) and UN-Habitat Urban Inequities Surveys identify dozens of area-level health determinant indicators that decision-makers can use to track and attempt to address population health burdens and inequalities. However, questions remain as to how such indicators can be measured in a cost-effective way. Area-level health determinants reflect the physical, ecological, and social environments that influence health outcomes at community and societal levels, and include, among others, access to quality health facilities, safe parks, and other urban services, traffic density, level of informality, level of air pollution, degree of social exclusion, and extent of social networks. The identification and disaggregation of indicators is necessarily constrained by which datasets are available. Typically, these include household- and individual-level survey, census, administrative, and health system data. However, continued advancements in earth observation (EO), geographical information system (GIS), and mobile technologies mean that new sources of area-level health determinant indicators derived from satellite imagery, aggregated anonymized mobile phone data, and other sources are also becoming available at granular geographic scale. Not only can these data be used to directly calculate neighborhood- and city-level indicators, they can be combined with survey, census, administrative and health system data to model household- and individual-level outcomes (e.g., population density, household wealth) with tremendous detail and accuracy. WorldPop and the Demographic and Health Surveys (DHS) have already modeled dozens of household survey indicators at country or continental scales at resolutions of 1thinspacetexttimesthinspace1 km or even smaller. This paper aims to broaden perceptions about which types of datasets are available for health and development decision-making. For data scientists, we flag area-level indicators at city and sub-city scales identified by health decision-makers in the SDGs, Urban HEART, and other initiatives. For local health decision-makers, we summarize a menu of new datasets that can be feasibly generated from EO, mobile phone, and other spatial data---ideally to be made free and publicly available---and offer lay descriptions of some of the difficulties in generating such data products.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Kraemer2019,
title = {Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus},
author = {Moritz U. G. Kraemer and Robert C. Reiner and Oliver J. Brady and Jane P. Messina and Marius Gilbert and David M. Pigott and Dingdong Yi and Kimberly Johnson and Lucas Earl and Laurie B. Marczak and Shreya Shirude and Nicole Davis Weaver and Donal Bisanzio and T. Alex Perkins and Shengjie Lai and Xin Lu and Peter Jones and Giovanini E. Coelho and Roberta G. Carvalho and Wim Van Bortel and Cedric Marsboom and Guy Hendrickx and Francis Schaffner and Chester G. Moore and Heinrich H. Nax and Linus Bengtsson and Erik Wetter and Andrew J. Tatem and John S. Brownstein and David L. Smith and Louis Lambrechts and Simon Cauchemez and Catherine Linard and Nuno R. Faria and Oliver G. Pybus and Thomas W. Scott and Qiyong Liu and Hongjie Yu and G. R. William Wint and Simon I. Hay and Nick Golding},
url = {https://doi.org/10.1038/s41564-019-0376-y},
doi = {10.1038/s41564-019-0376-y},
issn = {2058-5276},
year  = {2019},
date = {2019-05-01},
journal = {Nature Microbiology},
volume = {4},
number = {5},
pages = {854-863},
abstract = {The global population at risk from mosquito-borne diseases---including dengue, yellow fever, chikungunya and Zika---is expanding in concert with changes in the distribution of two key vectors: Aedes aegypti and Aedes albopictus. The distribution of these species is largely driven by both human movement and the presence of suitable climate. Using statistical mapping techniques, we show that human movement patterns explain the spread of both species in Europe and the United States following their introduction. We find that the spread of Ae. aegypti is characterized by long distance importations, while Ae. albopictus has expanded more along the fringes of its distribution. We describe these processes and predict the future distributions of both species in response to accelerating urbanization, connectivity and climate change. Global surveillance and control efforts that aim to mitigate the spread of chikungunya, dengue, yellow fever and Zika viruses must consider the so far unabated spread of these mosquitos. Our maps and predictions offer an opportunity to strategically target surveillance and control programmes and thereby augment efforts to reduce arbovirus burden in human populations globally.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Utazi2019,
title = {Mapping vaccination coverage to explore the effects of delivery mechanisms and inform vaccination strategies},
author = {C. Edson Utazi and Julia Thorley and Victor A. Alegana and Matthew J. Ferrari and Saki Takahashi and C. Jessica E. Metcalf and Justin Lessler and Felicity T. Cutts and Andrew J. Tatem},
url = {https://doi.org/10.1038/s41467-019-09611-1},
doi = {10.1038/s41467-019-09611-1},
issn = {2041-1723},
year  = {2019},
date = {2019-04-09},
journal = {Nature Communications},
volume = {10},
number = {1},
pages = {1633},
abstract = {The success of vaccination programs depends largely on the mechanisms used in vaccine delivery. National immunization programs offer childhood vaccines through fixed and outreach services within the health system and often, additional supplementary immunization activities (SIAs) are undertaken to fill gaps and boost coverage. Here, we map predicted coverage at 1thinspacetexttimesthinspace1thinspacekm spatial resolution in five low- and middle-income countries to identify areas that are under-vaccinated via each delivery method using Demographic and Health Surveys data. We compare estimates of the coverage of the third dose of diphtheria-tetanus-pertussis-containing vaccine (DTP3), which is typically delivered through routine immunization (RI), with those of measles-containing vaccine (MCV) for which SIAs are also undertaken. We find that SIAs have boosted MCV coverage in some places, but not in others, particularly where RI had been deficient, as depicted by DTP coverage. The modelling approaches outlined here can help to guide geographical prioritization and strategy design.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tusting2019,
title = {Mapping changes in housing in sub-Saharan Africa from 2000 to 2015},
author = {Lucy S. Tusting and Donal Bisanzio and Graham Alabaster and Ewan Cameron and Richard Cibulskis and Michael Davies and Seth Flaxman and Harry S. Gibson and Jakob Knudsen and Charles Mbogo and Fredros O. Okumu and Lorenz Seidlein and Daniel J. Weiss and Steve W. Lindsay and Peter W. Gething and Samir Bhatt},
url = {https://doi.org/10.1038/s41586-019-1050-5},
doi = {10.1038/s41586-019-1050-5},
issn = {1476-4687},
year  = {2019},
date = {2019-04-01},
journal = {Nature},
volume = {568},
number = {7752},
pages = {391-394},
abstract = {Access to adequate housing is a fundamental human right, essential to human security, nutrition and health, and a core objective of the United Nations Sustainable Development Goals1,2. Globally, the housing need is most acute in Africa, where the population will more than double by 2050. However, existing data on housing quality across Africa are limited primarily to urban areas and are mostly recorded at the national level. Here we quantify changes in housing in sub-Saharan Africa from 2000 to 2015 by combining national survey data within a geostatistical framework. We show a marked transformation of housing in urban and rural sub-Saharan Africa between 2000 and 2015, with the prevalence of improved housing (with improved water and sanitation, sufficient living area and durable construction) doubling from 11% (95% confidence interval, 10--12%) to 23% (21--25%). However, 53 (50--57) million urban Africans (47% (44--50%) of the urban population analysed) were living in unimproved housing in 2015. We provide high-resolution, standardized estimates of housing conditions across sub-Saharan Africa. Our maps provide a baseline for measuring change and a mechanism to guide interventions during the era of the Sustainable Development Goals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.7554/eLife.43510,
title = {Using parasite genetic and human mobility data to infer local and cross-border malaria connectivity in Southern Africa},
author = {Sofonias Tessema and Amy Wesolowski and Anna Chen and Maxwell Murphy and Jordan Wilheim and Anna-Rosa Mupiri and Nick W Ruktanonchai and Victor A Alegana and Andrew J Tatem and Munyaradzi Tambo and Bradley Didier and Justin M Cohen and Adam Bennett and Hugh JW Sturrock and Roland Gosling and Michelle S Hsiang and David L Smith and Davis R Mumbengegwi and Jennifer L Smith and Bryan Greenhouse},
editor = {Ben Cooper and Neil M Ferguson and Samir Bhatt and Thomas S Churcher},
url = {https://doi.org/10.7554/eLife.43510},
doi = {10.7554/eLife.43510},
issn = {2050-084X},
year  = {2019},
date = {2019-04-01},
journal = {eLife},
volume = {8},
pages = {e43510},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Local and cross-border importation remain major challenges to malaria elimination and are difficult to measure using traditional surveillance data. To address this challenge, we systematically collected parasite genetic data and travel history from thousands of malaria cases across northeastern Namibia and estimated human mobility from mobile phone data. We observed strong fine-scale spatial structure in local parasite populations, providing positive evidence that the majority of cases were due to local transmission. This result was largely consistent with estimates from mobile phone and travel history data. However, genetic data identified more detailed and extensive evidence of parasite connectivity over hundreds of kilometers than the other data, within Namibia and across the Angolan and Zambian borders. Our results provide a framework for incorporating genetic data into malaria surveillance and provide evidence that both strengthening of local interventions and regional coordination are likely necessary to eliminate malaria in this region of Southern Africa.},
keywords = {genetic epidemiology, malaria, Mobility, parasite genomics},
pubstate = {published},
tppubtype = {article}
}
@article{Lai2019b,
title = {Changing epidemiology and challenges of malaria in China towards elimination},
author = {Shengjie Lai and Junling Sun and Nick W. Ruktanonchai and Sheng Zhou and Jianxing Yu and Isobel Routledge and Liping Wang and Yaming Zheng and Andrew J. Tatem and Zhongjie Li},
url = {https://doi.org/10.1186/s12936-019-2736-8},
doi = {10.1186/s12936-019-2736-8},
issn = {1475-2875},
year  = {2019},
date = {2019-03-29},
journal = {Malaria Journal},
volume = {18},
number = {1},
pages = {107},
abstract = {Historically, malaria had been a widespread disease in China. A national plan was launched in China in 2010, aiming to eliminate malaria by 2020. In 2017, no indigenous cases of malaria were detected in China for the first time. To provide evidence for precise surveillance and response to achieve elimination goal, a comprehensive study is needed to determine the changing epidemiology of malaria and the challenges towards elimination.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Kraemer2019b,
title = {Utilizing general human movement models to predict the spread of emerging infectious diseases in resource poor settings},
author = {M. U. G. Kraemer and N. Golding and D. Bisanzio and S. Bhatt and D. M. Pigott and S. E. Ray and O. J. Brady and J. S. Brownstein and N. R. Faria and D. A. T. Cummings and O. G. Pybus and D. L. Smith and A. J. Tatem and S. I. Hay and R. C. Reiner},
url = {https://doi.org/10.1038/s41598-019-41192-3},
doi = {10.1038/s41598-019-41192-3},
issn = {2045-2322},
year  = {2019},
date = {2019-03-26},
journal = {Scientific Reports},
volume = {9},
number = {1},
pages = {5151},
abstract = {Human mobility is an important driver of geographic spread of infectious pathogens. Detailed information about human movements during outbreaks are, however, difficult to obtain and may not be available during future epidemics. The Ebola virus disease (EVD) outbreak in West Africa between 2014--16 demonstrated how quickly pathogens can spread to large urban centers following one cross-species transmission event. Here we describe a flexible transmission model to test the utility of generalised human movement models in estimating EVD cases and spatial spread over the course of the outbreak. A transmission model that includes a general model of human mobility significantly improves prediction of EVD's incidence compared to models without this component. Human movement plays an important role not only to ignite the epidemic in locations previously disease free, but over the course of the entire epidemic. We also demonstrate important differences between countries in population mixing and the improved prediction attributable to movement metrics. Given their relative rareness, locally derived mobility data are unlikely to exist in advance of future epidemics or pandemics. Our findings show that transmission patterns derived from general human movement models can improve forecasts of spatio-temporal transmission patterns in places where local mobility data is unavailable.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lai2019,
title = {Exploring the use of mobile phone data for national migration statistics},
author = {Shengjie Lai and Elisabeth zu Erbach-Schoenberg and Carla Pezzulo and Nick W. Ruktanonchai and Alessandro Sorichetta and Jessica Steele and Tracey Li and Claire A. Dooley and Andrew J. Tatem},
url = {https://doi.org/10.1057/s41599-019-0242-9},
doi = {10.1057/s41599-019-0242-9},
issn = {2055-1045},
year  = {2019},
date = {2019-03-26},
journal = {Palgrave Communications},
volume = {5},
number = {1},
pages = {34},
abstract = {Statistics on internal migration are important for keeping estimates of subnational population numbers up-to-date, as well as urban planning, infrastructure development, and impact assessment, among other applications. However, migration flow statistics typically remain constrained by the logistics of infrequent censuses or surveys. The penetration rate of mobile phones is now high across the globe with rapid recent increases in ownership in low-income countries. Analyzing the changing spatiotemporal distribution of mobile phone users through anonymized call detail records (CDRs) offers the possibility to measure migration at multiple temporal and spatial scales. Based on a dataset of 72 billion anonymized CDRs in Namibia from October 2010 to April 2014, we explore how internal migration estimates can be derived and modeled from CDRs at subnational and annual scales, and how precision and accuracy of these estimates compare to census-derived migration statistics. We also demonstrate the use of CDRs to assess how migration patterns change over time, with a finer temporal resolution compared with censuses. Moreover, we show how gravity-type spatial interaction models built using CDRs can accurately capture migration flows. The results highlight that estimates of migration flows made using mobile phone data is a promising avenue for complementing more traditional national migration statistics and obtaining more timely and local data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Oidtman2019,
title = {Inter-annual variation in seasonal dengue epidemics driven by multiple interacting factors in Guangzhou, China},
author = {Rachel J. Oidtman and Shengjie Lai and Zhoujie Huang and Juan Yang and Amir S. Siraj and Robert C. Reiner and Andrew J. Tatem and T. Alex Perkins and Hongjie Yu},
url = {https://doi.org/10.1038/s41467-019-09035-x},
doi = {10.1038/s41467-019-09035-x},
issn = {2041-1723},
year  = {2019},
date = {2019-03-08},
journal = {Nature Communications},
volume = {10},
number = {1},
pages = {1148},
abstract = {Vector-borne diseases display wide inter-annual variation in seasonal epidemic size due to their complex dependence on temporally variable environmental conditions and other factors. In 2014, Guangzhou, China experienced its worst dengue epidemic on record, with incidence exceeding the historical average by two orders of magnitude. To disentangle contributions from multiple factors to inter-annual variation in epidemic size, we fitted a semi-mechanistic model to time series data from 2005--2015 and performed a series of factorial simulation experiments in which seasonal epidemics were simulated under all combinations of year-specific patterns of four time-varying factors: imported cases, mosquito density, temperature, and residual variation in local conditions not explicitly represented in the model. Our results indicate that while epidemics in most years were limited by unfavorable conditions with respect to one or more factors, the epidemic in 2014 was made possible by the combination of favorable conditions for all factors considered in our analysis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1093/jtm/taz019,
title = {Measuring mobility, disease connectivity and individual risk: a review of using mobile phone data and mHealth for travel medicine},
author = {Shengjie Lai and Andrea Farnham and Nick W Ruktanonchai and Andrew J Tatem},
url = {https://doi.org/10.1093/jtm/taz019},
doi = {10.1093/jtm/taz019},
issn = {1708-8305},
year  = {2019},
date = {2019-01-01},
journal = {Journal of Travel Medicine},
volume = {26},
number = {3},
abstract = {The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data.Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel.Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin–destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.},
note = {taz019},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{su11071943,
title = {Measurement of Social Networks for Innovation within Community Disaster Resilience},
author = {Joanna Wilkin and Eloise Biggs and Andrew J Tatem},
url = {https://www.mdpi.com/2071-1050/11/7/1943},
doi = {10.3390/su11071943},
issn = {2071-1050},
year  = {2019},
date = {2019-01-01},
journal = {Sustainability},
volume = {11},
number = {7},
abstract = {Disaster risk reduction (DRR) research has long recognised that social networks are a vital source of support during and after a shock. However, the quantification of this social support, primarily through its recognition as social capital, has proven problematic as there is no singular method for its measurement, invalidating the credibility of studies that try to correlate its effects with community disaster resilience. Within the wider resilience field, research that specifically utilises social networks as the focus of analysis is evolving. This paper provides a critical synthesis of how this developing discourse is filtering into community disaster resilience, reviewing empirical case studies from the Global South within DRR that use social network analysis and connectivity measurement. Our analysis of these studies indicates that a robust methodology utilising social network analysis is emerging, which offers opportunity for research cross-comparability. Our review also finds that without this bottom-up mapping, the implementation of top-down preparedness policy and procedures are likely to fail, resulting in the advocation of social network analysis as a critical methodology in future resilience research and policy planning.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0215857,
title = {The epidemic potential of avian influenza A (H7N9) virus in humans in mainland China: A two-stage risk analysis},
author = {Xuzheng Shan and Shengjie Lai and Hongxiu Liao and Zhongjie Li and Yajia Lan and Weizhong Yang},
url = {https://doi.org/10.1371/journal.pone.0215857},
doi = {10.1371/journal.pone.0215857},
year  = {2019},
date = {2019-01-01},
journal = {PLOS ONE},
volume = {14},
number = {4},
pages = {1-14},
publisher = {Public Library of Science},
abstract = {Background From 2013 to 2017, more than one thousand avian influenza A (H7N9) confirmed cases with hundreds of deaths were reported in mainland China. To identify priorities for epidemic prevention and control, a risk assessing framework for subnational variations is needed to define the epidemic potential of A (H7N9). Methods We established a consolidated two-stage framework that outlined the potential epidemic of H7N9 in humans: The Stage 1, index-case potential, used a Boosted Regression Trees model to assess population at risk due to spillover from poultry; the Stage 2, epidemic potential, synthesized the variables upon a framework of the Index for Risk Management to measure epidemic potential based on the probability of hazards and exposure, the vulnerability and coping capacity. Results Provinces in southern and eastern China, especially Jiangsu, Zhejiang, Guangzhou, have high index-case potential of human infected with A (H7N9), while northern coastal provinces and municipalities with low morbidity, i.e. Tianjin and Liaoning, have an increasing risk of A (H7N9) infection. Provinces in central China are likely to have high potential of epidemic due to the high vulnerability and the lack of coping capacity. Conclusions This study provides a unified risk assessment of A (H7N9) to detect the two-stage heterogeneity of epidemic potential among different provinces in mainland China, allowing proactively evaluate health preparedness at subnational levels to improve surveillance, diagnostic capabilities, and health promotion.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Routledge628842,
title = {Tracking progress towards malaria elimination in China: estimates of reproduction numbers and their spatiotemporal variation},
author = {Isobel Routledge and Shengjie Lai and Katherine E Battle and Azra C Ghani and Manuel Gomez-Rodriguez and Kyle B Gustafson and Swapnil Mishra and Joshua L Proctor and Andrew J Tatem and Zhongjie Li and Samir Bhatt},
url = {https://www.biorxiv.org/content/early/2019/05/10/628842},
doi = {10.1101/628842},
year  = {2019},
date = {2019-01-01},
journal = {bioRxiv},
publisher = {Cold Spring Harbor Laboratory},
abstract = {China reported zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we jointly estimate the case reproduction number, Rc, and the number of unobserved sources of infection. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean Rc of 0.005 projected for the year 2019, locally-acquired cases are possible due to high levels of importation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{FLOYD2019100081,
title = {Exploring fine-scale human and livestock movement in western Kenya},
author = {Jessica R. Floyd and Nick W. Ruktanonchai and Nicola Wardrop and Andrew J. Tatem and Joseph Ogola and Eric M. Fèvre},
url = {https://www.sciencedirect.com/science/article/pii/S2352771418300181},
doi = {https://doi.org/10.1016/j.onehlt.2019.100081},
issn = {2352-7714},
year  = {2019},
date = {2019-01-01},
journal = {One Health},
volume = {7},
pages = {100081},
abstract = {Human and livestock mobility are key factors in the transmission of several high-burden zoonoses such as rift valley fever and trypanosomiasis, yet our knowledge of this mobility is relatively poor due to difficulty in quantifying population-level movement patterns. Significant variation in the movement patterns of individual hosts means it is necessary to capture their fine-scale mobility in order to gain useful knowledge that can be extrapolated to a population level. Here we explore how the movements of people and their ruminants, and their exposure to various types of land cover, correlate with ruminant ownership and other demographic factors which could affect individual exposure to zoonoses. The study was conducted in Busia County, western Kenya, where the population are mostly subsistence farmers operating a mixed crop/livestock farming system. We used GPS trackers to collect movement data from 26 people and their ruminants for 1 week per individual in July/August 2016, and the study was repeated at the end of the same year to compare movement patterns between the short rainy and dry seasons respectively. We found that during the dry season, people and their ruminants travelled further on trips outside of the household, and that people spent less time on swampland compared to the short rainy season. Our findings also showed that ruminant owners spent longer and travelled further on trips outside the household than non-ruminant owners, and that people and ruminants from poorer households travelled further than people from relatively wealthier households. These results indicate that some individual-level mobility may be predicted by season and by household characteristics such as ruminant ownership and household wealth, which could have practical uses for assessing individual risk of exposure to some zoonoses and for future modelling studies of zoonosis transmission in similar rural areas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1080/20964471.2019.1625151,
title = {Global spatio-temporally harmonised datasets for producing high-resolution gridded population distribution datasets},
author = {Christopher T. Lloyd and Heather Chamberlain and David Kerr and Greg Yetman and Linda Pistolesi and Forrest R. Stevens and Andrea E. Gaughan and Jeremiah J. Nieves and Graeme Hornby and Kytt MacManus and Parmanand Sinha and Maksym Bondarenko and Alessandro Sorichetta and Andrew J. Tatem},
url = {https://doi.org/10.1080/20964471.2019.1625151},
doi = {10.1080/20964471.2019.1625151},
year  = {2019},
date = {2019-01-01},
journal = {Big Earth Data},
volume = {3},
number = {2},
pages = {108-139},
publisher = {Taylor & Francis},
note = {PMID: 31565697},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{essd-11-1385-2019,
title = {The spatial allocation of population: a review of large-scale gridded 
population data products and their fitness for use},
author = {S. Leyk and A. E. Gaughan and S. B. Adamo and A. Sherbinin and D. Balk and S. Freire and A. Rose and F. R. Stevens and B. Blankespoor and C. Frye and J. Comenetz and A. Sorichetta and K. MacManus and L. Pistolesi and M. Levy and A. J. Tatem and M. Pesaresi},
url = {https://essd.copernicus.org/articles/11/1385/2019/},
doi = {10.5194/essd-11-1385-2019},
year  = {2019},
date = {2019-01-01},
journal = {Earth System Science Data},
volume = {11},
number = {3},
pages = {1385--1409},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{su11216056,
title = {New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products},
author = {Daniela Palacios-Lopez and Felix Bachofer and Thomas Esch and Wieke Heldens and Andreas Hirner and Mattia Marconcini and Alessandro Sorichetta and Julian Zeidler and Claudia Kuenzer and Stefan Dech and Andrew J. Tatem and Peter Reinartz},
url = {https://www.mdpi.com/2071-1050/11/21/6056},
doi = {10.3390/su11216056},
issn = {2071-1050},
year  = {2019},
date = {2019-01-01},
journal = {Sustainability},
volume = {11},
number = {21},
abstract = {In the production of gridded population maps, remotely sensed, human settlement datasets rank among the most important geographical factors to estimate population densities and distributions at regional and global scales. Within this context, the German Aerospace Centre (DLR) has developed a new suite of global layers, which accurately describe the built-up environment and its characteristics at high spatial resolution: (i) the World Settlement Footprint 2015 layer (WSF-2015), a binary settlement mask; and (ii) the experimental World Settlement Footprint Density 2015 layer (WSF-2015-Density), representing the percentage of impervious surface. This research systematically compares the effectiveness of both layers for producing population distribution maps through a dasymetric mapping approach in nine low-, middle-, and highly urbanised countries. Results indicate that the WSF-2015-Density layer can produce population distribution maps with higher qualitative and quantitative accuracies in comparison to the already established binary approach, especially in those countries where a good percentage of building structures have been identified within the rural areas. Moreover, our results suggest that population distribution accuracies could substantially improve through the dynamic preselection of the input layers and the correct parameterisation of the Settlement Size Complexity (SSC) index.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1177/0962280218797362,
title = {A spatial regression model for the disaggregation of areal unit based data to high-resolution grids with application to vaccination coverage mapping},
author = {CE Utazi and J Thorley and VA Alegana and MJ Ferrari and K Nilsen and S Takahashi and CJE Metcalf and J Lessler and AJ Tatem},
url = {https://doi.org/10.1177/0962280218797362},
doi = {10.1177/0962280218797362},
year  = {2019},
date = {2019-01-01},
journal = {Statistical Methods in Medical Research},
volume = {28},
number = {10-11},
pages = {3226-3241},
abstract = {The growing demand for spatially detailed data to advance the Sustainable Development Goals agenda of ‘leaving no one behind’ has resulted in a shift in focus from aggregate national and province-based metrics to small areas and high-resolution grids in the health and development arena. Vaccination coverage is customarily measured through aggregate-level statistics, which mask fine-scale heterogeneities and ‘coldspots’ of low coverage. This paper develops a methodology for high-resolution mapping of vaccination coverage using areal data in settings where point-referenced survey data are inaccessible. The proposed methodology is a binomial spatial regression model with a logit link and a combination of covariate data and random effects modelling two levels of spatial autocorrelation in the linear predictor. The principal aspect of the model is the melding of the misaligned areal data and the prediction grid points using the regression component and each of the conditional autoregressive and the Gaussian spatial process random effects. The Bayesian model is fitted using the INLA-SPDE approach. We demonstrate the predictive ability of the model using simulated data sets. The results obtained indicate a good predictive performance by the model, with correlations of between 0.66 and 0.98 obtained at the grid level between true and predicted values. The methodology is applied to predicting the coverage of measles and diphtheria-tetanus-pertussis vaccinations at 5 × 5 km2 in Afghanistan and Pakistan using subnational Demographic and Health Surveys data. The predicted maps are used to highlight vaccination coldspots and assess progress towards coverage targets to facilitate the implementation of more geographically precise interventions. The proposed methodology can be readily applied to wider disaggregation problems in related contexts, including mapping other health and development indicators.},
note = {PMID: 30229698},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Ruktanonchai2018,
title = {Temporal trends in spatial inequalities of maternal and newborn health services among four east African countries, 1999--2015},
author = {Corrine W. Ruktanonchai and Kristine Nilsen and Victor A. Alegana and Claudio Bosco and Rogers Ayiko and Andrew C. Seven Kajeguka and Zöe Matthews and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12889-018-6241-8},
doi = {10.1186/s12889-018-6241-8},
issn = {1471-2458},
year  = {2018},
date = {2018-12-04},
journal = {BMC Public Health},
volume = {18},
number = {1},
pages = {1339},
abstract = {Sub-Saharan Africa continues to account for the highest regional maternal mortality ratio (MMR) in the world, at just under 550 maternal deaths per 100,000 live births in 2015, compared to a global rate of 216 deaths. Spatial inequalities in access to life-saving maternal and newborn health (MNH) services persist within sub-Saharan Africa, however, with varied improvement over the past two decades. While previous research within the East African Community (EAC) region has examined utilisation of MNH care as an emergent property of geographic accessibility, no research has examined how these spatial inequalities have evolved over time at similar spatial scales.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Alegana2018,
title = {National and sub-national variation in patterns of febrile case management in sub-Saharan Africa},
author = {Victor A. Alegana and Joseph Maina and Paul O. Ouma and Peter M. Macharia and Jim Wright and Peter M. Atkinson and Emelda A. Okiro and Robert W. Snow and Andrew J. Tatem},
url = {https://doi.org/10.1038/s41467-018-07536-9},
doi = {10.1038/s41467-018-07536-9},
issn = {2041-1723},
year  = {2018},
date = {2018-11-26},
journal = {Nature Communications},
volume = {9},
number = {1},
pages = {4994},
abstract = {Given national healthcare coverage gaps, understanding treatment-seeking behaviour for fever is crucial for the management of childhood illness and to reduce deaths. Here, we conduct a modelling study triangulating household survey data for fever in children under the age of five years with georeferenced public health facility databases (nthinspace=thinspace86,442 facilities) in 29 countries across sub-Saharan Africa, to estimate the probability of seeking treatment for fever at public facilities. A Bayesian item response theory framework is used to estimate this probability based on reported fever episodes, treatment choice, residence, and estimated travel-time to the nearest public-sector health facility. Findings show inter- and intra-country variation, with the likelihood of seeking treatment for fever less than 50% in 16 countries. Results highlight the need to invest in public healthcare and related databases. The variation in public sector use illustrates the need to include such modelling in future infectious disease burden estimation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2018,
title = {Innovation to impact in spatial epidemiology},
author = {Andrew J. Tatem},
url = {https://doi.org/10.1186/s12916-018-1205-5},
doi = {10.1186/s12916-018-1205-5},
issn = {1741-7015},
year  = {2018},
date = {2018-11-14},
journal = {BMC Medicine},
volume = {16},
number = {1},
pages = {209},
abstract = {Spatial epidemiology is a rapidly advancing field, pushing our abilities to measure, monitor and map pathogens at increasingly finer spatiotemporal scales. However, these scales often do not align with the abilities of control programmes to act at them, building a disconnect between academia and implementation. Efforts are being made to feed innovations into government, build spatial data skills, and strengthen links between disease control programmes and universities, yet work remains to be done if goals for disease control, elimination and `leaving no one behind' are to be met.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Bharti2018,
title = {Fluctuations in anthropogenic nighttime lights from satellite imagery for five cities in Niger and Nigeria},
author = {Nita Bharti and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2018.256},
doi = {10.1038/sdata.2018.256},
issn = {2052-4463},
year  = {2018},
date = {2018-11-13},
journal = {Scientific Data},
volume = {5},
number = {1},
pages = {180256},
abstract = {Dynamic measures of human populations are critical for global health management but are often overlooked, largely because they are difficult to quantify. Measuring human population dynamics can be prohibitively expensive in under-resourced communities. Satellite imagery can provide measurements of human populations, past and present, to complement public health analyses and interventions. We used anthropogenic illumination from publicly accessible, serial satellite nighttime images as a quantifiable proxy for seasonal population variation in five urban areas in Niger and Nigeria. We identified population fluxes as the mechanistic driver of regional seasonal measles outbreaks. Our data showed 1) urban illumination fluctuated seasonally, 2) corresponding population fluctuations were sufficient to drive seasonal measles outbreaks, and 3) overlooking these fluctuations during vaccination activities resulted in below-target coverage levels, incapable of halting transmission of the virus. We designed immunization solutions capable of achieving above-target coverage of both resident and mobile populations. Here, we provide detailed data on brightness from 2000--2005 for 5 cities in Niger and Nigeria and detailed methodology for application to other populations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{James2018,
title = {Gridded birth and pregnancy datasets for Africa, Latin America and the Caribbean},
author = {W. H. M. James and N. Tejedor-Garavito and S. E. Hanspal and A. Campbell-Sutton and G. M. Hornby and C. Pezzulo and K. Nilsen and A. Sorichetta and C. W. Ruktanonchai and A. Carioli and D. Kerr and Z. Matthews and A. J. Tatem},
url = {https://doi.org/10.1038/sdata.2018.90},
doi = {10.1038/sdata.2018.90},
issn = {2052-4463},
year  = {2018},
date = {2018-05-22},
journal = {Scientific Data},
volume = {5},
number = {1},
pages = {180090},
abstract = {Understanding the fine scale spatial distribution of births and pregnancies is crucial for informing planning decisions related to public health. This is especially important in lower income countries where infectious disease is a major concern for pregnant women and new-borns, as highlighted by the recent Zika virus epidemic. Despite this, the spatial detail of basic data on the numbers and distribution of births and pregnancies is often of a coarse resolution and difficult to obtain, with no co-ordination between countries and organisations to create one consistent set of subnational estimates. To begin to address this issue, under the framework of the WorldPop program, an open access archive of high resolution gridded birth and pregnancy distribution datasets for all African, Latin America and Caribbean countries has been created. Datasets were produced using the most recent and finest level census and official population estimate data available and are at a resolution of 30 arc seconds (approximately 1thinspacekm at the equator). All products are available through WorldPop.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Siraj2018,
title = {Spatiotemporal incidence of Zika and associated environmental drivers for the 2015-2016 epidemic in Colombia},
author = {Amir S. Siraj and Isabel Rodriguez-Barraquer and Christopher M. Barker and Natalia Tejedor-Garavito and Dennis Harding and Christopher Lorton and Dejan Lukacevic and Gene Oates and Guido Espana and Moritz U. G. Kraemer and Carrie Manore and Michael A. Johansson and Andrew J. Tatem and Robert C. Reiner and T. Alex Perkins},
url = {https://doi.org/10.1038/sdata.2018.73},
doi = {10.1038/sdata.2018.73},
issn = {2052-4463},
year  = {2018},
date = {2018-04-24},
journal = {Scientific Data},
volume = {5},
number = {1},
pages = {180073},
abstract = {Despite a long history of mosquito-borne virus epidemics in the Americas, the impact of the Zika virus (ZIKV) epidemic of 2015--2016 was unexpected. The need for scientifically informed decision-making is driving research to understand the emergence and spread of ZIKV. To support that research, we assembled a data set of key covariates for modeling ZIKV transmission dynamics in Colombia, where ZIKV transmission was widespread and the government made incidence data publically available. On a weekly basis between January 1, 2014 and October 1, 2016 at three administrative levels, we collated spatiotemporal Zika incidence data, nine environmental variables, and demographic data into a single downloadable database. These new datasets and those we identified, processed, and assembled at comparable spatial and temporal resolutions will save future researchers considerable time and effort in performing these data processing steps, enabling them to focus instead on extracting epidemiological insights from this important data set. Similar approaches could prove useful for filling data gaps to enable epidemiological analyses of future disease emergence events.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Strano2018,
title = {Mapping road network communities for guiding disease surveillance and control strategies},
author = {Emanuele Strano and Matheus P. Viana and Alessandro Sorichetta and Andrew J. Tatem},
url = {https://doi.org/10.1038/s41598-018-22969-4},
doi = {10.1038/s41598-018-22969-4},
issn = {2045-2322},
year  = {2018},
date = {2018-03-16},
journal = {Scientific Reports},
volume = {8},
number = {1},
pages = {4744},
abstract = {Human mobility is increasing in its volume, speed and reach, leading to the movement and introduction of pathogens through infected travelers. An understanding of how areas are connected, the strength of these connections and how this translates into disease spread is valuable for planning surveillance and designing control and elimination strategies. While analyses have been undertaken to identify and map connectivity in global air, shipping and migration networks, such analyses have yet to be undertaken on the road networks that carry the vast majority of travellers in low and middle income settings. Here we present methods for identifying road connectivity communities, as well as mapping bridge areas between communities and key linkage routes. We apply these to Africa, and show how many highly-connected communities straddle national borders and when integrating malaria prevalence and population data as an example, the communities change, highlighting regions most strongly connected to areas of high burden. The approaches and results presented provide a flexible tool for supporting the design of disease surveillance and control strategies through mapping areas of high connectivity that form coherent units of intervention and key link routes between communities for targeting surveillance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{JOCHEM2018104,
title = {Identifying residential neighbourhood types from settlement points in a machine learning approach},
author = {Warren C. Jochem and Tomas J. Bird and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S0198971517304210},
doi = {https://doi.org/10.1016/j.compenvurbsys.2018.01.004},
issn = {0198-9715},
year  = {2018},
date = {2018-01-01},
journal = {Computers, Environment and Urban Systems},
volume = {69},
pages = {104-113},
abstract = {Remote sensing techniques are now commonly applied to map and monitor urban land uses to measure growth and to assist with development and planning. Recent work in this area has highlighted the use of textures and other spatial features that can be measured in very high spatial resolution imagery. Far less attention has been given to using geospatial vector data (i.e. points, lines, polygons) to map land uses. This paper presents an approach to distinguish residential settlement types (regular vs. irregular) using an existing database of settlement points locating structures. Nine data features describing the density, distance, angles, and spacing of the settlement points are calculated at multiple spatial scales. These data are analysed alone and with five common remote sensing measures on elevation, slope, vegetation, and nighttime lights in a supervised machine learning approach to classify land use areas. The method was tested in seven provinces of Afghanistan (Balkh, Helmand, Herat, Kabul, Kandahar, Kunduz, Nangarhar). Overall accuracy ranged from 78% in Kandahar to 90% in Nangarhar. This research demonstrates the potential to accurately map land uses from even the simplest representation of structures.},
keywords = {Big data, Land use, Machine learning, Point pattern analysis, Texture, Urban morphology},
pubstate = {published},
tppubtype = {article}
}
@article{UTAZI20181583,
title = {High resolution age-structured mapping of childhood vaccination coverage in low and middle income countries},
author = {C. Edson Utazi and Julia Thorley and Victor A. Alegana and Matthew J. Ferrari and Saki Takahashi and C. Jessica E. Metcalf and Justin Lessler and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S0264410X18301944},
doi = {https://doi.org/10.1016/j.vaccine.2018.02.020},
issn = {0264-410X},
year  = {2018},
date = {2018-01-01},
journal = {Vaccine},
volume = {36},
number = {12},
pages = {1583-1591},
abstract = {Background 
The expansion of childhood vaccination programs in low and middle income countries has been a substantial public health success story. Indicators of the performance of intervention programmes such as coverage levels and numbers covered are typically measured through national statistics or at the scale of large regions due to survey design, administrative convenience or operational limitations. These mask heterogeneities and ‘coldspots’ of low coverage that may allow diseases to persist, even if overall coverage is high. Hence, to decrease inequities and accelerate progress towards disease elimination goals, fine-scale variation in coverage should be better characterized. 
Methods 
Using measles as an example, cluster-level Demographic and Health Surveys (DHS) data were used to map vaccination coverage at 1 km spatial resolution in Cambodia, Mozambique and Nigeria for varying age-group categories of children under five years, using Bayesian geostatistical techniques built on a suite of publicly available geospatial covariates and implemented via Markov Chain Monte Carlo (MCMC) methods. 
Results 
Measles vaccination coverage was found to be strongly predicted by just 4–5 covariates in geostatistical models, with remoteness consistently selected as a key variable. The output 1 × 1 km maps revealed significant heterogeneities within the three countries that were not captured using province-level summaries. Integration with population data showed that at the time of the surveys, few districts attained the 80% coverage, that is one component of the WHO Global Vaccine Action Plan 2020 targets. 
Conclusion 
The elimination of vaccine-preventable diseases requires a strong evidence base to guide strategies and inform efficient use of limited resources. The approaches outlined here provide a route to moving beyond large area summaries of vaccination coverage that mask epidemiologically-important heterogeneities to detailed maps that capture subnational vulnerabilities. The output datasets are built on open data and methods, and in flexible format that can be aggregated to more operationally-relevant administrative unit levels.},
keywords = {Bayesian geostatistics, Coverage heterogeneities, Demographic and Health Surveys, Measles vaccine},
pubstate = {published},
tppubtype = {article}
}
@article{Buckeee000538,
title = {Productive disruption: opportunities and challenges for innovation in infectious disease surveillance},
author = {Caroline O. Buckee and Maria I E Cardenas and June Corpuz and Arpita Ghosh and Farhana Haque and Jahirul Karim and Ayesha S. Mahmud and Richard J Maude and Keitly Mensah and Nkengafac Villyen Motaze and Maria Nabaggala and Charlotte Jessica Eland Metcalf and Sedera Aurélien Mioramalala and Frank Mubiru and Corey M. Peak and Santanu Pramanik and Jean Marius Rakotondramanga and Eric Remera and Ipsita Sinha and Siv Sovannaroth and Andrew J Tatem and Win Zaw},
url = {https://gh.bmj.com/content/3/1/e000538},
doi = {10.1136/bmjgh-2017-000538},
year  = {2018},
date = {2018-01-01},
journal = {BMJ Global Health},
volume = {3},
number = {1},
publisher = {BMJ Specialist Journals},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1073/pnas.1715305115,
title = {Spatially disaggregated population estimates in the absence of national population and housing census data},
author = {N. A. Wardrop and W. C. Jochem and T. J. Bird and H. R. Chamberlain and D. Clarke and D. Kerr and L. Bengtsson and S. Juran and V. Seaman and A. J. Tatem},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1715305115},
doi = {10.1073/pnas.1715305115},
year  = {2018},
date = {2018-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {115},
number = {14},
pages = {3529-3537},
abstract = {Population numbers at local levels are fundamental data for many applications, including the delivery and planning of services, election preparation, and response to disasters. In resource-poor settings, recent and reliable demographic data at subnational scales can often be lacking. National population and housing census data can be outdated, inaccurate, or missing key groups or areas, while registry data are generally lacking or incomplete. Moreover, at local scales accurate boundary data are often limited, and high rates of migration and urban growth make existing data quickly outdated. Here we review past and ongoing work aimed at producing spatially disaggregated local-scale population estimates, and discuss how new technologies are now enabling robust and cost-effective solutions. Recent advances in the availability of detailed satellite imagery, geopositioning tools for field surveys, statistical methods, and computational power are enabling the development and application of approaches that can estimate population distributions at fine spatial scales across entire countries in the absence of census data. We outline the potential of such approaches as well as their limitations, emphasizing the political and operational hurdles for acceptance and sustainable implementation of new approaches, and the continued importance of traditional sources of national statistical data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{wesolowski_winter_tatem_qureshi_engø-monsen_buckee_cummings_metcalf_2018,
title = {Measles outbreak risk in Pakistan: exploring the potential of combining vaccination coverage and incidence data with novel data-streams to strengthen control},
author = {Amy Wesolowski and Amy Winter and Andrew J. Tatem and Taimur Qureshi and Kenth Engø-Monsen and Caroline O. Buckee and Derek A. T. Cummings and C. Jessica E. Metcalf},
doi = {10.1017/S0950268818001449},
year  = {2018},
date = {2018-01-01},
journal = {Epidemiology and Infection},
volume = {146},
number = {12},
pages = {1575–1583},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{ZAGATTI2018133,
title = {A trip to work: Estimation of origin and destination of commuting patterns in the main metropolitan regions of Haiti using CDR},
author = {Guilherme Augusto Zagatti and Miguel Gonzalez and Paolo Avner and Nancy Lozano-Gracia and Christopher J. Brooks and Maximilian Albert and Jonathan Gray and Sarah Elizabeth Antos and Priya Burci and Elisabeth Erbach-Schoenberg and Andrew J. Tatem and Erik Wetter and Linus Bengtsson},
url = {https://www.sciencedirect.com/science/article/pii/S2352728517300866},
doi = {https://doi.org/10.1016/j.deveng.2018.03.002},
issn = {2352-7285},
year  = {2018},
date = {2018-01-01},
journal = {Development Engineering},
volume = {3},
pages = {133-165},
abstract = {The rapid, unplanned urbanisation in Haiti creates a series of urban mobility challenges which can contribute to job market fragmentation and decrease the quality of life in the city. Data on population and job distributions, and on home-work commuting patterns in major urban centres are scarce. The most recent census took place in 2003 and events such as the 2010 earthquake have caused major redistributions of the population. In this data scarce context, our work takes advantage of nationwide de-identified Call Detail Records (CDR) from the main mobile operator in the country to investigate night and daytime populations densities and commuting patterns. We use a non-supervised learning algorithm to identify meaningful locations for individuals. These locations are then labelled according to a scoring criteria. The labelled locations are distributed in a grid with cells measuring 500 × 500 m in order to aggregate the individual level data and to create origin-destination matrices of weighted connections between home and work locations. The results suggest that labor markets are fragmented in Haiti. The two main urban centres, Port-au-Prince and Cap-Haïtien suffer from low employment accessibility as measured by the percentage of the population that travels beyond their identified home cluster (1 km radius) during the day. The data from the origin-destination matrices suggest that only 42 and 40 percent of the population are considered to be commuters in Port-au-Prince and Cap-Haïtien respectively.},
keywords = {Call detail records, CDR, Commuting, Non-supervised learning, Urban planning, Urbanisation},
pubstate = {published},
tppubtype = {article}
}
@article{data3030033,
title = {Gridded Population Maps Informed by Different Built Settlement Products},
author = {Fennis J. Reed and Andrea E. Gaughan and Forrest R. Stevens and Greg Yetman and Alessandro Sorichetta and Andrew J. Tatem},
url = {https://www.mdpi.com/2306-5729/3/3/33},
doi = {10.3390/data3030033},
issn = {2306-5729},
year  = {2018},
date = {2018-01-01},
journal = {Data},
volume = {3},
number = {3},
abstract = {The spatial distribution of humans on the earth is critical knowledge that informs many disciplines and is available in a spatially explicit manner through gridded population techniques. While many approaches exist to produce specialized gridded population maps, little has been done to explore how remotely sensed, built-area datasets might be used to dasymetrically constrain these estimates. This study presents the effectiveness of three different high-resolution built area datasets for producing gridded population estimates through the dasymetric disaggregation of census counts in Haiti, Malawi, Madagascar, Nepal, Rwanda, and Thailand. Modeling techniques include a binary dasymetric redistribution, a random forest with a dasymetric component, and a hybrid of the previous two. The relative merits of these approaches and the data are discussed with regards to studying human populations and related spatially explicit phenomena. Results showed that the accuracy of random forest and hybrid models was comparable in five of six countries.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pntd.0006743,
title = {Seasonal and interannual risks of dengue introduction from South-East Asia into China, 2005-2015},
author = {Shengjie Lai and Michael A. Johansson and Wenwu Yin and Nicola A. Wardrop and Willem G. Panhuis and Amy Wesolowski and Moritz U. G. Kraemer and Isaac I. Bogoch and Dylain Kain and Aidan Findlater and Marc Choisy and Zhuojie Huang and Di Mu and Yu Li and Yangni He and Qiulan Chen and Juan Yang and Kamran Khan and Andrew J. Tatem and Hongjie Yu},
url = {https://doi.org/10.1371/journal.pntd.0006743},
doi = {10.1371/journal.pntd.0006743},
year  = {2018},
date = {2018-01-01},
journal = {PLOS Neglected Tropical Diseases},
volume = {12},
number = {11},
pages = {1-16},
publisher = {Public Library of Science},
abstract = {Due to worldwide increased human mobility, air-transportation data and mathematical models have been widely used to measure risks of global dispersal of pathogens. However, the seasonal and interannual risks of pathogens importation and onward transmission from endemic countries have rarely been quantified and validated. We constructed a modelling framework, integrating air travel, epidemiological, demographical, entomological and meteorological data, to measure the seasonal probability of dengue introduction from endemic countries. This framework has been applied retrospectively to elucidate spatiotemporal patterns and increasing seasonal risk of dengue importation from South-East Asia into China via air travel in multiple populations, Chinese travelers and local residents, over a decade of 2005–15. We found that the volume of airline travelers from South-East Asia into China has quadrupled from 2005 to 2015 with Chinese travelers increased rapidly. Following the growth of air traffic, the probability of dengue importation from South-East Asia into China has increased dramatically from 2005 to 2015. This study also revealed seasonal asymmetries of transmission routes: Sri Lanka and Maldives have emerged as origins; neglected cities at central and coastal China have been increasingly vulnerable to dengue importation and onward transmission. Compared to the monthly occurrence of dengue reported in China, our model performed robustly for importation and onward transmission risk estimates. The approach and evidence could facilitate to understand and mitigate the changing seasonal threat of arbovirus from endemic regions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{OUMA2018e342,
title = {Access to emergency hospital care provided by the public sector in sub-Saharan Africa in 2015: a geocoded inventory and spatial analysis},
author = {Paul O Ouma and Joseph Maina and Pamela N Thuranira and Peter M Macharia and Victor A Alegana and Mike English and Emelda A Okiro and Robert W Snow},
url = {https://www.sciencedirect.com/science/article/pii/S2214109X17304886},
doi = {https://doi.org/10.1016/S2214-109X(17)30488-6},
issn = {2214-109X},
year  = {2018},
date = {2018-01-01},
journal = {The Lancet Global Health},
volume = {6},
number = {3},
pages = {e342-e350},
abstract = {Summary 
Background 
Timely access to emergency care can substantially reduce mortality. International benchmarks for access to emergency hospital care have been established to guide ambitions for universal health care by 2030. However, no Pan-African database of where hospitals are located exists; therefore, we aimed to complete a geocoded inventory of hospital services in Africa in relation to how populations might access these services in 2015, with focus on women of child bearing age. 
Methods 
We assembled a geocoded inventory of public hospitals across 48 countries and islands of sub-Saharan Africa, including Zanzibar, using data from various sources. We only included public hospitals with emergency services that were managed by governments at national or local levels and faith-based or non-governmental organisations. For hospital listings without geographical coordinates, we geocoded each facility using Microsoft Encarta (version 2009), Google Earth (version 7.3), Geonames, Fallingrain, OpenStreetMap, and other national digital gazetteers. We obtained estimates for total population and women of child bearing age (15–49 years) at a 1 km2 spatial resolution from the WorldPop database for 2015. Additionally, we assembled road network data from Google Map Maker Project and OpenStreetMap using ArcMap (version 10.5). We then combined the road network and the population locations to form a travel impedance surface. Subsequently, we formulated a cost distance algorithm based on the location of public hospitals and the travel impedance surface in AccessMod (version 5) to compute the proportion of populations living within a combined walking and motorised travel time of 2 h to emergency hospital services. 
Findings 
We consulted 100 databases from 48 sub-Saharan countries and islands, including Zanzibar, and identified 4908 public hospitals. 2701 hospitals had either full or partial information about their geographical coordinates. We estimated that 287 282 013 (29·0%) people and 64 495 526 (28·2%) women of child bearing age are located more than 2-h travel time from the nearest hospital. Marked differences were observed within and between countries, ranging from less than 25% of the population within 2-h travel time of a public hospital in South Sudan to more than 90% in Nigeria, Kenya, Cape Verde, Swaziland, South Africa, Burundi, Comoros, São Tomé and Príncipe, and Zanzibar. Only 16 countries reached the international benchmark of more than 80% of their populations living within a 2-h travel time of the nearest hospital. 
Interpretation 
Physical access to emergency hospital care provided by the public sector in Africa remains poor and varies substantially within and between countries. Innovative targeting of emergency care services is necessary to reduce these inequities. This study provides the first spatial census of public hospital services in Africa. 
Funding 
Wellcome Trust and the UK Department for International Development.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Utazie000611,
title = {Geographic coverage of demographic surveillance systems for characterising the drivers of childhood mortality in sub-Saharan Africa},
author = {C Edson Utazi and Sujit K Sahu and Peter M Atkinson and Natalia Tejedor-Garavito and Christopher T Lloyd and Andrew J Tatem},
url = {https://gh.bmj.com/content/3/2/e000611},
doi = {10.1136/bmjgh-2017-000611},
year  = {2018},
date = {2018-01-01},
journal = {BMJ Global Health},
volume = {3},
number = {2},
publisher = {BMJ Specialist Journals},
abstract = {A major focus of international health and development goals is the reduction of mortality rates in children under 5 years of age. Achieving this requires understanding the drivers of mortality and how they vary geographically to facilitate the targeting and prioritisation of appropriate interventions. Much of our knowledge on the causes of, and trends in, childhood mortality come from longitudinal demographic surveillance sites, with a renewed focus recently on the establishment and growth of networks of sites from which standardised outputs can facilitate broader understanding of processes. To ensure that the collective outputs from surveillance sites can be used to derive a comprehensive understanding and monitoring system for driving policy on tackling childhood mortality, confidence is needed that existing and planned networks of sites are providing a reliable and representative picture of the geographical variation in factors associated with mortality. Here, we assembled subnational data on childhood mortality as well as key factors known to be associated with it from household surveys in 27 sub-Saharan African countries. We then mapped the locations of existing longitudinal demographic surveillance sites to assess the extent of current coverage of the range of factors, identifying where gaps exist. The results highlight regions with unique combinations of factors associated with childhood mortality that are poorly represented by the current distribution of sites, such as southern Mali, central Nigeria and southern Zambia. Finally, we determined where the establishment of new surveillance systems could improve coverage.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Jurane000875,
title = {Geospatial mapping of access to timely essential surgery in sub-Saharan Africa},
author = {Sabrina Juran and P. Niclas Broer and Stefanie J. Klug and Rachel C. Snow and Emelda A. Okiro and Paul O. Ouma and Robert W. Snow and Andrew J. Tatem and John G. Meara and Victor A. Alegana},
url = {https://gh.bmj.com/content/3/4/e000875},
doi = {10.1136/bmjgh-2018-000875},
year  = {2018},
date = {2018-01-01},
journal = {BMJ Global Health},
volume = {3},
number = {4},
publisher = {BMJ Specialist Journals},
abstract = {Introduction Despite an estimated one-third of the global burden of disease being surgical, only limited estimates of accessibility to surgical treatment in sub-Saharan Africa exist and these remain spatially undefined. Geographical metrics of access to major hospitals were estimated based on travel time. Estimates were then used to assess need for surgery at country level.Methods Major district and regional hospitals were assumed to have capability to perform bellwether procedures. Geographical locations of hospitals in relation to the population in the 47 sub-Saharan countries were combined with spatial ancillary data on roads, elevation, land use or land cover to estimate travel-time metrics of 30 min, 1 hour and 2 hours. Hospital catchment was defined as population residing in areas less than 2 hours of travel time to the next major hospital. Travel-time metrics were combined with fine-scale population maps to define burden of surgery at hospital catchment level.Results Overall, the majority of the population (92.5%) in sub-Saharan Africa reside in areas within 2 hours of a major hospital catchment defined based on spatially defined travel times. The burden of surgery in all-age population was 257.8 million to 294.7 million people and was highest in high-population density countries and lowest in sparsely populated or smaller countries. The estimated burden in children &lt;15 years was 115.3 million to 131.8 million and had similar spatial distribution to the all-age pattern.Conclusion The study provides an assessment of accessibility and burden of surgical disease in sub-Saharan Africa. Yet given the optimistic assumption of adequare surgical capability of major hospitals, the true burden of surgical disease is expected to be much greater. In-depth health facility assessments are needed to define infrastructure, personnel and medicine supply for delivering timely and safe affordable surgery to further inform the analysis.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{WEBER2018786,
title = {Census-independent population mapping in northern Nigeria},
author = {Eric M. Weber and Vincent Y. Seaman and Robert N. Stewart and Tomas J. Bird and Andrew J. Tatem and Jacob J. McKee and Budhendra L. Bhaduri and Jessica J. Moehl and Andrew E. Reith},
url = {https://www.sciencedirect.com/science/article/pii/S0034425717304364},
doi = {https://doi.org/10.1016/j.rse.2017.09.024},
issn = {0034-4257},
year  = {2018},
date = {2018-01-01},
journal = {Remote Sensing of Environment},
volume = {204},
pages = {786-798},
abstract = {Although remote sensing has long been used to aid in the estimation of population, it has usually been in the context of spatial disaggregation of national census data, with the census counts serving both as observational data for specifying models and as constraints on model outputs. Here we present a framework for estimating populations from the bottom up, entirely independently of national census data, a critical need in areas without recent and reliable census data. To make observations of population density, we replace national census data with a microcensus, in which we enumerate population for a sample of small areas within the states of Kano and Kaduna in northern Nigeria. Using supervised texture-based classifiers with very high resolution satellite imagery, we produce a binary map of human settlement at 8-meter resolution across the two states and then a more refined classification consisting of 7 residential types and 1 non-residential type. Using the residential types and a model linking them to the population density observations, we produce population estimates across the two states in a gridded raster format, at approximately 90-meter resolution. We also demonstrate a simulation framework for capturing uncertainty and presenting estimates as prediction intervals for any region of interest of any size and composition within the study region. Used in concert with previously published demographic estimates, our population estimates allowed for predictions of the population under 5 in ten administrative wards that fit strongly with reference data collected during polio vaccination campaigns.},
keywords = {Demographics, Nigeria, Polio, Population, Settlement mapping},
pubstate = {published},
tppubtype = {article}
}
@article{Wesolowski2017,
title = {Multinational patterns of seasonal asymmetry in human movement influence infectious disease dynamics},
author = {Amy Wesolowski and Elisabeth Erbach-Schoenberg and Andrew J. Tatem and Christopher Lourenço and Cecile Viboud and Vivek Charu and Nathan Eagle and Kenth Engø-Monsen and Taimur Qureshi and Caroline O. Buckee and C. J. E. Metcalf},
url = {https://doi.org/10.1038/s41467-017-02064-4},
doi = {10.1038/s41467-017-02064-4},
issn = {2041-1723},
year  = {2017},
date = {2017-12-12},
journal = {Nature Communications},
volume = {8},
number = {1},
pages = {2069},
abstract = {Seasonal variation in human mobility is globally ubiquitous and affects the spatial spread of infectious diseases, but the ability to measure seasonality in human movement has been limited by data availability. Here, we use mobile phone data to quantify seasonal travel and directional asymmetries in Kenya, Namibia, and Pakistan, across a spectrum from rural nomadic populations to highly urbanized communities. We then model how the geographic spread of several acute pathogens with varying life histories could depend on country-wide connectivity fluctuations through the year. In all three countries, major national holidays are associated with shifts in the scope of travel. Within this broader pattern, the relative importance of particular routes also fluctuates over the course of the year, with increased travel from rural to urban communities after national holidays, for example. These changes in travel impact how fast communities are likely to be reached by an introduced pathogen.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tejedor‐Garavito2017,
title = {Travel patterns and demographic characteristics of malaria cases in Swaziland, 2010--2014},
author = {Natalia Tejedor‐Garavito and Nomcebo Dlamini and Deepa Pindolia and Adam Soble and Nick W. Ruktanonchai and Victor Alegana and Arnaud Le Menach and Nyasatu Ntshalintshali and Bongani Dlamini and David L. Smith and Andrew J. Tatem and Simon Kunene},
url = {https://doi.org/10.1186/s12936-017-2004-8},
doi = {10.1186/s12936-017-2004-8},
issn = {1475-2875},
year  = {2017},
date = {2017-09-08},
journal = {Malaria Journal},
volume = {16},
number = {1},
pages = {359},
abstract = {As Swaziland progresses towards national malaria elimination, the importation of parasites into receptive areas becomes increasingly important. Imported infections have the potential to instigate local transmission and sustain local parasite reservoirs.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Pezzulo2017,
title = {Sub-national mapping of population pyramids and dependency ratios in Africa and Asia},
author = {Carla Pezzulo and Graeme M. Hornby and Alessandro Sorichetta and Andrea E. Gaughan and Catherine Linard and Tomas J. Bird and David Kerr and Christopher T. Lloyd and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2017.89},
doi = {10.1038/sdata.2017.89},
issn = {2052-4463},
year  = {2017},
date = {2017-07-19},
journal = {Scientific Data},
volume = {4},
number = {1},
pages = {170089},
abstract = {The age group composition of populations varies substantially across continents and within countries, and is linked to levels of development, health status and poverty. The subnational variability in the shape of the population pyramid as well as the respective dependency ratio are reflective of the different levels of development of a country and are drivers for a country's economic prospects and health burdens. Whether measured as the ratio between those of working age and those young and old who are dependent upon them, or through separate young and old-age metrics, dependency ratios are often highly heterogeneous between and within countries. Assessments of subnational dependency ratio and age structure patterns have been undertaken for specific countries and across high income regions, but to a lesser extent across the low income regions. In the framework of the WorldPop Project, through the assembly of over 100 million records across 6,389 subnational administrative units, subnational dependency ratio and high resolution gridded age/sex group datasets were produced for 87 countries in Africa and Asia.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Thomson2017,
title = {GridSample: an R package to generate household survey primary sampling units (PSUs) from gridded population data},
author = {Dana R. Thomson and Forrest R. Stevens and Nick W. Ruktanonchai and Andrew J. Tatem and Marcia C. Castro},
url = {https://doi.org/10.1186/s12942-017-0098-4},
doi = {10.1186/s12942-017-0098-4},
issn = {1476-072X},
year  = {2017},
date = {2017-07-19},
journal = {International Journal of Health Geographics},
volume = {16},
number = {1},
pages = {25},
abstract = {Household survey data are collected by governments, international organizations, and companies to prioritize policies and allocate billions of dollars. Surveys are typically selected from recent census data; however, census data are often outdated or inaccurate. This paper describes how gridded population data might instead be used as a sample frame, and introduces the R GridSample algorithm for selecting primary sampling units (PSU) for complex household surveys with gridded population data. With a gridded population dataset and geographic boundary of the study area, GridSample allows a two-step process to sample ``seed'' cells with probability proportionate to estimated population size, then ``grows'' PSUs until a minimum population is achieved in each PSU. The algorithm permits stratification and oversampling of urban or rural areas. The approximately uniform size and shape of grid cells allows for spatial oversampling, not possible in typical surveys, possibly improving small area estimates with survey results.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Takahashi2017,
title = {The geography of measles vaccination in the African Great Lakes region},
author = {Saki Takahashi and C. Jessica E. Metcalf and Matthew J. Ferrari and Andrew J. Tatem and Justin Lessler},
url = {https://doi.org/10.1038/ncomms15585},
doi = {10.1038/ncomms15585},
issn = {2041-1723},
year  = {2017},
date = {2017-05-25},
journal = {Nature Communications},
volume = {8},
number = {1},
pages = {15585},
abstract = {Expanded access to measles vaccination was among the most successful public health interventions of recent decades. All WHO regions currently target measles elimination by 2020, yet continued measles circulation makes that goal seem elusive. Using Demographic and Health Surveys with generalized additive models, we quantify spatial patterns of measles vaccination in ten contiguous countries in the African Great Lakes region between 2009--2014. Seven countries have `coldspots' where vaccine coverage is below the WHO target of 80%. Over 14 million children under 5 years of age live in coldspots across the region, and a total of 8--12 million children are unvaccinated. Spatial patterns of vaccination do not map directly onto sub-national administrative units and transnational coldspots exist. Clustering of low vaccination areas may allow for pockets of susceptibility that sustain circulation despite high overall coverage. Targeting at-risk areas and transnational coordination are likely required to eliminate measles in the region.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Takahashi2017b,
title = {The geography of measles vaccination in the African Great Lakes region},
author = {Saki Takahashi and C. Jessica E. Metcalf and Matthew J. Ferrari and Andrew J. Tatem and Justin Lessler},
url = {https://doi.org/10.1038/ncomms15585},
doi = {10.1038/ncomms15585},
issn = {2041-1723},
year  = {2017},
date = {2017-05-25},
journal = {Nature Communications},
volume = {8},
number = {1},
pages = {15585},
abstract = {Expanded access to measles vaccination was among the most successful public health interventions of recent decades. All WHO regions currently target measles elimination by 2020, yet continued measles circulation makes that goal seem elusive. Using Demographic and Health Surveys with generalized additive models, we quantify spatial patterns of measles vaccination in ten contiguous countries in the African Great Lakes region between 2009--2014. Seven countries have `coldspots' where vaccine coverage is below the WHO target of 80%. Over 14 million children under 5 years of age live in coldspots across the region, and a total of 8--12 million children are unvaccinated. Spatial patterns of vaccination do not map directly onto sub-national administrative units and transnational coldspots exist. Clustering of low vaccination areas may allow for pockets of susceptibility that sustain circulation despite high overall coverage. Targeting at-risk areas and transnational coordination are likely required to eliminate measles in the region.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Alegana2017,
title = {Treatment-seeking behaviour in low- and middle-income countries estimated using a Bayesian model},
author = {Victor A. Alegana and Jim Wright and Carla Pezzulo and Andrew J. Tatem and Peter M. Atkinson},
url = {https://doi.org/10.1186/s12874-017-0346-0},
doi = {10.1186/s12874-017-0346-0},
issn = {1471-2288},
year  = {2017},
date = {2017-04-20},
journal = {BMC Medical Research Methodology},
volume = {17},
number = {1},
pages = {67},
abstract = {Seeking treatment in formal healthcare for uncomplicated infections is vital to combating disease in low- and middle-income countries (LMICs). Healthcare treatment-seeking behaviour varies within and between communities and is modified by socio-economic, demographic, and physical factors. As a result, it remains a challenge to quantify healthcare treatment-seeking behaviour using a metric that is comparable across communities. Here, we present an application for transforming individual categorical responses (actions related to fever) to a continuous probabilistic estimate of fever treatment for one country in Sub-Saharan Africa (SSA).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Dudas2017,
title = {Virus genomes reveal factors that spread and sustained the Ebola epidemic},
author = {Gytis Dudas and Luiz Max Carvalho and Trevor Bedford and Andrew J. Tatem and Guy Baele and Nuno R. Faria and Daniel J. Park and Jason T. Ladner and Armando Arias and Danny Asogun and Filip Bielejec and Sarah L. Caddy and Matthew Cotten and Jonathan DÁmbrozio and Simon Dellicour and Antonino Di Caro and Joseph W. Diclaro and Sophie Duraffour and Michael J. Elmore and Lawrence S. Fakoli and Ousmane Faye and Merle L. Gilbert and Sahr M. Gevao and Stephen Gire and Adrianne Gladden-Young and Andreas Gnirke and Augustine Goba and Donald S. Grant and Bart L. Haagmans and Julian A. Hiscox and Umaru Jah and Jeffrey R. Kugelman and Di Liu and Jia Lu and Christine M. Malboeuf and Suzanne Mate and David A. Matthews and Christian B. Matranga and Luke W. Meredith and James Qu and Joshua Quick and Suzan D. Pas and My V. T. Phan and Georgios Pollakis and Chantal B. Reusken and Mariano Sanchez-Lockhart and Stephen F. Schaffner and John S. Schieffelin and Rachel S. Sealfon and Etienne Simon-Loriere and Saskia L. Smits and Kilian Stoecker and Lucy Thorne and Ekaete Alice Tobin and Mohamed A. Vandi and Simon J. Watson and Kendra West and Shannon Whitmer and Michael R. Wiley and Sarah M. Winnicki and Shirlee Wohl and Roman Wölfel and Nathan L. Yozwiak and Kristian G. Andersen and Sylvia O. Blyden and Fatorma Bolay and Miles W. Carroll and Bernice Dahn and Boubacar Diallo and Pierre Formenty and Christophe Fraser and George F. Gao and Robert F. Garry and Ian Goodfellow and Stephan Günther and Christian T. Happi and Edward C. Holmes and Brima Kargbo and Sakoba Ke"ita and Paul Kellam and Marion P. G. Koopmans and Jens H. Kuhn and Nicholas J. Loman and N'Faly Magassouba and Dhamari Naidoo and Stuart T. Nichol and Tolbert Nyenswah and Gustavo Palacios and Oliver G. Pybus and Pardis C. Sabeti and Amadou Sall and Ute Ströher and Isatta Wurie and Marc A. Suchard and Philippe Lemey and Andrew Rambaut},
url = {https://doi.org/10.1038/nature22040},
doi = {10.1038/nature22040},
issn = {1476-4687},
year  = {2017},
date = {2017-04-01},
journal = {Nature},
volume = {544},
number = {7650},
pages = {309-315},
abstract = {The 2013--2016 West African epidemic caused by the Ebola virus was of unprecedented magnitude, duration and impact. Here we reconstruct the dispersal, proliferation and decline of Ebola virus throughout the region by analysing 1,610 Ebola virus genomes, which represent over 5% of the known cases. We test the association of geography, climate and demography with viral movement among administrative regions, inferring a classic `gravity' model, with intense dispersal between larger and closer populations. Despite attenuation of international dispersal after border closures, cross-border transmission had already sown the seeds for an international epidemic, rendering these measures ineffective at curbing the epidemic. We address why the epidemic did not spread into neighbouring countries, showing that these countries were susceptible to substantial outbreaks but at lower risk of introductions. Finally, we reveal that this large epidemic was a heterogeneous and spatially dissociated collection of transmission clusters of varying size, duration and connectivity. These insights will help to inform interventions in future epidemics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2017,
title = {WorldPop, open data for spatial demography},
author = {Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2017.4},
doi = {10.1038/sdata.2017.4},
issn = {2052-4463},
year  = {2017},
date = {2017-01-31},
journal = {Scientific Data},
volume = {4},
number = {1},
pages = {170004},
abstract = {High resolution, contemporary data on human population distributions, their characteristics and changes over time are a prerequisite for the accurate measurement of the impacts of population growth, for monitoring changes and for planning interventions. WorldPop aims to meet these needs through the provision of detailed and open access spatial demographic datasets built using transparent approaches. The Scientific Data WorldPop collection brings together descriptor papers on these datasets and is introduced here.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lloyd2017,
title = {High resolution global gridded data for use in population studies},
author = {Christopher T. Lloyd and Alessandro Sorichetta and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2017.1},
doi = {10.1038/sdata.2017.1},
issn = {2052-4463},
year  = {2017},
date = {2017-01-31},
journal = {Scientific Data},
volume = {4},
number = {1},
pages = {170001},
abstract = {Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project website.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1080/17538947.2016.1275829,
title = {Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009},
author = {Catherine Linard and Caroline W. Kabaria and Marius Gilbert and Andrew J. Tatem and Andrea E. Gaughan and Forrest R. Stevens and Alessandro Sorichetta and Abdisalan M. Noor and Robert W. Snow},
url = {https://doi.org/10.1080/17538947.2016.1275829},
doi = {10.1080/17538947.2016.1275829},
year  = {2017},
date = {2017-01-01},
journal = {International Journal of Digital Earth},
volume = {10},
number = {10},
pages = {1017-1029},
publisher = {Taylor & Francis},
note = {PMID: 29098016},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rsif.2016.0690,
title = {Mapping poverty using mobile phone and satellite data},
author = {Jessica E. Steele and Pål Roe Sundsøy and Carla Pezzulo and Victor A. Alegana and Tomas J. Bird and Joshua Blumenstock and Johannes Bjelland and Kenth Engø-Monsen and Yves-Alexandre Montjoye and Asif M. Iqbal and Khandakar N. Hadiuzzaman and Xin Lu and Erik Wetter and Andrew J. Tatem and Linus Bengtsson},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2016.0690},
doi = {10.1098/rsif.2016.0690},
year  = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {127},
pages = {20160690},
abstract = {Poverty is one of the most important determinants of adverse health outcomes globally, a major cause of societal instability and one of the largest causes of lost human potential. Traditional approaches to measuring and targeting poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date. Alternate measures are needed to complement and update estimates between censuses. This study demonstrates how public and private data sources that are commonly available for LMICs can be used to provide novel insight into the spatial distribution of poverty. We evaluate the relative value of modelling three traditional poverty measures using aggregate data from mobile operators and widely available geospatial data. Taken together, models combining these data sources provide the best predictive power (highest r2 = 0.78) and lowest error, but generally models employing mobile data only yield comparable results, offering the potential to measure poverty more frequently and at finer granularity. Stratifying models into urban and rural areas highlights the advantage of using mobile data in urban areas and different data in different contexts. The findings indicate the possibility to estimate and continually monitor poverty rates at high spatial resolution in countries with limited capacity to support traditional methods of data collection.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rsif.2016.0825,
title = {Exploring the high-resolution mapping of gender-disaggregated development indicators},
author = {C. Bosco and V. Alegana and T. Bird and C. Pezzulo and L. Bengtsson and A. Sorichetta and J. Steele and G. Hornby and C. Ruktanonchai and N. Ruktanonchai and E. Wetter and A. J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2016.0825},
doi = {10.1098/rsif.2016.0825},
year  = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {129},
pages = {20160825},
abstract = {Improved understanding of geographical variation and inequity in health status, wealth and access to resources within countries is increasingly being recognized as central to meeting development goals. Development and health indicators assessed at national or subnational scale can often conceal important inequities, with the rural poor often least well represented. The ability to target limited resources is fundamental, especially in an international context where funding for health and development comes under pressure. This has recently prompted the exploration of the potential of spatial interpolation methods based on geolocated clusters from national household survey data for the high-resolution mapping of features such as population age structures, vaccination coverage and access to sanitation. It remains unclear, however, how predictable these different factors are across different settings, variables and between demographic groups. Here we test the accuracy of spatial interpolation methods in producing gender-disaggregated high-resolution maps of the rates of literacy, stunting and the use of modern contraceptive methods from a combination of geolocated demographic and health surveys cluster data and geospatial covariates. Bayesian geostatistical and machine learning modelling methods were tested across four low-income countries and varying gridded environmental and socio-economic covariate datasets to build 1×1 km spatial resolution maps with uncertainty estimates. Results show the potential of the approach in producing high-resolution maps of key gender-disaggregated socio-economic indicators, with explained variance through cross-validation being as high as 74–75% for female literacy in Nigeria and Kenya, and in the 50–70% range for many other variables. However, substantial variations by both country and variable were seen, with many variables showing poor mapping accuracies in the range of 2–30% explained variance using both geostatistical and machine learning approaches. The analyses offer a robust basis for the construction of timely maps with levels of detail that support geographically stratified decision-making and the monitoring of progress towards development goals. However, the great variability in results between countries and variables highlights the challenges in applying these interpolation methods universally across multiple countries, and the importance of validation and quantifying uncertainty if this is undertaken.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rsif.2017.0401,
title = {Examining the correlates and drivers of human population distributions across low- and middle-income countries},
author = {Jeremiah J. Nieves and Forrest R. Stevens and Andrea E. Gaughan and Catherine Linard and Alessandro Sorichetta and Graeme Hornby and Nirav N. Patel and Andrew J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2017.0401},
doi = {10.1098/rsif.2017.0401},
year  = {2017},
date = {2017-01-01},
journal = {Journal of The Royal Society Interface},
volume = {14},
number = {137},
pages = {20170401},
abstract = {Geographical factors have influenced the distributions and densities of global human population distributions for centuries. Climatic regimes have made some regions more habitable than others, harsh topography has discouraged human settlement, and transport links have encouraged population growth. A better understanding of these types of relationships enables both improved mapping of population distributions today and modelling of future scenarios. However, few comprehensive studies of the relationships between population spatial distributions and the range of drivers and correlates that exist have been undertaken at all, much less at high spatial resolutions, and particularly across the low- and middle-income countries. Here, we quantify the relative importance of multiple types of drivers and covariates in explaining observed population densities across 32 low- and middle-income countries over four continents using machine-learning approaches. We find that, while relationships between population densities and geographical factors show some variation between regions, they are generally remarkably consistent, pointing to universal drivers of human population distribution. Here, we find that a set of geographical features relating to the built environment, ecology and topography consistently explain the majority of variability in population distributions at fine spatial scales across the low- and middle-income regions of the world.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pmed.1002234,
title = {Housing Improvements and Malaria Risk in Sub-Saharan Africa: A Multi-Country Analysis of Survey Data},
author = {Lucy S. Tusting and Christian Bottomley and Harry Gibson and Immo Kleinschmidt and Andrew J. Tatem and Steve W. Lindsay and Peter W. Gething},
url = {https://doi.org/10.1371/journal.pmed.1002234},
doi = {10.1371/journal.pmed.1002234},
year  = {2017},
date = {2017-01-01},
journal = {PLOS Medicine},
volume = {14},
number = {2},
pages = {1-15},
publisher = {Public Library of Science},
abstract = {Background Improvements to housing may contribute to malaria control and elimination by reducing house entry by malaria vectors and thus exposure to biting. We tested the hypothesis that the odds of malaria infection are lower in modern, improved housing compared to traditional housing in sub-Saharan Africa (SSA). Methods and Findings We analysed 15 Demographic and Health Surveys (DHS) and 14 Malaria Indicator Surveys (MIS) conducted in 21 countries in SSA between 2008 and 2015 that measured malaria infection by microscopy or rapid diagnostic test (RDT). DHS/MIS surveys record whether houses are built with finished materials (e.g., metal) or rudimentary materials (e.g., thatch). This information was used to develop a binary housing quality variable where houses built using finished wall, roof, and floor materials were classified as “modern”, and all other houses were classified as “traditional”. Conditional logistic regression was used to determine the association between housing quality and prevalence of malaria infection in children aged 0–5 y, adjusting for age, gender, insecticide-treated net (ITN) use, indoor residual spraying, household wealth, and geographic cluster. Individual survey odds ratios (ORs) were combined to determine a summary OR using a random effects meta-analysis. Of 284,532 total children surveyed, 139,318 were tested for malaria infection using microscopy (n = 131,652) or RDT (n = 138,540). Within individual surveys, malaria prevalence measured by microscopy ranged from 0.4% (Madagascar 2011) to 45.5% (Burkina Faso 2010) among children living in modern houses and from 0.4% (The Gambia 2013) to 70.6% (Burkina Faso 2010) in traditional houses, and malaria prevalence measured by RDT ranged from 0.3% (Senegal 2013–2014) to 61.2% (Burkina Faso 2010) in modern houses and from 1.5% (The Gambia 2013) to 79.8% (Burkina Faso 2010) in traditional houses. Across all surveys, modern housing was associated with a 9% to 14% reduction in the odds of malaria infection (microscopy: adjusted OR 0.91, 95% CI 0.85–0.97},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{HEAD2017e772,
title = {Global funding trends for malaria research in sub-Saharan Africa: a systematic analysis},
author = {Michael G Head and Sian Goss and Yann Gelister and Victor Alegana and Rebecca J Brown and Stuart C Clarke and Joseph R A Fitchett and Rifat Atun and J Anthony G Scott and Marie-Louise Newell and Sabu S Padmadas and Andrew J Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S2214109X17302450},
doi = {https://doi.org/10.1016/S2214-109X(17)30245-0},
issn = {2214-109X},
year  = {2017},
date = {2017-01-01},
journal = {The Lancet Global Health},
volume = {5},
number = {8},
pages = {e772-e781},
abstract = {Summary 
Background 
Total domestic and international funding for malaria is inadequate to achieve WHO global targets in burden reduction by 2030. We describe the trends of investments in malaria-related research in sub-Saharan Africa and compare investment with national disease burden to identify areas of funding strength and potentially neglected populations. We also considered funding for malaria control. 
Methods 
Research funding data related to malaria for 1997–2013 were sourced from existing datasets, from 13 major public and philanthropic global health funders, and from funding databases. Investments (reported in US$) were considered by geographical area and compared with data on parasite prevalence and populations at risk in sub-Saharan Africa. 45 sub-Saharan African countries were ranked by amount of research funding received. 
Findings 
We found 333 research awards totalling US$814·4 million. Public health research covered $308·1 million (37·8%) and clinical trials covered $275·2 million (33·8%). Tanzania ($107·8 million [13·2%]), Uganda ($97·9 million [12·0%]), and Kenya ($92·9 million [11·4%]) received the highest sum of research investment and the most research awards. Malawi, Tanzania, and Uganda remained highly ranked after adjusting for national gross domestic product. Countries with a reasonably high malaria burden that received little research investment or funding for malaria control included Central African Republic (ranked 40th) and Sierra Leone (ranked 35th). Congo (Brazzaville) and Guinea had reasonably high malaria mortality, yet Congo (Brazzaville) ranked 38th and Guinea ranked 25th, thus receiving little investment. 
Interpretation 
Some countries receive reasonably large investments in malaria-related research (Tanzania, Kenya, Uganda), whereas others receive little or no investments (Sierra Leone, Central African Republic). Research investments are typically highest in countries where funding for malaria control is also high. Investment strategies should consider more equitable research and operational investments across countries to include currently neglected and susceptible populations. 
Funding 
Royal Society of Tropical Medicine and Hygiene and Bill & Melinda Gates Foundation.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rspb.2017.0901,
title = {Identifying climate drivers of infectious disease dynamics: recent advances and challenges ahead},
author = {C. Jessica E. Metcalf and Katharine S. Walter and Amy Wesolowski and Caroline O. Buckee and Elena Shevliakova and Andrew J. Tatem and William R. Boos and Daniel M. Weinberger and Virginia E. Pitzer},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2017.0901},
doi = {10.1098/rspb.2017.0901},
year  = {2017},
date = {2017-01-01},
journal = {Proceedings of the Royal Society B: Biological Sciences},
volume = {284},
number = {1860},
pages = {20170901},
abstract = {Climate change is likely to profoundly modulate the burden of infectious diseases. However, attributing health impacts to a changing climate requires being able to associate changes in infectious disease incidence with the potentially complex influences of climate. This aim is further complicated by nonlinear feedbacks inherent in the dynamics of many infections, driven by the processes of immunity and transmission. Here, we detail the mechanisms by which climate drivers can shape infectious disease incidence, from direct effects on vector life history to indirect effects on human susceptibility, and detail the scope of variation available with which to probe these mechanisms. We review approaches used to evaluate and quantify associations between climate and infectious disease incidence, discuss the array of data available to tackle this question, and detail remaining challenges in understanding the implications of climate change for infectious disease incidence. We point to areas where synthesis between approaches used in climate science and infectious disease biology provide potential for progress.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{https://doi.org/10.1111/tgis.12214,
title = {Improving Large Area Population Mapping Using Geotweet Densities},
author = {Nirav N. Patel and Forrest R. Stevens and Zhuojie Huang and Andrea E. Gaughan and Iqbal Elyazar and Andrew J. Tatem},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/tgis.12214},
doi = {https://doi.org/10.1111/tgis.12214},
year  = {2017},
date = {2017-01-01},
journal = {Transactions in GIS},
volume = {21},
number = {2},
pages = {317-331},
abstract = {Abstract Many different methods are used to disaggregate census data and predict population densities to construct finer scale, gridded population data sets. These methods often involve a range of high resolution geospatial covariate datasets on aspects such as urban areas, infrastructure, land cover and topography; such covariates, however, are not directly indicative of the presence of people. Here we tested the potential of geo-located tweets from the social media application, Twitter, as a covariate in the production of population maps. The density of geo-located tweets in 1x1 km grid cells over a 2-month period across Indonesia, a country with one of the highest Twitter usage rates in the world, was input as a covariate into a previously published random forests-based census disaggregation method. Comparison of internal measures of accuracy and external assessments between models built with and without the geotweets showed that increases in population mapping accuracy could be obtained using the geotweet densities as a covariate layer. The work highlights the potential for such social media-derived data in improving our understanding of population distributions and offers promise for more dynamic mapping with such data being continually produced and freely available.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{BUCKEE201710,
title = {Seasonal Population Movements and the Surveillance and Control of Infectious Diseases},
author = {Caroline O. Buckee and Andrew J. Tatem and C. Jessica E. Metcalf},
url = {https://www.sciencedirect.com/science/article/pii/S1471492216301891},
doi = {https://doi.org/10.1016/j.pt.2016.10.006},
issn = {1471-4922},
year  = {2017},
date = {2017-01-01},
journal = {Trends in Parasitology},
volume = {33},
number = {1},
pages = {10-20},
abstract = {National policies designed to control infectious diseases should allocate resources for interventions based on regional estimates of disease burden from surveillance systems. For many infectious diseases, however, there is pronounced seasonal variation in incidence. Policy-makers must routinely manage a public health response to these seasonal fluctuations with limited understanding of their underlying causes. Two complementary and poorly described drivers of seasonal disease incidence are the mobility and aggregation of human populations, which spark outbreaks and sustain transmission, respectively, and may both exhibit distinct seasonal variations. Here we highlight the key challenges that seasonal migration creates when monitoring and controlling infectious diseases. We discuss the potential of new data sources in accounting for seasonal population movements in dynamic risk mapping strategies.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{TATEM201798,
title = {The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics},
author = {Andrew J Tatem and Peng Jia and Dariya Ordanovich and Michael Falkner and Zhuojie Huang and Rosalind Howes and Simon I Hay and Peter W Gething and David L Smith},
url = {https://www.sciencedirect.com/science/article/pii/S1473309916303267},
doi = {https://doi.org/10.1016/S1473-3099(16)30326-7},
issn = {1473-3099},
year  = {2017},
date = {2017-01-01},
journal = {The Lancet Infectious Diseases},
volume = {17},
number = {1},
pages = {98-107},
abstract = {Summary 
Background 
Malaria remains a problem for many countries classified as malaria free through cases imported from endemic regions. Imported cases to non-endemic countries often result in delays in diagnosis, are expensive to treat, and can sometimes cause secondary local transmission. The movement of malaria in endemic countries has also contributed to the spread of drug resistance and threatens long-term eradication goals. Here we focused on quantifying the international movements of malaria to improve our understanding of these phenomena and facilitate the design of mitigation strategies. 
Methods 
In this meta-analysis, we studied the database of publicly available nationally reported statistics on imported malaria in the past 10 years, covering more than 50 000 individual cases. We obtained data from 40 non-endemic countries and recorded the geographical variations. 
Findings 
Infection movements were strongly skewed towards a small number of high-traffic routes between 2005 and 2015, with the west Africa region accounting for 56% (13 947/24 941) of all imported cases to non-endemic countries with a reported travel destination, and France and the UK receiving the highest number of cases, with more than 4000 reported cases per year on average. Countries strongly linked by movements of imported cases are grouped by historical, language, and travel ties. There is strong spatial clustering of plasmodium species types. 
Interpretation 
The architecture of the air network, historical ties, demographics of travellers, and malaria endemicity contribute to highly heterogeneous patterns of numbers, routes, and species compositions of parasites transported. With global malaria eradication on the international agenda, malaria control altering local transmission, and the threat of drug resistance, understanding these patterns and their drivers is increasing in importance. 
Funding 
Bill & Melinda Gates Foundation, National Institutes of Health, UK Medical Research Council, UK Department for International Development, Wellcome Trust.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{KRAEMER2017330,
title = {Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study},
author = {Moritz U G Kraemer and Nuno R Faria and Robert C Reiner and Nick Golding and Birgit Nikolay and Stephanie Stasse and Michael A Johansson and Henrik Salje and Ousmane Faye and G R William Wint and Matthias Niedrig and Freya M Shearer and Sarah C Hill and Robin N Thompson and Donal Bisanzio and Nuno Taveira and Heinrich H Nax and Bary S R Pradelski and Elaine O Nsoesie and Nicholas R Murphy and Isaac I Bogoch and Kamran Khan and John S Brownstein and Andrew J Tatem and Tulio Oliveira and David L Smith and Amadou A Sall and Oliver G Pybus and Simon I Hay and Simon Cauchemez},
url = {https://www.sciencedirect.com/science/article/pii/S1473309916305138},
doi = {https://doi.org/10.1016/S1473-3099(16)30513-8},
issn = {1473-3099},
year  = {2017},
date = {2017-01-01},
journal = {The Lancet Infectious Diseases},
volume = {17},
number = {3},
pages = {330-338},
abstract = {Summary 
Background 
Since late 2015, an epidemic of yellow fever has caused more than 7334 suspected cases in Angola and the Democratic Republic of the Congo, including 393 deaths. We sought to understand the spatial spread of this outbreak to optimise the use of the limited available vaccine stock. 
Methods 
We jointly analysed datasets describing the epidemic of yellow fever, vector suitability, human demography, and mobility in central Africa to understand and predict the spread of yellow fever virus. We used a standard logistic model to infer the district-specific yellow fever virus infection risk during the course of the epidemic in the region. 
Findings 
The early spread of yellow fever virus was characterised by fast exponential growth (doubling time of 5–7 days) and fast spatial expansion (49 districts reported cases after only 3 months) from Luanda, the capital of Angola. Early invasion was positively correlated with high population density (Pearson's r 0·52, 95% CI 0·34–0·66). The further away locations were from Luanda, the later the date of invasion (Pearson's r 0·60, 95% CI 0·52–0·66). In a Cox model, we noted that districts with higher population densities also had higher risks of sustained transmission (the hazard ratio for cases ceasing was 0·74, 95% CI 0·13–0·92 per log-unit increase in the population size of a district). A model that captured human mobility and vector suitability successfully discriminated districts with high risk of invasion from others with a lower risk (area under the curve 0·94, 95% CI 0·92–0·97). If at the start of the epidemic, sufficient vaccines had been available to target 50 out of 313 districts in the area, our model would have correctly identified 27 (84%) of the 32 districts that were eventually affected. 
Interpretation 
Our findings show the contributions of ecological and demographic factors to the ongoing spread of the yellow fever outbreak and provide estimates of the areas that could be prioritised for vaccination, although other constraints such as vaccine supply and delivery need to be accounted for before such insights can be translated into policy. 
Funding 
Wellcome Trust.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lai2016,
title = {Plasmodium falciparum malaria importation from Africa to China and its mortality: an analysis of driving factors},
author = {Shengjie Lai and Nicola A. Wardrop and Zhuojie Huang and Claudio Bosco and Junling Sun and Tomas Bird and Amy Wesolowski and Sheng Zhou and Qian Zhang and Canjun Zheng and Zhongjie Li and Andrew J. Tatem and Hongjie Yu},
url = {https://doi.org/10.1038/srep39524},
doi = {10.1038/srep39524},
issn = {2045-2322},
year  = {2016},
date = {2016-12-21},
journal = {Scientific Reports},
volume = {6},
number = {1},
pages = {39524},
abstract = {Plasmodium falciparum malaria importation from Africa to China is rising with increasing Chinese overseas investment and international travel. Identifying networks and drivers of this phenomenon as well as the contributors to high case-fatality rate is a growing public health concern to enable efficient response. From 2011--2015, 8653thinspaceP. falciparum cases leading to 98 deaths (11.3 per 1000 cases) were imported from 41 sub-Saharan countries into China, with most cases (91.3%) occurring in labour-related Chinese travellers. Four strongly connected groupings of origin African countries with destination Chinese provinces were identified, and the number of imported cases was significantly associated with the volume of air passengers to China (Pthinspace=thinspace0.006), parasite prevalence in Africa (Pthinspace&lt;thinspace0.001), and the amount of official development assistance from China (Pthinspace&lt;thinspace0.001) with investment in resource extraction having the strongest relationship with parasite importation. Risk factors for deaths from imported cases were related to the capacity of malaria diagnosis and diverse socioeconomic factors. The spatial heterogeneity uncovered, principal drivers explored, and risk factors for mortality found in the rising rates of P. falciparum malaria importation to China can serve to refine malaria elimination strategies and the management of cases, and high risk groups and regions should be targeted.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Alegana2016b,
title = {Spatio-temporal analysis of malaria vector density from baseline through intervention in a high transmission setting},
author = {Victor A. Alegana and Simon P. Kigozi and Joaniter Nankabirwa and Emmanuel Arinaitwe and Ruth Kigozi and Henry Mawejje and Maxwell Kilama and Nick W. Ruktanonchai and Corrine W. Ruktanonchai and Chris Drakeley and Steve W. Lindsay and Bryan Greenhouse and Moses R. Kamya and David L. Smith and Peter M. Atkinson and Grant Dorsey and Andrew J. Tatem},
url = {https://doi.org/10.1186/s13071-016-1917-3},
doi = {10.1186/s13071-016-1917-3},
issn = {1756-3305},
year  = {2016},
date = {2016-12-12},
journal = {Parasites & Vectors},
volume = {9},
number = {1},
pages = {637},
abstract = {An increase in effective malaria control since 2000 has contributed to a decline in global malaria morbidity and mortality. Knowing when and how existing interventions could be combined to maximise their impact on malaria vectors can provide valuable information for national malaria control programs in different malaria endemic settings. Here, we assess the effect of indoor residual spraying on malaria vector densities in a high malaria endemic setting in eastern Uganda as part of a cohort study where the use of long-lasting insecticidal nets (LLINs) was high.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{zuErbach-Schoenberg2016,
title = {Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates},
author = {Elisabeth Erbach-Schoenberg and Victor A. Alegana and Alessandro Sorichetta and Catherine Linard and Christoper Lourenço and Nick W. Ruktanonchai and Bonita Graupe and Tomas J. Bird and Carla Pezzulo and Amy Wesolowski and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12963-016-0106-0},
doi = {10.1186/s12963-016-0106-0},
issn = {1478-7954},
year  = {2016},
date = {2016-10-12},
journal = {Population Health Metrics},
volume = {14},
number = {1},
pages = {35},
abstract = {Reliable health metrics are crucial for accurately assessing disease burden and planning interventions. Many health indicators are measured through passive surveillance systems and are reliant on accurate estimates of denominators to transform case counts into incidence measures. These denominator estimates generally come from national censuses and use large area growth rates to estimate annual changes. Typically, they do not account for any seasonal fluctuations and thus assume a static denominator population. Many recent studies have highlighted the dynamic nature of human populations through quantitative analyses of mobile phone call data records and a range of other sources, emphasizing seasonal changes. In this study, we use mobile phone data to capture patterns of short-term human population movement and to map dynamism in population densities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Bharti2016,
title = {Measuring populations to improve vaccination coverage},
author = {Nita Bharti and Ali Djibo and Andrew J. Tatem and Bryan T. Grenfell and Matthew J. Ferrari},
url = {https://doi.org/10.1038/srep34541},
doi = {10.1038/srep34541},
issn = {2045-2322},
year  = {2016},
date = {2016-10-05},
journal = {Scientific Reports},
volume = {6},
number = {1},
pages = {34541},
abstract = {In low-income settings, vaccination campaigns supplement routine immunization but often fail to achieve coverage goals due to uncertainty about target population size and distribution. Accurate, updated estimates of target populations are rare but critical; short-term fluctuations can greatly impact population size and susceptibility. We use satellite imagery to quantify population fluctuations and the coverage achieved by a measles outbreak response vaccination campaign in urban Niger and compare campaign estimates to measurements from a post-campaign survey. Vaccine coverage was overestimated because the campaign underestimated resident numbers and seasonal migration further increased the target population. We combine satellite-derived measurements of fluctuations in population distribution with high-resolution measles case reports to develop a dynamic model that illustrates the potential improvement in vaccination campaign coverage if planners account for predictable population fluctuations. Satellite imagery can improve retrospective estimates of vaccination campaign impact and future campaign planning by synchronizing interventions with predictable population fluxes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lu2016,
title = {Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen},
author = {Xin Lu and David J. Wrathall and Pål Roe Sundsøy and Md. Nadiruzzaman and Erik Wetter and Asif Iqbal and Taimur Qureshi and Andrew J. Tatem and Geoffrey S. Canright and Kenth Engø-Monsen and Linus Bengtsson},
url = {https://doi.org/10.1007/s10584-016-1753-7},
doi = {10.1007/s10584-016-1753-7},
issn = {1573-1480},
year  = {2016},
date = {2016-10-01},
journal = {Climatic Change},
volume = {138},
number = {3},
pages = {505-519},
abstract = {Large-scale data from digital infrastructure, like mobile phone networks, provides rich information on the behavior of millions of people in areas affected by climate stress. Using anonymized data on mobility and calling behavior from 5.1 million Grameenphone users in Barisal Division and Chittagong District, Bangladesh, we investigate the effect of Cyclone Mahasen, which struck Barisal and Chittagong in May 2013. We characterize spatiotemporal patterns and anomalies in calling frequency, mobile recharges, and population movements before, during and after the cyclone. While it was originally anticipated that the analysis might detect mass evacuations and displacement from coastal areas in the weeks following the storm, no evidence was found to suggest any permanent changes in population distributions. We detect anomalous patterns of mobility both around the time of early warning messages and the storm's landfall, showing where and when mobility occurred as well as its characteristics. We find that anomalous patterns of mobility and calling frequency correlate with rainfall intensity (r = .75, p &lt; 0.05) and use calling frequency to construct a spatiotemporal distribution of cyclone impact as the storm moves across the affected region. Likewise, from mobile recharge purchases we show the spatiotemporal patterns in people's preparation for the storm in vulnerable areas. In addition to demonstrating how anomaly detection can be useful for modeling human adaptation to climate extremes, we also identify several promising avenues for future improvement of disaster planning and response activities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Armstrong2016,
title = {Subnational variation for care at birth in Tanzania: is this explained by place, people, money or drugs?},
author = {Corinne E. Armstrong and Melisa Martínez-Álvarez and Neha S. Singh and Theopista John and Hoviyeh Afnan-Holmes and Chris Grundy and Corrine W. Ruktanochai and Josephine Borghi and Moke Magoma and Georgina Msemo and Zoe Matthews and Gemini Mtei and Joy E. Lawn},
url = {https://doi.org/10.1186/s12889-016-3404-3},
doi = {10.1186/s12889-016-3404-3},
issn = {1471-2458},
year  = {2016},
date = {2016-09-12},
journal = {BMC Public Health},
volume = {16},
number = {2},
pages = {795},
abstract = {Tanzania achieved the Millennium Development Goal for child survival, yet made insufficient progress for maternal and neonatal survival and stillbirths, due to low coverage and quality of services for care at birth, with rural women left behind. Our study aimed to evaluate Tanzania's subnational (regional-level) variations for rural care at birth outcomes, i.e., rural women giving birth in a facility and by Caesarean section (C-section), and associations with health systems inputs (financing, health workforce, facilities, and commodities), outputs (readiness and quality of care) and context (education and GDP).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Neal2016,
title = {Mapping adolescent first births within three east African countries using data from Demographic and Health Surveys: exploring geospatial methods to inform policy},
author = {Sarah Neal and Corrine Ruktanonchai and Venkatraman Chandra-Mouli and Zo"e Matthews and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12978-016-0205-1},
doi = {10.1186/s12978-016-0205-1},
issn = {1742-4755},
year  = {2016},
date = {2016-08-23},
journal = {Reproductive Health},
volume = {13},
number = {1},
pages = {98},
abstract = {Early adolescent pregnancy presents a major barrier to the health and wellbeing of young women and their children. Previous studies suggest geographic heterogeneity in adolescent births, with clear ``hot spots'' experiencing very high prevalence of teenage pregnancy. As the reduction of adolescent pregnancy is a priority in many countries, further detailed information of the geographical areas where they most commonly occur is of value to national and district level policy makers. The aim of this study is to develop a comprehensive assessment of the geographical distribution of adolescent first births in Uganda, Kenya and Tanzania using Demographic and Household (DHS) data using descriptive, spatial analysis and spatial modelling methods.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Early2016,
title = {Global threats from invasive alien species in the twenty-first century and national response capacities},
author = {Regan Early and Bethany A. Bradley and Jeffrey S. Dukes and Joshua J. Lawler and Julian D. Olden and Dana M. Blumenthal and Patrick Gonzalez and Edwin D. Grosholz and Ines Ibañez and Luke P. Miller and Cascade J. B. Sorte and Andrew J. Tatem},
url = {https://doi.org/10.1038/ncomms12485},
doi = {10.1038/ncomms12485},
issn = {2041-1723},
year  = {2016},
date = {2016-08-23},
journal = {Nature Communications},
volume = {7},
number = {1},
pages = {12485},
abstract = {Invasive alien species (IAS) threaten human livelihoods and biodiversity globally. Increasing globalization facilitates IAS arrival, and environmental changes, including climate change, facilitate IAS establishment. Here we provide the first global, spatial analysis of the terrestrial threat from IAS in light of twenty-first century globalization and environmental change, and evaluate national capacities to prevent and manage species invasions. We find that one-sixth of the global land surface is highly vulnerable to invasion, including substantial areas in developing economies and biodiversity hotspots. The dominant invasion vectors differ between high-income countries (imports, particularly of plants and pets) and low-income countries (air travel). Uniting data on the causes of introduction and establishment can improve early-warning and eradication schemes. Most countries have limited capacity to act against invasions. In particular, we reveal a clear need for proactive invasion strategies in areas with high poverty levels, high biodiversity and low historical levels of invasion.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Sorichetta2016,
title = {Mapping internal connectivity through human migration in malaria endemic countries},
author = {Alessandro Sorichetta and Tom J. Bird and Nick W. Ruktanonchai and Elisabeth Erbach-Schoenberg and Carla Pezzulo and Natalia Tejedor and Ian C. Waldock and Jason D. Sadler and Andres J. Garcia and Luigi Sedda and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2016.66},
doi = {10.1038/sdata.2016.66},
issn = {2052-4463},
year  = {2016},
date = {2016-08-16},
journal = {Scientific Data},
volume = {3},
number = {1},
pages = {160066},
abstract = {Human mobility continues to increase in terms of volumes and reach, producing growing global connectivity. This connectivity hampers efforts to eliminate infectious diseases such as malaria through reintroductions of pathogens, and thus accounting for it becomes important in designing global, continental, regional, and national strategies. Recent works have shown that census-derived migration data provides a good proxy for internal connectivity, in terms of relative strengths of movement between administrative units, across temporal scales. To support global malaria eradication strategy efforts, here we describe the construction of an open access archive of estimated internal migration flows in endemic countries built through pooling of census microdata. These connectivity datasets, described here along with the approaches and methods used to create and validate them, are available both through the WorldPop website and the WorldPop Dataverse Repository.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Perkins2016,
title = {Model-based projections of Zika virus infections in childbearing women in the Americas},
author = {T. Alex Perkins and Amir S. Siraj and Corrine W. Ruktanonchai and Moritz U. G. Kraemer and Andrew J. Tatem},
url = {https://doi.org/10.1038/nmicrobiol.2016.126},
doi = {10.1038/nmicrobiol.2016.126},
issn = {2058-5276},
year  = {2016},
date = {2016-07-25},
journal = {Nature Microbiology},
volume = {1},
number = {9},
pages = {16126},
abstract = {Zika virus is a mosquito-borne pathogen that is rapidly spreading across the Americas. Due to associations between Zika virus infection and a range of fetal maladies1,2, the epidemic trajectory of this viral infection poses a significant concern for the nearly 15 million children born in the Americas each year. Ascertaining the portion of this population that is truly at risk is an important priority. One recent estimate3 suggested that 5.42 million childbearing women live in areas of the Americas that are suitable for Zika occurrence. To improve on that estimate, which did not take into account the protective effects of herd immunity, we developed a new approach that combines classic results from epidemiological theory with seroprevalence data and highly spatially resolved data about drivers of transmission to make location-specific projections of epidemic attack rates. Our results suggest that 1.65 (1.45--2.06) million childbearing women and 93.4 (81.6--117.1) million people in total could become infected before the first wave of the epidemic concludes. Based on current estimates of rates of adverse fetal outcomes among infected women2,4,5, these results suggest that tens of thousands of pregnancies could be negatively impacted by the first wave of the epidemic. These projections constitute a revised upper limit of populations at risk in the current Zika epidemic, and our approach offers a new way to make rapid assessments of the threat posed by emerging infectious diseases more generally.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Alegana2016,
title = {Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence},
author = {Victor A. Alegana and Peter M. Atkinson and Christopher Lourenço and Nick W. Ruktanonchai and Claudio Bosco and Elisabeth zu Erbach-Schoenberg and Bradley Didier and Deepa Pindolia and Arnaud Le Menach and Stark Katokele and Petrina Uusiku and Andrew J. Tatem},
url = {https://doi.org/10.1038/srep29628},
doi = {10.1038/srep29628},
issn = {2045-2322},
year  = {2016},
date = {2016-07-13},
journal = {Scientific Reports},
volume = {6},
number = {1},
pages = {29628},
abstract = {The long-term goal of the global effort to tackle malaria is national and regional elimination and eventually eradication. Fine scale multi-temporal mapping in low malaria transmission settings remains a challenge and the World Health Organisation propose use of surveillance in elimination settings. Here, we show how malaria incidence can be modelled at a fine spatial and temporal resolution from health facility data to help focus surveillance and control to population not attending health facilities. Using Namibia as a case study, we predicted the incidence of malaria, via a Bayesian spatio-temporal model, at a fine spatial resolution from parasitologically confirmed malaria cases and incorporated metrics on healthcare use as well as measures of uncertainty associated with incidence predictions. We then combined the incidence estimates with population maps to estimate clinical burdens and show the benefits of such mapping to identifying areas and seasons that can be targeted for improved surveillance and interventions. Fine spatial resolution maps produced using this approach were then used to target resources to specific local populations, and to specific months of the season. This remote targeting can be especially effective where the population distribution is sparse and further surveillance can be limited to specific local areas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Ruktanonchai2016,
title = {Census-derived migration data as a tool for informing malaria elimination policy},
author = {Nick W. Ruktanonchai and Darlene Bhavnani and Alessandro Sorichetta and Linus Bengtsson and Keith H. Carter and Roberto C. Córdoba and Arnaud Le Menach and Xin Lu and Erik Wetter and Elisabeth Erbach-Schoenberg and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12936-016-1315-5},
doi = {10.1186/s12936-016-1315-5},
issn = {1475-2875},
year  = {2016},
date = {2016-05-11},
journal = {Malaria Journal},
volume = {15},
number = {1},
pages = {273},
abstract = {Numerous countries around the world are approaching malaria elimination. Until global eradication is achieved, countries that successfully eliminate the disease will contend with parasite reintroduction through international movement of infected people. Human-mediated parasite mobility is also important within countries near elimination, as it drives parasite flows that affect disease transmission on a subnational scale.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.7554/eLife.15272,
title = {Mapping global environmental suitability for Zika virus},
author = {Jane P Messina and Moritz UG Kraemer and Oliver J Brady and David M Pigott and Freya M Shearer and Daniel J Weiss and Nick Golding and Corrine W Ruktanonchai and Peter W Gething and Emily Cohn and John S Brownstein and Kamran Khan and Andrew J Tatem and Thomas Jaenisch and Christopher JL Murray and Fatima Marinho and Thomas W Scott and Simon I Hay},
editor = {Mark Jit},
url = {https://doi.org/10.7554/eLife.15272},
doi = {10.7554/eLife.15272},
issn = {2050-084X},
year  = {2016},
date = {2016-04-01},
journal = {eLife},
volume = {5},
pages = {e15272},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Zika virus was discovered in Uganda in 1947 and is transmitted by Aedes mosquitoes, which also act as vectors for dengue and chikungunya viruses throughout much of the tropical world. In 2007, an outbreak in the Federated States of Micronesia sparked public health concern. In 2013, the virus began to spread across other parts of Oceania and in 2015, a large outbreak in Latin America began in Brazil. Possible associations with microcephaly and Guillain-Barré syndrome observed in this outbreak have raised concerns about continued global spread of Zika virus, prompting its declaration as a Public Health Emergency of International Concern by the World Health Organization. We conducted species distribution modelling to map environmental suitability for Zika. We show a large portion of tropical and sub-tropical regions globally have suitable environmental conditions with over 2.17 billion people inhabiting these areas.},
keywords = {disease mapping, vector-borne disease, Zika virus},
pubstate = {published},
tppubtype = {article}
}
@article{Zhao2016,
title = {Exploring the spatiotemporal drivers of malaria elimination in Europe},
author = {Xia Zhao and David L. Smith and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12936-016-1175-z},
doi = {10.1186/s12936-016-1175-z},
issn = {1475-2875},
year  = {2016},
date = {2016-03-04},
journal = {Malaria Journal},
volume = {15},
number = {1},
pages = {122},
abstract = {Europe once had widespread malaria, but today it is free from endemic transmission. Changing land use, agricultural practices, housing quality, urbanization, climate change, and improved healthcare are among the many factors thought to have played a role in the declines of malaria seen, but their effects and relative contributions have rarely been quantified.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Gaughan2016,
title = {Spatiotemporal patterns of population in mainland China, 1990 to 2010},
author = {Andrea E. Gaughan and Forrest R. Stevens and Zhuojie Huang and Jeremiah J. Nieves and Alessandro Sorichetta and Shengjie Lai and Xinyue Ye and Catherine Linard and Graeme M. Hornby and Simon I. Hay and Hongjie Yu and Andrew J. Tatem},
url = {https://doi.org/10.1038/sdata.2016.5},
doi = {10.1038/sdata.2016.5},
issn = {2052-4463},
year  = {2016},
date = {2016-02-16},
journal = {Scientific Data},
volume = {3},
number = {1},
pages = {160005},
abstract = {According to UN forecasts, global population will increase to over 8 billion by 2025, with much of this anticipated population growth expected in urban areas. In China, the scale of urbanization has, and continues to be, unprecedented in terms of magnitude and rate of change. Since the late 1970s, the percentage of Chinese living in urban areas increased from textasciitilde18% to over 50%. To quantify these patterns spatially we use time-invariant or temporally-explicit data, including census data for 1990, 2000, and 2010 in an ensemble prediction model. Resulting multi-temporal, gridded population datasets are unique in terms of granularity and extent, providing fine-scale (textasciitilde100thinspacem) patterns of population distribution for mainland China. For consistency purposes, the Tibet Autonomous Region, Taiwan, and the islands in the South China Sea were excluded. The statistical model and considerations for temporally comparable maps are described, along with the resulting datasets. Final, mainland China population maps for 1990, 2000, and 2010 are freely available as products from the WorldPop Project website and the WorldPop Dataverse Repository.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{LU20161,
title = {Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh},
author = {Xin Lu and David J. Wrathall and Pål Roe Sundsøy and Md. Nadiruzzaman and Erik Wetter and Asif Iqbal and Taimur Qureshi and Andrew Tatem and Geoffrey Canright and Kenth Engø-Monsen and Linus Bengtsson},
url = {https://www.sciencedirect.com/science/article/pii/S0959378016300140},
doi = {https://doi.org/10.1016/j.gloenvcha.2016.02.002},
issn = {0959-3780},
year  = {2016},
date = {2016-01-01},
journal = {Global Environmental Change},
volume = {38},
pages = {1-7},
abstract = {Climate change is likely to drive migration from environmentally stressed areas. However quantifying short and long-term movements across large areas is challenging due to difficulties in the collection of highly spatially and temporally resolved human mobility data. In this study we use two datasets of individual mobility trajectories from six million de-identified mobile phone users in Bangladesh over three months and two years respectively. Using data collected during Cyclone Mahasen, which struck Bangladesh in May 2013, we show first how analyses based on mobile network data can describe important short-term features (hours–weeks) of human mobility during and after extreme weather events, which are extremely hard to quantify using standard survey based research. We then demonstrate how mobile data for the first time allow us to study the relationship between fundamental parameters of migration patterns on a national scale. We concurrently quantify incidence, direction, duration and seasonality of migration episodes in Bangladesh. While we show that changes in the incidence of migration episodes are highly correlated with changes in the duration of migration episodes, the correlation between in- and out-migration between areas is unexpectedly weak. The methodological framework described here provides an important addition to current methods in studies of human migration and climate change.},
keywords = {Adaptation, Bangladesh, Climate change, Disaster, Migration, Mobile data},
pubstate = {published},
tppubtype = {article}
}
@article{UTAZI2016161,
title = {A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks},
author = {C. Edson Utazi and Sujit K. Sahu and Peter M. Atkinson and Natalia Tejedor and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S2211675316300240},
doi = {https://doi.org/10.1016/j.spasta.2016.05.006},
issn = {2211-6753},
year  = {2016},
date = {2016-01-01},
journal = {Spatial Statistics},
volume = {17},
pages = {161-178},
abstract = {Health and demographic surveillance systems, formed into networks of sites, are increasingly being established to circumvent unreliable national civil registration systems for estimates of mortality and its determinants in low income countries. Health outcomes, as measured by morbidity and mortality, generally correlate strongly with socioeconomic and environmental characteristics. Therefore, to enable comparison between sites, understand which sites can be grouped and where additional sites would aid understanding of rates and determinants, determining the environmental and socioeconomic representativeness of networks becomes important. This paper proposes a full Bayesian methodology for assessing current representativeness and consequently, identification of future sites, focusing on the INDEPTH network in sub-Saharan Africa as an example. Using socioeconomic and environmental data from the current network of 39 sites, we develop a multi-dimensional finite Gaussian mixture model for clustering the existing sites. Using the fitted model we obtain the posterior predictive probability distribution for cluster membership of each 1×1 km grid cell in Africa. The maximum of the posterior predictive probability distribution for each grid cell is proposed as the criterion for representativeness of the network for that particular grid cell. We demonstrate the conceptual superiority and practical appeal of the proposed Bayesian probabilistic method over previously applied deterministic clustering methods. As an example of the potential utility and application of the method, we also suggest optimal site selection methods for possible additions to the network.},
keywords = {Bayesian inference, BIC, Central clustering, Finite Gaussian mixture model, Gibbs sampling, Predictive clustering},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0162006,
title = {Equality in Maternal and Newborn Health: Modelling Geographic Disparities in Utilisation of Care in Five East African Countries},
author = {Corrine W. Ruktanonchai and Nick W. Ruktanonchai and Andrea Nove and Sofia Lopes and Carla Pezzulo and Claudio Bosco and Victor A. Alegana and Clara R. Burgert and Rogers Ayiko and Andrew SEK Charles and Nkurunziza Lambert and Esther Msechu and Esther Kathini and Zoë Matthews and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0162006},
doi = {10.1371/journal.pone.0162006},
year  = {2016},
date = {2016-01-01},
journal = {PLOS ONE},
volume = {11},
number = {8},
pages = {1-17},
publisher = {Public Library of Science},
abstract = {Background Geographic accessibility to health facilities represents a fundamental barrier to utilisation of maternal and newborn health (MNH) services, driving historically hidden spatial pockets of localized inequalities. Here, we examine utilisation of MNH care as an emergent property of accessibility, highlighting high-resolution spatial heterogeneity and sub-national inequalities in receiving care before, during, and after delivery throughout five East African countries. Methods We calculated a geographic inaccessibility score to the nearest health facility at 300 x 300 m using a dataset of 9,314 facilities throughout Burundi, Kenya, Rwanda, Tanzania and Uganda. Using Demographic and Health Surveys data, we utilised hierarchical mixed effects logistic regression to examine the odds of: 1) skilled birth attendance, 2) receiving 4+ antenatal care visits at time of delivery, and 3) receiving a postnatal health check-up within 48 hours of delivery. We applied model results onto the accessibility surface to visualise the probabilities of obtaining MNH care at both high-resolution and sub-national levels after adjusting for live births in 2015. Results Across all outcomes, decreasing wealth and education levels were associated with lower odds of obtaining MNH care. Increasing geographic inaccessibility scores were associated with the strongest effect in lowering odds of obtaining care observed across outcomes, with the widest disparities observed among skilled birth attendance. Specifically, for each increase in the inaccessibility score to the nearest health facility, the odds of having skilled birth attendance at delivery was reduced by over 75% (0.24; CI: 0.19–0.3), while the odds of receiving antenatal care decreased by nearly 25% (0.74; CI: 0.61–0.89) and 40% for obtaining postnatal care (0.58; CI: 0.45–0.75). Conclusions Overall, these results suggest decreasing accessibility to the nearest health facility significantly deterred utilisation of all maternal health care services. These results demonstrate how spatial approaches can inform policy efforts and promote evidence-based decision-making, and are particularly pertinent as the world shifts into the Sustainable Goals Development era, where sub-national applications will become increasingly useful in identifying and reducing persistent inequalities.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@book{doi/10.2767/61617,
title = {Inferring migrations, traditional methods and new approaches based on mobile phone, social media, and other big data : feasibility study on inferring (labour) mobility and migration in the European Union from big data and social media data},
author = {European Commission and Social Affairs Directorate-General for Employment and Inclusion and A Wi´sniowski and A Tatem and G Abel and E Zagheni and I Weber and A Sorichetta and C Hughes},
doi = {doi/10.2767/61617},
year  = {2016},
date = {2016-01-01},
publisher = {Publications Office},
keywords = {},
pubstate = {published},
tppubtype = {book}
}
@article{doi:10.1098/rsif.2015.1101,
title = {Introduction of rubella-containing-vaccine to Madagascar: implications for roll-out and local elimination},
author = {Amy Wesolowski and Keitly Mensah and Cara E. Brook and Miora Andrianjafimasy and Amy Winter and Caroline O. Buckee and Richter Razafindratsimandresy and Andrew J. Tatem and Jean-Michel Heraud and C. Jessica E. Metcalf},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2015.1101},
doi = {10.1098/rsif.2015.1101},
year  = {2016},
date = {2016-01-01},
journal = {Journal of The Royal Society Interface},
volume = {13},
number = {117},
pages = {20151101},
abstract = {Few countries in Africa currently include rubella-containing vaccination (RCV) in their immunization schedule. The Global Alliance for Vaccines Initiative (GAVI) recently opened a funding window that has motivated more widespread roll-out of RCV. As countries plan RCV introductions, an understanding of the existing burden, spatial patterns of vaccine coverage, and the impact of patterns of local extinction and reintroduction for rubella will be critical to developing effective programmes. As one of the first countries proposing RCV introduction in part with GAVI funding, Madagascar provides a powerful and timely case study. We analyse serological data from measles surveillance systems to characterize the epidemiology of rubella in Madagascar. Combining these results with data on measles vaccination delivery, we develop an age-structured model to simulate rubella vaccination scenarios and evaluate the dynamics of rubella and the burden of congenital rubella syndrome (CRS) across Madagascar. We additionally evaluate the drivers of spatial heterogeneity in age of infection to identify focal locations where vaccine surveillance should be strengthened and where challenges to successful vaccination introduction are expected. Our analyses indicate that characteristics of rubella in Madagascar are in line with global observations, with an average age of infection near 7 years, and an impact of frequent local extinction with reintroductions causing localized epidemics. Modelling results indicate that introduction of RCV into the routine programme alone may initially decrease rubella incidence but then result in cumulative increases in the burden of CRS in some regions (and transient increases in this burden in many regions). Deployment of RCV with regular supplementary campaigns will mitigate these outcomes. Results suggest that introduction of RCV offers a potential for elimination of rubella in Madagascar, but also emphasize both that targeted vaccination is likely to be a lynchpin of this success, and the public health vigilance that this introduction will require.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pcbi.1004846,
title = {Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data},
author = {Nick W. Ruktanonchai and Patrick DeLeenheer and Andrew J. Tatem and Victor A. Alegana and T. Trevor Caughlin and Elisabeth Erbach-Schoenberg and Christopher Lourenço and Corrine W. Ruktanonchai and David L. Smith},
url = {https://doi.org/10.1371/journal.pcbi.1004846},
doi = {10.1371/journal.pcbi.1004846},
year  = {2016},
date = {2016-01-01},
journal = {PLOS Computational Biology},
volume = {12},
number = {4},
pages = {1-19},
publisher = {Public Library of Science},
abstract = {Humans move frequently and tend to carry parasites among areas with endemic malaria and into areas where local transmission is unsustainable. Human-mediated parasite mobility can thus sustain parasite populations in areas where they would otherwise be absent. Data describing human mobility and malaria epidemiology can help classify landscapes into parasite demographic sources and sinks, ecological concepts that have parallels in malaria control discussions of transmission foci. By linking transmission to parasite flow, it is possible to stratify landscapes for malaria control and elimination, as sources are disproportionately important to the regional persistence of malaria parasites. Here, we identify putative malaria sources and sinks for pre-elimination Namibia using malaria parasite rate (PR) maps and call data records from mobile phones, using a steady-state analysis of a malaria transmission model to infer where infections most likely occurred. We also examined how the landscape of transmission and burden changed from the pre-elimination setting by comparing the location and extent of predicted pre-elimination transmission foci with modeled incidence for 2009. This comparison suggests that while transmission was spatially focal pre-elimination, the spatial distribution of cases changed as burden declined. The changing spatial distribution of burden could be due to importation, with cases focused around importation hotspots, or due to heterogeneous application of elimination effort. While this framework is an important step towards understanding progressive changes in malaria distribution and the role of subnational transmission dynamics in a policy-relevant way, future work should account for international parasite movement, utilize real time surveillance data, and relax the steady state assumption required by the presented model.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{https://doi.org/10.1016/j.ijgo.2015.11.004,
title = {Spatial distribution of emergency obstetric and newborn care services in Ghana: Using the evidence to plan interventions},
author = {Samuel Bosomprah and Andrew J. Tatem and Winfred Dotse-Gborgbortsi and Patrick Aboagye and Zoe Matthews},
url = {https://obgyn.onlinelibrary.wiley.com/doi/abs/10.1016/j.ijgo.2015.11.004},
doi = {https://doi.org/10.1016/j.ijgo.2015.11.004},
year  = {2016},
date = {2016-01-01},
journal = {International Journal of Gynecology & Obstetrics},
volume = {132},
number = {1},
pages = {130-134},
abstract = {Abstract Objective To provide clear policy directions for gaps in the provision of signal function services and sub-regions requiring priority attention using data from the 2010 Ghana Emergency Obstetric and Newborn Care (EmONC) survey. Methods Using 2010 survey data, the fraction of facilities with only one or two signal functions missing was calculated for each facility type and EmONC designation. Thematic maps were used to provide insight into inequities in service provision. Results Of 1159 maternity facilities, 89 provided all the necessary basic or comprehensive EmONC signal functions 3 months prior to the 2010 survey. Only 21% of facility-based births were in fully functioning EmONC facilities, but an additional 30% occurred in facilities missing one or two basic signal functions—most often assisted vaginal delivery and removal of retained products. Tackling these missing signal functions would extend births taking place in fully functioning facilities to over 50%. Subnational analyses based on estimated total pregnancies in each district revealed a pattern of inequity in service provision across the country. Conclusion Upgrading facilities missing only one or two signal functions will allow Ghana to meet international standards for availability of EmONC services. Reducing maternal deaths will require high national priority given to addressing inequities in the distribution of EmONC services.},
keywords = {Emergency obstetric and newborn care (EmONC), Geospatial analysis, Ghana, Maternal mortality, Needs assessment, Signal functions},
pubstate = {published},
tppubtype = {article}
}
@article{Sedda2015,
title = {A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997--2010},
author = {Luigi Sedda and Qiuyin Qi and Andrew J. Tatem},
url = {https://doi.org/10.1186/s12936-015-1024-5},
doi = {10.1186/s12936-015-1024-5},
issn = {1475-2875},
year  = {2015},
date = {2015-12-16},
journal = {Malaria Journal},
volume = {14},
number = {1},
pages = {500},
abstract = {The absence of conflict in a country has been cited as a crucial factor affecting the operational feasibility of achieving malaria control and elimination, yet mixed evidence exists on the influence that conflicts have had on malaria transmission. Over the past two decades, Africa has seen substantial numbers of armed conflicts of varying length and scale, creating conditions that can disrupt control efforts and impact malaria transmission. However, very few studies have quantitatively assessed the associations between conflicts and malaria transmission, particularly in a consistent way across multiple countries.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.7554/eLife.09672,
title = {Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017},
author = {Samir Bhatt and Daniel J Weiss and Bonnie Mappin and Ursula Dalrymple and Ewan Cameron and Donal Bisanzio and David L Smith and Catherine L Moyes and Andrew J Tatem and Michael Lynch and Cristin A Fergus and Joshua Yukich and Adam Bennett and Thomas P Eisele and Jan Kolaczinski and Richard E Cibulskis and Simon I Hay and Peter W Gething},
editor = {Catherine Kyobutungi},
url = {https://doi.org/10.7554/eLife.09672},
doi = {10.7554/eLife.09672},
issn = {2050-084X},
year  = {2015},
date = {2015-12-01},
urldate = {2015-12-01},
journal = {eLife},
volume = {4},
pages = {e09672},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Insecticide-treated nets (ITNs) for malaria control are widespread but coverage remains inadequate. We developed a Bayesian model using data from 102 national surveys, triangulated against delivery data and distribution reports, to generate year-by-year estimates of four ITN coverage indicators. We explored the impact of two potential ínefficiencies': uneven net distribution among households and rapid rates of net loss from households. We estimated that, in 2013, 21% (17%–26%) of ITNs were over-allocated and this has worsened over time as overall net provision has increased. We estimated that rates of ITN loss from households are more rapid than previously thought, with 50% lost after 23 (20–28) months. We predict that the current estimate of 920 million additional ITNs required to achieve universal coverage would in reality yield a lower level of coverage (77% population access). By improving efficiency, however, the 920 million ITNs could yield population access as high as 95%.},
keywords = {Bayesian compartment model, ITN, LLIN, malaria},
pubstate = {published},
tppubtype = {article}
}
@article{10.7554/eLife.09520,
title = {Mapping residual transmission for malaria elimination},
author = {Jr Robert C Reiner and Arnaud Le Menach and Simon Kunene and Nyasatu Ntshalintshali and Michelle S Hsiang and T Alex Perkins and Bryan Greenhouse and Andrew J Tatem and Justin M Cohen and David L Smith},
editor = {Mark Jit},
url = {https://doi.org/10.7554/eLife.09520},
doi = {10.7554/eLife.09520},
issn = {2050-084X},
year  = {2015},
date = {2015-12-01},
journal = {eLife},
volume = {4},
pages = {e09520},
publisher = {eLife Sciences Publications, Ltd},
abstract = {Eliminating malaria from a defined region involves draining the endemic parasite reservoir and minimizing local malaria transmission around imported malaria infections. In the last phases of malaria elimination, as universal interventions reap diminishing marginal returns, national resources must become increasingly devoted to identifying where residual transmission is occurring. The needs for accurate measures of progress and practical advice about how to allocate scarce resources require new analytical methods to quantify fine-grained heterogeneity in malaria risk. Using routine national surveillance data from Swaziland (a sub-Saharan country on the verge of elimination), we estimated individual reproductive numbers. Fine-grained maps of reproductive numbers and local malaria importation rates were combined to show ‘malariogenic potential’, a first for malaria elimination. As countries approach elimination, these individual-based measures of transmission risk provide meaningful metrics for planning programmatic responses and prioritizing areas where interventions will contribute most to malaria elimination.},
keywords = {malaria elimination, plasmodium falciparum, spatio-temporal transmission dynamics},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0107042,
title = {Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data},
author = {Forrest R. Stevens and Andrea E. Gaughan and Catherine Linard and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0107042},
doi = {10.1371/journal.pone.0107042},
year  = {2015},
date = {2015-01-01},
journal = {PLOS ONE},
volume = {10},
number = {2},
pages = {1-22},
publisher = {Public Library of Science},
abstract = {High resolution, contemporary data on human population distributions are vital for measuring impacts of population growth, monitoring human-environment interactions and for planning and policy development. Many methods are used to disaggregate census data and predict population densities for finer scale, gridded population data sets. We present a new semi-automated dasymetric modeling approach that incorporates detailed census and ancillary data in a flexible, “Random Forest” estimation technique. We outline the combination of widely available, remotely-sensed and geospatial data that contribute to the modeled dasymetric weights and then use the Random Forest model to generate a gridded prediction of population density at ~100 m spatial resolution. This prediction layer is then used as the weighting surface to perform dasymetric redistribution of the census counts at a country level. As a case study we compare the new algorithm and its products for three countries (Vietnam, Cambodia, and Kenya) with other common gridded population data production methodologies. We discuss the advantages of the new method and increases over the accuracy and flexibility of those previous approaches. Finally, we outline how this algorithm will be extended to provide freely-available gridded population data sets for Africa, Asia and Latin America.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rsif.2015.0073,
title = {Fine resolution mapping of population age-structures for health and development applications},
author = {V. A. Alegana and P. M. Atkinson and C. Pezzulo and A. Sorichetta and D. Weiss and T. Bird and E. Erbach-Schoenberg and A. J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2015.0073},
doi = {10.1098/rsif.2015.0073},
year  = {2015},
date = {2015-01-01},
journal = {Journal of The Royal Society Interface},
volume = {12},
number = {105},
pages = {20150073},
abstract = {The age-group composition of populations varies considerably across the world, and obtaining accurate, spatially detailed estimates of numbers of children under 5 years is important in designing vaccination strategies, educational planning or maternal healthcare delivery. Traditionally, such estimates are derived from population censuses, but these can often be unreliable, outdated and of coarse resolution for resource-poor settings. Focusing on Nigeria, we use nationally representative household surveys and their cluster locations to predict the proportion of the under-five population in 1 × 1 km using a Bayesian hierarchical spatio-temporal model. Results showed that land cover, travel time to major settlements, night-time lights and vegetation index were good predictors and that accounting for fine-scale variation, rather than assuming a uniform proportion of under 5 year olds can result in significant differences in health metrics. The largest gaps in estimated bednet and vaccination coverage were in Kano, Katsina and Jigawa. Geolocated household surveys are a valuable resource for providing detailed, contemporary and regularly updated population age-structure data in the absence of recent census data. By combining these with covariate layers, age-structure maps of unprecedented detail can be produced to guide the targeting of interventions in resource-poor settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1093/inthealth/ihv003,
title = {Remotely measuring populations during a crisis by overlaying two data sources},
author = {Nita Bharti and Xin Lu and Linus Bengtsson and Erik Wetter and Andrew J. Tatem},
url = {https://doi.org/10.1093/inthealth/ihv003},
doi = {10.1093/inthealth/ihv003},
issn = {1876-3413},
year  = {2015},
date = {2015-01-01},
journal = {International Health},
volume = {7},
number = {2},
pages = {90-98},
abstract = {Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods.We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales.The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis.Using complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pcbi.1004267,
title = {Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa},
author = {Amy Wesolowski and Wendy Prudhomme O’Meara and Nathan Eagle and Andrew J. Tatem and Caroline O. Buckee},
url = {https://doi.org/10.1371/journal.pcbi.1004267},
doi = {10.1371/journal.pcbi.1004267},
year  = {2015},
date = {2015-01-01},
journal = {PLOS Computational Biology},
volume = {11},
number = {7},
pages = {1-16},
publisher = {Public Library of Science},
abstract = {Simple spatial interaction models of human mobility based on physical laws have been used extensively in the social, biological, and physical sciences, and in the study of the human dynamics underlying the spread of disease. Recent analyses of commuting patterns and travel behavior in high-income countries have led to the suggestion that these models are highly generalizable, and as a result, gravity and radiation models have become standard tools for describing population mobility dynamics for infectious disease epidemiology. Communities in Sub-Saharan Africa may not conform to these models, however; physical accessibility, availability of transport, and cost of travel between locations may be variable and severely constrained compared to high-income settings, informal labor movements rather than regular commuting patterns are often the norm, and the rise of mega-cities across the continent has important implications for travel between rural and urban areas. Here, we first review how infectious disease frameworks incorporate human mobility on different spatial scales and use anonymous mobile phone data from nearly 15 million individuals to analyze the spatiotemporal dynamics of the Kenyan population. We find that gravity and radiation models fail in systematic ways to capture human mobility measured by mobile phones; both severely overestimate the spatial spread of travel and perform poorly in rural areas, but each exhibits different characteristic patterns of failure with respect to routes and volumes of travel. Thus, infectious disease frameworks that rely on spatial interaction models are likely to misrepresent population dynamics important for the spread of disease in many African populations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@data{DVN/PUGPVR_2015,
title = {Americas Datasets},
author = {Alessandro Sorichetta and Graeme M. Hornby and Forrest R. Stevens and Andrea E. Gaughan and Catherine Linard and Andrew J. Tatem},
url = {https://doi.org/10.7910/DVN/PUGPVR},
doi = {10.7910/DVN/PUGPVR},
year  = {2015},
date = {2015-01-01},
publisher = {Harvard Dataverse},
keywords = {},
pubstate = {published},
tppubtype = {data}
}
@article{10.1093/inthealth/ihv005,
title = {Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa},
author = {Luigi Sedda and Andrew J. Tatem and David W. Morley and Peter M. Atkinson and Nicola A. Wardrop and Carla Pezzulo and Alessandro Sorichetta and Joanna Kuleszo and David J. Rogers},
url = {https://doi.org/10.1093/inthealth/ihv005},
doi = {10.1093/inthealth/ihv005},
issn = {1876-3413},
year  = {2015},
date = {2015-01-01},
journal = {International Health},
volume = {7},
number = {2},
pages = {99-106},
abstract = {Previous analyses have shown the individual correlations between poverty, health and satellite-derived vegetation indices such as the normalized difference vegetation index (NDVI). However, generally these analyses did not explore the statistical interconnections between poverty, health outcomes and NDVI.In this research aspatial methods (principal component analysis) and spatial models (variography, factorial kriging and cokriging) were applied to investigate the correlations and spatial relationships between intensity of poverty, health (expressed as child mortality and undernutrition), and NDVI for a large area of West Africa.This research showed that the intensity of poverty (and hence child mortality and nutrition) varies inversely with NDVI. From the spatial point-of-view, similarities in the spatial variation of intensity of poverty and NDVI were found.These results highlight the utility of satellite-based metrics for poverty models including health and ecological components and, in general for large scale analysis, estimation and optimisation of multidimensional poverty metrics. However, it also stresses the need for further studies on the causes of the association between NDVI, health and poverty. Once these relationships are confirmed and better understood, the presence of this ecological component in poverty metrics has the potential to facilitate the analysis of the impacts of climate change on the rural populations afflicted by poverty and child mortality.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1073/pnas.1423542112,
title = {Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data},
author = {Amy Wesolowski and C. J. E. Metcalf and Nathan Eagle and Janeth Kombich and Bryan T. Grenfell and Ottar N. Bjørnstad and Justin Lessler and Andrew J. Tatem and Caroline O. Buckee},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1423542112},
doi = {10.1073/pnas.1423542112},
year  = {2015},
date = {2015-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {112},
number = {35},
pages = {11114-11119},
abstract = {Changing patterns of human mobility can drive seasonal outbreaks of infectious diseases, but limited data about travel behavior and population flux over time have made this idea difficult to quantify. Mobile phone data provide a unique source of information about human travel. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population fluxes inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates, showing for the first time to our knowledge that mobile phone data capture epidemiologically relevant patterns of movement. Changing patterns of human aggregation are thought to drive annual and multiannual outbreaks of infectious diseases, but the paucity of data about travel behavior and population flux over time has made this idea difficult to test quantitatively. Current measures of human mobility, especially in low-income settings, are often static, relying on approximate travel times, road networks, or cross-sectional surveys. Mobile phone data provide a unique source of information about human travel, but the power of these data to describe epidemiologically relevant changes in population density remains unclear. Here we quantify seasonal travel patterns using mobile phone data from nearly 15 million anonymous subscribers in Kenya. Using a rich data source of rubella incidence, we show that patterns of population travel (fluxes) inferred from mobile phone data are predictive of disease transmission and improve significantly on standard school term time and weather covariates. Further, combining seasonal and spatial data on travel from mobile phone data allows us to characterize seasonal fluctuations in risk across Kenya and produce dynamic importation risk maps for rubella. Mobile phone data therefore offer a valuable previously unidentified source of data for measuring key drivers of seasonal epidemics.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{JIA201588,
title = {Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network},
author = {Peng Jia and Osman Sankoh and Andrew J. Tatem},
url = {https://www.sciencedirect.com/science/article/pii/S1353829215001379},
doi = {https://doi.org/10.1016/j.healthplace.2015.09.009},
issn = {1353-8292},
year  = {2015},
date = {2015-01-01},
journal = {Health & Place},
volume = {36},
pages = {88-96},
abstract = {The International Network for the Demographic Evaluation of Populations and their Health (INDEPTH) has produced reliable longitudinal data about the lives of people in low- and middle-income countries (LMICs) through a global network of health and demographic surveillance system (HDSS) sites. Since reliable demographic data are scarce across many LMICs, we examine the environmental and socioeconomic (ES) similarities between existing HDSS sites and the rest of the LMICs. The HDSS sites were hierarchically grouped by the similarity of their ES conditions to quantify the ES variability between sites. The entire Africa and Asia region was classified to identify which regions were most similar to existing sites, based on available ES data. Results show that the current INDEPTH network architecture does a good job in representing ES conditions, but that great heterogeneities exist, even within individual countries. The results provide valuable information in determining the confidence with which relationships derived from present HDSS sites can be broadly extended to other areas, and to highlight areas where the new HDSS sites would improve significantly the ES coverage of the network.},
keywords = {Africa, Asia, Demographic surveillance sites, Health, Remote sensing},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rspb.2014.2878,
title = {Virus evolution and transmission in an ever more connected world},
author = {Oliver G. Pybus and Andrew J. Tatem and Philippe Lemey},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rspb.2014.2878},
doi = {10.1098/rspb.2014.2878},
year  = {2015},
date = {2015-01-01},
journal = {Proceedings of the Royal Society B: Biological Sciences},
volume = {282},
number = {1821},
pages = {20142878},
abstract = {The frequency and global impact of infectious disease outbreaks, particularly those caused by emerging viruses, demonstrate the need for a better understanding of how spatial ecology and pathogen evolution jointly shape epidemic dynamics. Advances in computational techniques and the increasing availability of genetic and geospatial data are helping to address this problem, particularly when both information sources are combined. Here, we review research at the intersection of evolutionary biology, human geography and epidemiology that is working towards an integrated view of spatial incidence, host mobility and viral genetic diversity. We first discuss how empirical studies have combined viral spatial and genetic data, focusing particularly on the contribution of evolutionary analyses to epidemiology and disease control. Second, we explore the interplay between virus evolution and global dispersal in more depth for two pathogens: human influenza A virus and chikungunya virus. We discuss the opportunities for future research arising from new analyses of human transportation and trade networks, as well as the associated challenges in accessing and sharing relevant spatial and genetic data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1080/17538947.2014.965761,
title = {Exploring nationally and regionally defined models for large area population mapping},
author = {A. E. Gaughan and F. R. Stevens and C. Linard and N. N. Patel and A. J. Tatem},
url = {https://doi.org/10.1080/17538947.2014.965761},
doi = {10.1080/17538947.2014.965761},
year  = {2015},
date = {2015-01-01},
journal = {International Journal of Digital Earth},
volume = {8},
number = {12},
pages = {989-1006},
publisher = {Taylor & Francis},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{metcalf_tatem_bjornstad_lessler_o'reilly_takahashi_cutts_grenfell_2015,
title = {Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa},
author = {C. J. E. METCALF and A. TATEM and O. N. BJORNSTAD and J. LESSLER and K. O'REILLY and S. TAKAHASHI and F. CUTTS and B. T. GRENFELL},
doi = {10.1017/S0950268814001988},
year  = {2015},
date = {2015-01-01},
journal = {Epidemiology and Infection},
volume = {143},
number = {7},
pages = {1457–1466},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PATEL2015199,
title = {Multitemporal settlement and population mapping from Landsat using Google Earth Engine},
author = {Nirav N. Patel and Emanuele Angiuli and Paolo Gamba and Andrea Gaughan and Gianni Lisini and Forrest R. Stevens and Andrew J. Tatem and Giovanna Trianni},
url = {https://www.sciencedirect.com/science/article/pii/S0303243414001998},
doi = {https://doi.org/10.1016/j.jag.2014.09.005},
issn = {0303-2434},
year  = {2015},
date = {2015-01-01},
journal = {International Journal of Applied Earth Observation and Geoinformation},
volume = {35},
pages = {199-208},
abstract = {As countries become increasingly urbanized, understanding how urban areas are changing within the landscape becomes increasingly important. Urbanized areas are often the strongest indicators of human interaction with the environment, and understanding how urban areas develop through remotely sensed data allows for more sustainable practices. The Google Earth Engine (GEE) leverages cloud computing services to provide analysis capabilities on over 40 years of Landsat data. As a remote sensing platform, its ability to analyze global data rapidly lends itself to being an invaluable tool for studying the growth of urban areas. Here we present (i) An approach for the automated extraction of urban areas from Landsat imagery using GEE, validated using higher resolution images, (ii) a novel method of validation of the extracted urban extents using changes in the statistical performance of a high resolution population mapping method. Temporally distinct urban extractions were classified from the GEE catalog of Landsat 5 and 7 data over the Indonesian island of Java by using a Normalized Difference Spectral Vector (NDSV) method. Statistical evaluation of all of the tests was performed, and the value of population mapping methods in validating these urban extents was also examined. Results showed that the automated classification from GEE produced accurate urban extent maps, and that the integration of GEE-derived urban extents also improved the quality of the population mapping outputs.},
keywords = {Google Earth Engine, Landsat, Multitemporal, Population mapping, Settlement mapping, Spatial demography, Urbanization},
pubstate = {published},
tppubtype = {article}
}
@article{MERTES2015331,
title = {Detecting change in urban areas at continental scales with MODIS data},
author = {C. M. Mertes and A. Schneider and D. Sulla-Menashe and A. J. Tatem and B. Tan},
url = {https://www.sciencedirect.com/science/article/pii/S003442571400368X},
doi = {https://doi.org/10.1016/j.rse.2014.09.023},
issn = {0034-4257},
year  = {2015},
date = {2015-01-01},
journal = {Remote Sensing of Environment},
volume = {158},
pages = {331-347},
abstract = {Urbanization is one of the most important components of global environmental change, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify c. 2010 urban extent; and (3) mapping newly built areas (2000–2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East–Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socioeconomic/political and ecological/climatic conditions with good accuracy (70–91% overall accuracy by country, 69–89% by biome). The 250m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of the error in the change detection can be related to definitional issues or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as for providing a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.},
keywords = {Change detection, Cities, Classification, Data fusion, Decision fusion, Decision trees, Land cover, Machine learning, Urban areas, Urbanization},
pubstate = {published},
tppubtype = {article}
}
@article{metcalf_tatem_bjornstad_lessler_o'reilly_takahashi_cutts_grenfell_2015b,
title = {Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa},
author = {C. J. E. METCALF and A. TATEM and O. N. BJORNSTAD and J. LESSLER and K. O'REILLY and S. TAKAHASHI and F. CUTTS and B. T. GRENFELL},
doi = {10.1017/S0950268814001988},
year  = {2015},
date = {2015-01-01},
journal = {Epidemiology and Infection},
volume = {143},
number = {7},
pages = {1457–1466},
publisher = {Cambridge University Press},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Gilbert2014,
title = {Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia},
author = {Marius Gilbert and Nick Golding and Hang Zhou and G. R. William Wint and Timothy P. Robinson and Andrew J. Tatem and Shengjie Lai and Sheng Zhou and Hui Jiang and Danhuai Guo and Zhi Huang and Jane P. Messina and Xiangming Xiao and Catherine Linard and Thomas P. Van Boeckel and Vincent Martin and Samir Bhatt and Peter W. Gething and Jeremy J. Farrar and Simon I. Hay and Hongjie Yu},
url = {https://doi.org/10.1038/ncomms5116},
doi = {10.1038/ncomms5116},
issn = {2041-1723},
year  = {2014},
date = {2014-06-17},
journal = {Nature Communications},
volume = {5},
number = {1},
pages = {4116},
abstract = {Two epidemic waves of an avian influenza A (H7N9) virus have so far affected China. Most human cases have been attributable to poultry exposure at live-poultry markets, where most positive isolates were sampled. The potential geographic extent of potential re-emerging epidemics is unknown, as are the factors associated with it. Using newly assembled data sets of the locations of 8,943 live-poultry markets in China and maps of environmental correlates, we develop a statistical model that accurately predicts the risk of H7N9 market infection across Asia. Local density of live-poultry markets is the most important predictor of H7N9 infection risk in markets, underscoring their key role in the spatial epidemiology of H7N9, alongside other poultry, land cover and anthropogenic predictor variables. Identification of areas in Asia with high suitability for H7N9 infection enhances our capacity to target biosurveillance and control, helping to restrict the spread of this important disease.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Pindolia2014,
title = {Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and elimination},
author = {Deepa K. Pindolia and Andres J. Garcia and Zhuojie Huang and Timothy Fik and David L. Smith and Andrew J. Tatem},
url = {https://doi.org/10.1186/1475-2875-13-169},
doi = {10.1186/1475-2875-13-169},
issn = {1475-2875},
year  = {2014},
date = {2014-05-03},
journal = {Malaria Journal},
volume = {13},
number = {1},
pages = {169},
abstract = {Identifying human and malaria parasite movements is important for control planning across all transmission intensities. Imported infections can reintroduce infections into areas previously free of infection, maintain `hotspots' of transmission and import drug resistant strains, challenging national control programmes at a variety of temporal and spatial scales. Recent analyses based on mobile phone usage data have provided valuable insights into population and likely parasite movements within countries, but these data are restricted to sub-national analyses, leaving important cross-border movements neglected.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2014b,
title = {Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning},
author = {Andrew J. Tatem and Zhuojie Huang and Clothilde Narib and Udayan Kumar and Deepika Kandula and Deepa K. Pindolia and David L. Smith and Justin M. Cohen and Bonita Graupe and Petrina Uusiku and Christopher Lourenço},
url = {https://doi.org/10.1186/1475-2875-13-52},
doi = {10.1186/1475-2875-13-52},
issn = {1475-2875},
year  = {2014},
date = {2014-02-10},
journal = {Malaria Journal},
volume = {13},
number = {1},
pages = {52},
abstract = {As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2014c,
title = {Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning},
author = {Andrew J. Tatem and Zhuojie Huang and Clothilde Narib and Udayan Kumar and Deepika Kandula and Deepa K. Pindolia and David L. Smith and Justin M. Cohen and Bonita Graupe and Petrina Uusiku and Christopher Lourenço},
url = {https://doi.org/10.1186/1475-2875-13-52},
doi = {10.1186/1475-2875-13-52},
issn = {1475-2875},
year  = {2014},
date = {2014-02-10},
journal = {Malaria Journal},
volume = {13},
number = {1},
pages = {52},
abstract = {As successful malaria control programmes re-orientate towards elimination, the identification of transmission foci, targeting of attack measures to high-risk areas and management of importation risk become high priorities. When resources are limited and transmission is varying seasonally, approaches that can rapidly prioritize areas for surveillance and control can be valuable, and the most appropriate attack measure for a particular location is likely to differ depending on whether it exports or imports malaria infections.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2014,
title = {Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births},
author = {Andrew J. Tatem and James Campbell and Maria Guerra-Arias and Luc Bernis and Allisyn Moran and Zo"e Matthews},
url = {https://doi.org/10.1186/1476-072X-13-2},
doi = {10.1186/1476-072X-13-2},
issn = {1476-072X},
year  = {2014},
date = {2014-01-04},
journal = {International Journal of Health Geographics},
volume = {13},
number = {1},
pages = {2},
abstract = {The health and survival of women and their new-born babies in low income countries has been a key priority in public health since the 1990s. However, basic planning data, such as numbers of pregnancies and births, remain difficult to obtain and information is also lacking on geographic access to key services, such as facilities with skilled health workers. For maternal and newborn health and survival, planning for safer births and healthier newborns could be improved by more accurate estimations of the distributions of women of childbearing age. Moreover, subnational estimates of projected future numbers of pregnancies are needed for more effective strategies on human resources and infrastructure, while there is a need to link information on pregnancies to better information on health facilities in districts and regions so that coverage of services can be assessed.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1093/inthealth/ihu006,
title = {Mapping population and pathogen movements},
author = {Andrew J. Tatem},
url = {https://doi.org/10.1093/inthealth/ihu006},
doi = {10.1093/inthealth/ihu006},
issn = {1876-3413},
year  = {2014},
date = {2014-01-01},
journal = {International Health},
volume = {6},
number = {1},
pages = {5-11},
abstract = {For most of human history, populations have been relatively isolated from each other, and only recently has there been extensive contact between peoples, flora and fauna from both old and new worlds. The reach, volume and speed of modern travel are unprecedented, with human mobility increasing in high income countries by over 1000-fold since 1800. This growth is putting people at risk from the emergence of new strains of familiar diseases, and from completely new diseases, while ever more cases of the movement of both disease vectors and the diseases they carry are being seen. Pathogens and their vectors can now move further, faster and in greater numbers than ever before. Equally however, we now have access to the most detailed and comprehensive datasets on human mobility and pathogen distributions ever assembled, in order to combat these threats. This short review paper provides an overview of these datasets, with a particular focus on low income regions, and covers briefly approaches used to combine them to help us understand and control some of the negative effects of population and pathogen movements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1093/migration/mnu036,
title = {Modeling internal migration flows in sub-Saharan Africa using census microdata},
author = {Andres J. Garcia and Deepa K. Pindolia and Kenneth K. Lopiano and Andrew J. Tatem},
url = {https://doi.org/10.1093/migration/mnu036},
doi = {10.1093/migration/mnu036},
issn = {2049-5838},
year  = {2014},
date = {2014-01-01},
journal = {Migration Studies},
volume = {3},
number = {1},
pages = {89-110},
abstract = {Globalization and the expansion of transport networks has transformed migration into a major policy issue because of its effects on a range of phenomena, including resource flows in economics, urbanization, as well as the epidemiology of infectious diseases. Quantifying and modeling human migration can contribute towards a better understanding of the nature of migration and help develop evidence-based interventions for disease control policy, economic development, and resource allocation. In this study we paired census microdata from 10 countries in sub-Saharan Africa with additional spatial datasets to develop models for the internal migration flows in each country, including key drivers that reflect the changing social, demographic, economic, and environmental landscapes. We assessed how well these gravity-type spatial interaction models can both explain and predict migration. Results show that the models can explain up to 87 percent of internal migration, can predict future within-country migration with correlations of up to 0.91, and can also predict migration in other countries with correlations of up to 0.72. Findings show that such models are useful tools for understanding migration as well as predicting flows in regions where data are sparse, and can contribute towards strategic economic development, planning, and disease control targeting.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rsif.2014.0642,
title = {Theory and data for simulating fine-scale human movement in an urban environment},
author = {T. Alex Perkins and Andres J. Garcia and Valerie A. Paz-Soldán and Steven T. Stoddard and Robert C. Reiner and Gonzalo Vazquez-Prokopec and Donal Bisanzio and Amy C. Morrison and Eric S. Halsey and Tadeusz J. Kochel and David L. Smith and Uriel Kitron and Thomas W. Scott and Andrew J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2014.0642},
doi = {10.1098/rsif.2014.0642},
year  = {2014},
date = {2014-01-01},
journal = {Journal of The Royal Society Interface},
volume = {11},
number = {99},
pages = {20140642},
abstract = {Individual-based models of infectious disease transmission depend on accurate quantification of fine-scale patterns of human movement. Existing models of movement either pertain to overly coarse scales, simulate some aspects of movement but not others, or were designed specifically for populations in developed countries. Here, we propose a generalizable framework for simulating the locations that an individual visits, time allocation across those locations, and population-level variation therein. As a case study, we fit alternative models for each of five aspects of movement (number, distance from home and types of locations visited; frequency and duration of visits) to interview data from 157 residents of the city of Iquitos, Peru. Comparison of alternative models showed that location type and distance from home were significant determinants of the locations that individuals visited and how much time they spent there. We also found that for most locations, residents of two neighbourhoods displayed indistinguishable preferences for visiting locations at various distances, despite differing distributions of locations around those neighbourhoods. Finally, simulated patterns of time allocation matched the interview data in a number of ways, suggesting that our framework constitutes a sound basis for simulating fine-scale movement and for investigating factors that influence it.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1073/pnas.1408439111,
title = {Dynamic population mapping using mobile phone data},
author = {Pierre Deville and Catherine Linard and Samuel Martin and Marius Gilbert and Forrest R. Stevens and Andrea E. Gaughan and Vincent D. Blondel and Andrew J. Tatem},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1408439111},
doi = {10.1073/pnas.1408439111},
year  = {2014},
date = {2014-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {111},
number = {45},
pages = {15888-15893},
abstract = {Knowing where people are is critical for accurate impact assessments and intervention planning, particularly those focused on population health, food security, climate change, conflicts, and natural disasters. This study demonstrates how data collected by mobile phone network operators can cost-effectively provide accurate and detailed maps of population distribution over national scales and any time period while guaranteeing phone users’ privacy. The methods outlined may be applied to estimate human population densities in low-income countries where data on population distributions may be scarce, outdated, and unreliable, or to estimate temporal variations in population density. The work highlights how facilitating access to anonymized mobile phone data might enable fast and cheap production of population maps in emergency and data-scarce situations. During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{PIEL2014e80,
title = {Global migration and the changing distribution of sickle haemoglobin: a quantitative study of temporal trends between 1960 and 2000},
author = {Frédéric B Piel and Andrew J Tatem and Zhuojie Huang and Sunetra Gupta and Thomas N Williams and David J Weatherall},
url = {https://www.sciencedirect.com/science/article/pii/S2214109X13701505},
doi = {https://doi.org/10.1016/S2214-109X(13)70150-5},
issn = {2214-109X},
year  = {2014},
date = {2014-01-01},
journal = {The Lancet Global Health},
volume = {2},
number = {2},
pages = {e80-e89},
abstract = {Summary 
Background 
Changes in the geographical distribution of genetic disorders are often thought to happen slowly, especially when compared with infectious diseases. Whereas mutations, genetic drift, and natural selection take place over many generations, epidemics can spread through large populations within a few days or weeks. Nevertheless, population movements can interfere with these processes, and few studies have been done of their effect on genetic disorders. We aimed to investigate the effect of global migration on the distribution of the sickle-cell gene—the most common and clinically significant haemoglobin structural variant. 
Methods 
For each country, we extracted data from the World Bank's Global Bilateral Migration Database about international human migrations between 1960 and 2000. We combined this information with evidence-based estimates of national HbS allele frequencies, generated within a Bayesian geostatistical framework, to analyse temporal changes in the net numbers of migrants, and classified countries with an index summarising these temporal trends. 
Findings 
The number of international migrants increased from 92·6 million in 1960, to 165·2 million in 2000. The estimated global number of migrants with HbS increased from about 1·6 million in 1960, to 3·6 million in 2000. This increase was largely due to an increase in the number of migrants from countries with HbS allele frequencies higher than 10%, from 3·1 million in 1960, to 14·2 million in 2000. Additionally, the mean number of countries of origin for each destination country increased from 70 (SE 46) in 1960, to 98 (48) in 2000, showing an increasing diversity in the network of international migrations between countries. Our index of change map shows a patchy distribution of the magnitude of temporal changes, with the highest positive and negative values scattered across all continents. 
Interpretation 
Global human population movements have had a substantial effect on the distribution of the HbS gene. Population movements can create a long-term burden on health-care systems. Our findings, which emphasise countries in which migration fluxes are changing the most, should increase awareness about the global burden of haemoglobinopathies and encourage policy makers to implement specific public health interventions, such as screening programmes and genetic counselling. 
Funding 
Wellcome Trust, European Research Council, Bill & Melinda Gates Foundation, National Institute of Allergy and Infectious Diseases–National Institutes of Health, the Research and Policy for Infectious Disease Dynamics program, Fogarty International Center.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{,
title = {Mapping the denominator: spatial demography in the measurement of progress},
author = {Andrew J. Tatem},
url = {https://academic.oup.com/inthealth/article/6/3/153/2964850},
issn = {1876-3413},
year  = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
journal = {International Health},
volume = {6},
number = {3},
pages = {153-155},
abstract = {Measuring progress towards international health goals requires a reliable baseline from which to measure change and recent methodological advancements have advanced our abilities to measure, model and map the prevalence of health issues using sophisticated tools. The provision of burden estimates generally requires linking these estimates with spatial demographic data, but for many resource-poor countries data on total population sizes, distributions, compositions and temporal trends are lacking, prompting a reliance on uncertain estimates. Modern technologies and data archives are offering solutions, but the huge range of uncertainties that exist today in spatial denominator datasets will still be around for many years to come.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Pindolia2013,
title = {The demographics of human and malaria movement and migration patterns in East Africa},
author = {Deepa K. Pindolia and Andres J. Garcia and Zhuojie Huang and David L. Smith and Victor A. Alegana and Abdisalan M. Noor and Robert W. Snow and Andrew J. Tatem},
url = {https://doi.org/10.1186/1475-2875-12-397},
doi = {10.1186/1475-2875-12-397},
issn = {1475-2875},
year  = {2013},
date = {2013-11-05},
journal = {Malaria Journal},
volume = {12},
number = {1},
pages = {397},
abstract = {The quantification of parasite movements can provide valuable information for control strategy planning across all transmission intensities. Mobile parasite carrying individuals can instigate transmission in receptive areas, spread drug resistant strains and reduce the effectiveness of control strategies. The identification of mobile demographic groups, their routes of travel and how these movements connect differing transmission zones, potentially enables limited resources for interventions to be efficiently targeted over space, time and populations.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Lu2013,
title = {Approaching the Limit of Predictability in Human Mobility},
author = {Xin Lu and Erik Wetter and Nita Bharti and Andrew J. Tatem and Linus Bengtsson},
url = {https://doi.org/10.1038/srep02923},
doi = {10.1038/srep02923},
issn = {2045-2322},
year  = {2013},
date = {2013-10-11},
journal = {Scientific Reports},
volume = {3},
number = {1},
pages = {2923},
abstract = {In this study we analyze the travel patterns of 500,000 individuals in Cote d'Ivoire using mobile phone call data records. By measuring the uncertainties of movements using entropy, considering both the frequencies and temporal correlations of individual trajectories, we find that the theoretical maximum predictability is as high as 88%. To verify whether such a theoretical limit can be approached, we implement a series of Markov chain (MC) based models to predict the actual locations visited by each user. Results show that MC models can produce a prediction accuracy of 87% for stationary trajectories and 95% for non-stationary trajectories. Our findings indicate that human mobility is highly dependent on historical behaviors and that the maximum predictability is not only a fundamental theoretical limit for potential predictive power, but also an approachable target for actual prediction accuracy.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2013,
title = {Millennium development health metrics: where do Africa's children and women of childbearing age live?},
author = {Andrew J. Tatem and Andres J. Garcia and Robert W. Snow and Abdisalan M. Noor and Andrea E. Gaughan and Marius Gilbert and Catherine Linard},
url = {https://doi.org/10.1186/1478-7954-11-11},
doi = {10.1186/1478-7954-11-11},
issn = {1478-7954},
year  = {2013},
date = {2013-07-23},
journal = {Population Health Metrics},
volume = {11},
number = {1},
pages = {11},
abstract = {The Millennium Development Goals (MDGs) have prompted an expansion in approaches to deriving health metrics to measure progress toward their achievement. Accurate measurements should take into account the high degrees of spatial heterogeneity in health risks across countries, and this has prompted the development of sophisticated cartographic techniques for mapping and modeling risks. Conversion of these risks to relevant population-based metrics requires equally detailed information on the spatial distribution and attributes of the denominator populations. However, spatial information on age and sex composition over large areas is lacking, prompting many influential studies that have rigorously accounted for health risk heterogeneities to overlook the substantial demographic variations that exist subnationally and merely apply national-level adjustments.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2013b,
title = {Urbanization and the global malaria recession},
author = {Andrew J. Tatem and Peter W. Gething and David L. Smith and Simon I. Hay},
url = {https://doi.org/10.1186/1475-2875-12-133},
doi = {10.1186/1475-2875-12-133},
issn = {1475-2875},
year  = {2013},
date = {2013-04-17},
journal = {Malaria Journal},
volume = {12},
number = {1},
pages = {133},
abstract = {The past century has seen a significant contraction in the global extent of malaria transmission, resulting in over 50 countries being declared malaria free, and many regions of currently endemic countries eliminating the disease. Moreover, substantial reductions in transmission have been seen since 1900 in those areas that remain endemic today. Recent work showed that this malaria recession was unlikely to have been driven by climatic factors, and that control measures likely played a significant role. It has long been considered, however, that economic development, and particularly urbanization, has also been a causal factor. The urbanization process results in profound socio-economic and landscape changes that reduce malaria transmission, but the magnitude and extent of these effects on global endemicity reductions are poorly understood.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Cohen2013,
title = {Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland},
author = {Justin M. Cohen and Sabelo Dlamini and Joseph M. Novotny and Deepika Kandula and Simon Kunene and Andrew J. Tatem},
url = {https://doi.org/10.1186/1475-2875-12-61},
doi = {10.1186/1475-2875-12-61},
issn = {1475-2875},
year  = {2013},
date = {2013-02-11},
journal = {Malaria Journal},
volume = {12},
number = {1},
pages = {61},
abstract = {As successful malaria control programmes move towards elimination, they must identify residual transmission foci, target vector control to high-risk areas, focus on both asymptomatic and symptomatic infections, and manage importation risk. High spatial and temporal resolution maps of malaria risk can support all of these activities, but commonly available malaria maps are based on parasite rate, a poor metric for measuring malaria at extremely low prevalence. New approaches are required to provide case-based risk maps to countries seeking to identify remaining hotspots of transmission while managing the risk of transmission from imported cases.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0055882,
title = {High Resolution Population Distribution Maps for Southeast Asia in 2010 and 2015},
author = {Andrea E. Gaughan and Forrest R. Stevens and Catherine Linard and Peng Jia and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0055882},
doi = {10.1371/journal.pone.0055882},
year  = {2013},
date = {2013-01-01},
journal = {PLOS ONE},
volume = {8},
number = {2},
pages = {1-11},
publisher = {Public Library of Science},
abstract = {Spatially accurate, contemporary data on human population distributions are vitally important to many applied and theoretical researchers. The Southeast Asia region has undergone rapid urbanization and population growth over the past decade, yet existing spatial population distribution datasets covering the region are based principally on population count data from censuses circa 2000, with often insufficient spatial resolution or input data to map settlements precisely. Here we outline approaches to construct a database of GIS-linked circa 2010 census data and methods used to construct fine-scale (∼100 meters spatial resolution) population distribution datasets for each country in the Southeast Asia region. Landsat-derived settlement maps and land cover information were combined with ancillary datasets on infrastructure to model population distributions for 2010 and 2015. These products were compared with those from two other methods used to construct commonly used global population datasets. Results indicate mapping accuracies are consistently higher when incorporating land cover and settlement information into the AsiaPop modelling process. Using existing data, it is possible to produce detailed, contemporary and easily updatable population distribution datasets for Southeast Asia. The 2010 and 2015 datasets produced are freely available as a product of the AsiaPop Project and can be downloaded from: www.asiapop.org.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0056057,
title = {Place-Based Attributes Predict Community Membership in a Mobile Phone Communication Network},
author = {T. Trevor Caughlin and Nick Ruktanonchai and Miguel A. Acevedo and Kenneth K. Lopiano and Olivia Prosper and Nathan Eagle and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0056057},
doi = {10.1371/journal.pone.0056057},
year  = {2013},
date = {2013-01-01},
journal = {PLOS ONE},
volume = {8},
number = {2},
pages = {1-9},
publisher = {Public Library of Science},
abstract = {Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0052971,
title = {The Use of Census Migration Data to Approximate Human Movement Patterns across Temporal Scales},
author = {Amy Wesolowski and Caroline O. Buckee and Deepa K. Pindolia and Nathan Eagle and David L. Smith and Andres J. Garcia and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0052971},
doi = {10.1371/journal.pone.0052971},
year  = {2013},
date = {2013-01-01},
journal = {PLOS ONE},
volume = {8},
number = {1},
pages = {1-8},
publisher = {Public Library of Science},
abstract = {Human movement plays a key role in economies and development, the delivery of services, and the spread of infectious diseases. However, it remains poorly quantified partly because reliable data are often lacking, particularly for low-income countries. The most widely available are migration data from human population censuses, which provide valuable information on relatively long timescale relocations across countries, but do not capture the shorter-scale patterns, trips less than a year, that make up the bulk of human movement. Census-derived migration data may provide valuable proxies for shorter-term movements however, as substantial migration between regions can be indicative of well connected places exhibiting high levels of movement at finer time scales, but this has never been examined in detail. Here, an extensive mobile phone usage data set for Kenya was processed to extract movements between counties in 2009 on weekly, monthly, and annual time scales and compared to data on change in residence from the national census conducted during the same time period. We find that the relative ordering across Kenyan counties for incoming, outgoing and between-county movements shows strong correlations. Moreover, the distributions of trip durations from both sources of data are similar, and a spatial interaction model fit to the data reveals the relationships of different parameters over a range of movement time scales. Significant relationships between census migration data and fine temporal scale movement patterns exist, and results suggest that census data can be used to approximate certain features of movement patterns across multiple temporal scales, extending the utility of census-derived migration data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{LINARD201323,
title = {Modelling spatial patterns of urban growth in Africa},
author = {Catherine Linard and Andrew J. Tatem and Marius Gilbert},
url = {https://www.sciencedirect.com/science/article/pii/S0143622813001707},
doi = {https://doi.org/10.1016/j.apgeog.2013.07.009},
issn = {0143-6228},
year  = {2013},
date = {2013-01-01},
journal = {Applied Geography},
volume = {44},
pages = {23-32},
abstract = {The population of Africa is predicted to double over the next 40 years, driving exceptionally high urban expansion rates that will induce significant socio-economic, environmental and health changes. In order to prepare for these changes, it is important to better understand urban growth dynamics in Africa and better predict the spatial pattern of rural-urban conversions. Previous work on urban expansion has been carried out at the city level or at the global level with a relatively coarse 5–10 km resolution. The main objective of the present paper was to develop a modelling approach at an intermediate scale in order to identify factors that influence spatial patterns of urban expansion in Africa. Boosted Regression Tree models were developed to predict the spatial pattern of rural-urban conversions in every large African city. Urban change data between circa 1990 and circa 2000 available for 20 large cities across Africa were used as training data. Results showed that the urban land in a 1 km neighbourhood and the accessibility to the city centre were the most influential variables. Results obtained were generally more accurate than results obtained using a distance-based urban expansion model and showed that the spatial pattern of small, compact and fast growing cities were easier to simulate than cities with lower population densities and a lower growth rate. The simulation method developed here will allow the production of spatially detailed urban expansion forecasts for 2020 and 2025 for Africa, data that are increasingly required by global change modellers.},
keywords = {Africa, Boosted regression trees, Modelling, Spatial pattern, Urban growth},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rsif.2012.0756,
title = {Implications of spatially heterogeneous vaccination coverage for the risk of congenital rubella syndrome in South Africa},
author = {C. J. E. Metcalf and C. Cohen and J. Lessler and J. M. McAnerney and G. M. Ntshoe and A. Puren and P. Klepac and A. Tatem and B. T. Grenfell and O. N. Bjørnstad},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rsif.2012.0756},
doi = {10.1098/rsif.2012.0756},
year  = {2013},
date = {2013-01-01},
journal = {Journal of The Royal Society Interface},
volume = {10},
number = {78},
pages = {20120756},
abstract = {Rubella is generally a mild childhood disease, but infection during early pregnancy may cause spontaneous abortion or congenital rubella syndrome (CRS), which may entail a variety of birth defects. Since vaccination at levels short of those necessary to achieve eradication may increase the average age of infection, and thus potentially the CRS burden, introduction of the vaccine has been limited to contexts where coverage is high. Recent work suggests that spatial heterogeneity in coverage should also be a focus of concern. Here, we use a detailed dataset from South Africa to explore the implications of heterogeneous vaccination for the burden of CRS, introducing realistic vaccination scenarios based on reported levels of measles vaccine coverage. Our results highlight the potential impact of country-wide reductions of incidence of rubella on the local CRS burdens in districts with small population sizes. However, simulations indicate that if rubella vaccination is introduced with coverage reflecting current estimates for measles coverage in South Africa, the burden of CRS is likely to be reduced overall over a 30 year time horizon by a factor of 3, despite the fact that this coverage is lower than the traditional 80 per cent rule of thumb for vaccine introduction, probably owing to a combination of relatively low birth and transmission rates. We conclude by discussing the likely impact of private-sector vaccination.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1098/rstb.2012.0145,
title = {A sticky situation: the unexpected stability of malaria elimination},
author = {David L. Smith and Justin M. Cohen and Christinah Chiyaka and Geoffrey Johnston and Peter W. Gething and Roly Gosling and Caroline O. Buckee and Ramanan Laxminarayan and Simon I. Hay and Andrew J. Tatem},
url = {https://royalsocietypublishing.org/doi/abs/10.1098/rstb.2012.0145},
doi = {10.1098/rstb.2012.0145},
year  = {2013},
date = {2013-01-01},
journal = {Philosophical Transactions of the Royal Society B: Biological Sciences},
volume = {368},
number = {1623},
pages = {20120145},
abstract = {Malaria eradication involves eliminating malaria from every country where transmission occurs. Current theory suggests that the post-elimination challenges of remaining malaria-free by stopping transmission from imported malaria will have onerous operational and financial requirements. Although resurgent malaria has occurred in a majority of countries that tried but failed to eliminate malaria, a review of resurgence in countries that successfully eliminated finds only four such failures out of 50 successful programmes. Data documenting malaria importation and onwards transmission in these countries suggests malaria transmission potential has declined by more than 50-fold (i.e. more than 98%) since before elimination. These outcomes suggest that elimination is a surprisingly stable state. Elimination's ‘stickiness’ must be explained either by eliminating countries starting off qualitatively different from non-eliminating countries or becoming different once elimination was achieved. Countries that successfully eliminated were wealthier and had lower baseline endemicity than those that were unsuccessful, but our analysis shows that those same variables were at best incomplete predictors of the patterns of resurgence. Stability is reinforced by the loss of immunity to disease and by the health system's increasing capacity to control malaria transmission after elimination through routine treatment of cases with antimalarial drugs supplemented by malaria outbreak control. Human travel patterns reinforce these patterns; as malaria recedes, fewer people carry malaria from remote endemic areas to remote areas where transmission potential remains high. Establishment of an international resource with backup capacity to control large outbreaks can make elimination stickier, increase the incentives for countries to eliminate, and ensure steady progress towards global eradication. Although available evidence supports malaria elimination's stickiness at moderate-to-low transmission in areas with well-developed health systems, it is not yet clear if such patterns will hold in all areas. The sticky endpoint changes the projected costs of maintaining elimination and makes it substantially more attractive for countries acting alone, and it makes spatially progressive elimination a sensible strategy for a malaria eradication endgame.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Qi2012,
title = {The effects of urbanization on global Plasmodium vivax malaria transmission},
author = {Qiuyin Qi and Carlos A. Guerra and Catherine L. Moyes and Iqbal AR F. Elyazar and Peter W. Gething and Simon I. Hay and Andrew J. Tatem},
url = {https://doi.org/10.1186/1475-2875-11-403},
doi = {10.1186/1475-2875-11-403},
issn = {1475-2875},
year  = {2012},
date = {2012-12-05},
journal = {Malaria Journal},
volume = {11},
number = {1},
pages = {403},
abstract = {Many recent studies have examined the impact of urbanization on Plasmodium falciparum malaria endemicity and found a general trend of reduced transmission in urban areas. However, none has examined the effect of urbanization on Plasmodium vivax malaria, which is the most widely distributed malaria species and can also cause severe clinical syndromes in humans. In this study, a set of 10,003 community-based P. vivax parasite rate (Pv PR) surveys are used to explore the relationships between Pv PR in urban and rural settings.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Pindolia2012,
title = {Human movement data for malaria control and elimination strategic planning},
author = {Deepa K. Pindolia and Andres J. Garcia and Amy Wesolowski and David L. Smith and Caroline O. Buckee and Abdisalan M. Noor and Robert W. Snow and Andrew J. Tatem},
url = {https://doi.org/10.1186/1475-2875-11-205},
doi = {10.1186/1475-2875-11-205},
issn = {1475-2875},
year  = {2012},
date = {2012-06-18},
journal = {Malaria Journal},
volume = {11},
number = {1},
pages = {205},
abstract = {Recent increases in funding for malaria control have led to the reduction in transmission in many malaria endemic countries, prompting the national control programmes of 36 malaria endemic countries to set elimination targets. Accounting for human population movement (HPM) in planning for control, elimination and post-elimination surveillance is important, as evidenced by previous elimination attempts that were undermined by the reintroduction of malaria through HPM. Strategic control and elimination planning, therefore, requires quantitative information on HPM patterns and the translation of these into parasite dispersion. HPM patterns and the risk of malaria vary substantially across spatial and temporal scales, demographic and socioeconomic sub-groups, and motivation for travel, so multiple data sets are likely required for quantification of movement. While existing studies based on mobile phone call record data combined with malaria transmission maps have begun to address within-country HPM patterns, other aspects remain poorly quantified despite their importance in accurately gauging malaria movement patterns and building control and detection strategies, such as cross-border HPM, demographic and socioeconomic stratification of HPM patterns, forms of transport, personal malaria protection and other factors that modify malaria risk. A wealth of data exist to aid filling these gaps, which, when combined with spatial data on transport infrastructure, traffic and malaria transmission, can answer relevant questions to guide strategic planning. This review aims to (i) discuss relevant types of HPM across spatial and temporal scales, (ii) document where datasets exist to quantify HPM, (iii) highlight where data gaps remain and (iv) briefly put forward methods for integrating these datasets in a Geographic Information System (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2012,
title = {Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation},
author = {Andrew J. Tatem and Susana Adamo and Nita Bharti and Clara R. Burgert and Marcia Castro and Audrey Dorelien and Gunter Fink and Catherine Linard and Mendelsohn John and Livia Montana and Mark R. Montgomery and Andrew Nelson and Abdisalan M. Noor and Deepa Pindolia and Greg Yetman and Deborah Balk},
url = {https://doi.org/10.1186/1478-7954-10-8},
doi = {10.1186/1478-7954-10-8},
issn = {1478-7954},
year  = {2012},
date = {2012-05-16},
journal = {Population Health Metrics},
volume = {10},
number = {1},
pages = {8},
abstract = {The use of Global Positioning Systems (GPS) and Geographical Information Systems (GIS) in disease surveys and reporting is becoming increasingly routine, enabling a better understanding of spatial epidemiology and the improvement of surveillance and control strategies. In turn, the greater availability of spatially referenced epidemiological data is driving the rapid expansion of disease mapping and spatial modeling methods, which are becoming increasingly detailed and sophisticated, with rigorous handling of uncertainties. This expansion has, however, not been matched by advancements in the development of spatial datasets of human population distribution that accompany disease maps or spatial models.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Linard2012,
title = {Large-scale spatial population databases in infectious disease research},
author = {Catherine Linard and Andrew J. Tatem},
url = {https://doi.org/10.1186/1476-072X-11-7},
doi = {10.1186/1476-072X-11-7},
issn = {1476-072X},
year  = {2012},
date = {2012-03-20},
journal = {International Journal of Health Geographics},
volume = {11},
number = {1},
pages = {7},
abstract = {Modelling studies on the spatial distribution and spread of infectious diseases are becoming increasingly detailed and sophisticated, with global risk mapping and epidemic modelling studies now popular. Yet, in deriving populations at risk of disease estimates, these spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data. Moreover, a variety of different methods have been used to model population distribution at large spatial scales. In this review we describe the main global gridded population datasets that are freely available for health researchers and compare their construction methods, and highlight the uncertainties inherent in these population datasets. We review their application in past studies on disease risk and dynamics, and discuss how the choice of dataset can affect results. Moreover, we highlight how the lack of contemporary, detailed and reliable data on human population distribution in low income countries is proving a barrier to obtaining accurate large-scale estimates of population at risk and constructing reliable models of disease spread, and suggest research directions required to further reduce these barriers.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{<LineBreak>doi:10.1126/science.1229045,
title = {Optimizing Investments in Malaria Treatment and Diagnosis},
author = {Justin M. Cohen and Aaron M. Woolsey and Oliver J. Sabot and Peter W. Gething and Andrew J. Tatem and Bruno Moonen},
url = {https://www.science.org/doi/abs/10.1126/science.1229045},
doi = {10.1126/science.1229045},
year  = {2012},
date = {2012-01-01},
journal = {Science},
volume = {338},
number = {6107},
pages = {612-614},
abstract = {Better targeting of antimalarials to people who need them will maximize the impact of interventions in the private sector. The Roll Back Malaria (RBM) Partnership has set an ambitious target of achieving near zero deaths from malaria by 2015 (1). Scale-up of insecticide-treated nets, indoor residual spraying of insecticide, and increased access to treatment with artemisinin-based combination therapies (ACTs) over the past decade have led to reductions in malaria incidence of more than 50% in 43 countries, including 8 in Africa (2). However, as an estimated 655,000 malaria deaths still occurred in 2010 (2), with the great majority in sub-Saharan Africa, substantial challenges remain.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{<LineBreak>doi:10.1126/science.1223467,
title = {Quantifying the Impact of Human Mobility on Malaria},
author = {Amy Wesolowski and Nathan Eagle and Andrew J. Tatem and David L. Smith and Abdisalan M. Noor and Robert W. Snow and Caroline O. Buckee},
url = {https://www.science.org/doi/abs/10.1126/science.1223467},
doi = {10.1126/science.1223467},
year  = {2012},
date = {2012-01-01},
journal = {Science},
volume = {338},
number = {6104},
pages = {267-270},
abstract = {An obstacle to developing effective national malaria control programs is a lack of understanding of human movements, which are an important component of disease transmission. As mobile phones have become increasingly ubiquitous, it is now possible to collect individual-level, longitudinal data on human movements on a massive scale. Wesolowski et al. (p. 267) analyzed mobile phone call data records representing the travel patterns of 15 million mobile phone owners in Kenya over the course of a year. This was combined with a detailed malaria risk map, to estimate malaria parasite movements across the country that could be caused by human movement. This information enabled detailed analysis of parasite sources and sinks between hundreds of local settlements. Estimates were compared with hospital data from Nairobi to show that local pockets of transmission likely occur around the periphery of Nairobi, accounting for locally acquired cases, contrary to the accepted idea that there is no transmission in the capital. Geographical information in mobile phone records for 15 million Kenyans is linked to malaria prevalence estimates. Human movements contribute to the transmission of malaria on spatial scales that exceed the limits of mosquito dispersal. Identifying the sources and sinks of imported infections due to human travel and locating high-risk sites of parasite importation could greatly improve malaria control programs. Here, we use spatially explicit mobile phone data and malaria prevalence information from Kenya to identify the dynamics of human carriers that drive parasite importation between regions. Our analysis identifies importation routes that contribute to malaria epidemiology on regional spatial scales.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0031743,
title = {Population Distribution, Settlement Patterns and Accessibility across Africa in 2010},
author = {Catherine Linard and Marius Gilbert and Robert W. Snow and Abdisalan M. Noor and Andrew J. Tatem},
url = {https://doi.org/10.1371/journal.pone.0031743},
doi = {10.1371/journal.pone.0031743},
year  = {2012},
date = {2012-01-01},
journal = {PLOS ONE},
volume = {7},
number = {2},
pages = {1-8},
publisher = {Public Library of Science},
abstract = {The spatial distribution of populations and settlements across a country and their interconnectivity and accessibility from urban areas are important for delivering healthcare, distributing resources and economic development. However, existing spatially explicit population data across Africa are generally based on outdated, low resolution input demographic data, and provide insufficient detail to quantify rural settlement patterns and, thus, accurately measure population concentration and accessibility. Here we outline approaches to developing a new high resolution population distribution dataset for Africa and analyse rural accessibility to population centers. Contemporary population count data were combined with detailed satellite-derived settlement extents to map population distributions across Africa at a finer spatial resolution than ever before. Substantial heterogeneity in settlement patterns, population concentration and spatial accessibility to major population centres is exhibited across the continent. In Africa, 90% of the population is concentrated in less than 21% of the land surface and the average per-person travel time to settlements of more than 50,000 inhabitants is around 3.5 hours, with Central and East Africa displaying the longest average travel times. The analyses highlight large inequities in access, the isolation of many rural populations and the challenges that exist between countries and regions in providing access to services. The datasets presented are freely available as part of the AfriPop project, providing an evidence base for guiding strategic decisions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Linard2011,
title = {Assessing the use of global land cover data for guiding large area population distribution modelling},
author = {Catherine Linard and Marius Gilbert and Andrew J. Tatem},
url = {https://doi.org/10.1007/s10708-010-9364-8},
doi = {10.1007/s10708-010-9364-8},
issn = {1572-9893},
year  = {2011},
date = {2011-10-01},
journal = {GeoJournal},
volume = {76},
number = {5},
pages = {525-538},
abstract = {Gridded population distribution data are finding increasing use in a wide range of fields, including resource allocation, disease burden estimation and climate change impact assessment. Land cover information can be used in combination with detailed settlement extents to redistribute aggregated census counts to improve the accuracy of national-scale gridded population data. In East Africa, such analyses have been done using regional land cover data, thus restricting application of the approach to this region. If gridded population data are to be improved across Africa, an alternative, consistent and comparable source of land cover data is required. Here these analyses were repeated for Kenya using four continent-wide land cover datasets combined with detailed settlement extents and accuracies were assessed against detailed census data. The aim was to identify the large area land cover dataset that, combined with detailed settlement extents, produce the most accurate population distribution data. The effectiveness of the population distribution modelling procedures in the absence of high resolution census data was evaluated, as was the extrapolation ability of population densities between different regions. Results showed that the use of the GlobCover dataset refined with detailed settlement extents provided significantly more accurate gridded population data compared to the use of refined AVHRR-derived, MODIS-derived and GLC2000 land cover datasets. This study supports the hypothesis that land cover information is important for improving population distribution model accuracies, particularly in countries where only coarse resolution census data are available. Obtaining high resolution census data must however remain the priority. With its higher spatial resolution and its more recent data acquisition, the GlobCover dataset was found as the most valuable resource to use in combination with detailed settlement extents for the production of gridded population datasets across large areas.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2011b,
title = {Population mapping of poor countries},
author = {Andrew Tatem and Catherine Linard},
url = {https://doi.org/10.1038/474036d},
doi = {10.1038/474036d},
issn = {1476-4687},
year  = {2011},
date = {2011-06-01},
journal = {Nature},
volume = {474},
number = {7349},
pages = {36-36},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2011,
title = {The effects of spatial population dataset choice on estimates of population at risk of disease},
author = {Andrew J. Tatem and Nicholas Campiz and Peter W. Gething and Robert W. Snow and Catherine Linard},
url = {https://doi.org/10.1186/1478-7954-9-4},
doi = {10.1186/1478-7954-9-4},
issn = {1478-7954},
year  = {2011},
date = {2011-02-07},
journal = {Population Health Metrics},
volume = {9},
number = {1},
pages = {4},
abstract = {The spatial modeling of infectious disease distributions and dynamics is increasingly being undertaken for health services planning and disease control monitoring, implementation, and evaluation. Where risks are heterogeneous in space or dependent on person-to-person transmission, spatial data on human population distributions are required to estimate infectious disease risks, burdens, and dynamics. Several different modeled human population distribution datasets are available and widely used, but the disparities among them and the implications for enumerating disease burdens and populations at risk have not been considered systematically. Here, we quantify some of these effects using global estimates of populations at risk (PAR) of P. falciparum malaria as an example.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Linard2010,
title = {A high resolution spatial population database of Somalia for disease risk mapping},
author = {Catherine Linard and Victor A. Alegana and Abdisalan M. Noor and Robert W. Snow and Andrew J. Tatem},
url = {https://doi.org/10.1186/1476-072X-9-45},
doi = {10.1186/1476-072X-9-45},
issn = {1476-072X},
year  = {2010},
date = {2010-09-14},
journal = {International Journal of Health Geographics},
volume = {9},
number = {1},
pages = {45},
abstract = {Millions of Somali have been deprived of basic health services due to the unstable political situation of their country. Attempts are being made to reconstruct the health sector, in particular to estimate the extent of infectious disease burden. However, any approach that requires the use of modelled disease rates requires reasonable information on population distribution. In a low-income country such as Somalia, population data are lacking, are of poor quality, or become outdated rapidly. Modelling methods are therefore needed for the production of contemporary and spatially detailed population data.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{doi:10.1073/pnas.1002971107,
title = {International population movements and regional \textit{Plasmodium falciparum} malaria elimination strategies},
author = {Andrew J. Tatem and David L. Smith},
url = {https://www.pnas.org/doi/abs/10.1073/pnas.1002971107},
doi = {10.1073/pnas.1002971107},
year  = {2010},
date = {2010-01-01},
journal = {Proceedings of the National Academy of Sciences},
volume = {107},
number = {27},
pages = {12222-12227},
abstract = {Calls for the eradication of malaria require the development of global and regional strategies based on a strong and consistent evidence base. Evidence from the previous global malaria eradication program and more recent transborder control campaigns have shown the importance of accounting for human movement in introducing infections to areas targeted for elimination. Here, census-based migration data were analyzed with network analysis tools, Plasmodium falciparum malaria transmission maps, and global population databases to map globally communities of countries linked by relatively high levels of infection movements. The likely principal sources and destinations of imported cases in each region were also mapped. Results indicate that certain groups of countries, such as those in West Africa and central Asia are much more strongly connected by relatively high levels of population and infection movement than others. In contrast, countries such as Ethiopia and Myanmar display significantly greater isolation in terms of likely infection movements in and out. The mapping here of both communities of countries linked by likely higher levels of infection movement, and “natural” migration boundaries that display reduced movement of people and infections between regions has practical utility. These maps can inform the design of malaria elimination strategies by identifying regional communities of countries afforded protection from recolonization by surrounding regions of reduced migration. For more isolated countries, a nationally focused control or elimination program is likely to stand a better chance of success than those receiving high levels of visitors and migrants from high-transmission regions.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2008,
title = {Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity},
author = {Andrew J. Tatem and Carlos A. Guerra and Caroline W. Kabaria and Abdisalan M. Noor and Simon I. Hay},
url = {https://doi.org/10.1186/1475-2875-7-218},
doi = {10.1186/1475-2875-7-218},
issn = {1475-2875},
year  = {2008},
date = {2008-10-27},
journal = {Malaria Journal},
volume = {7},
number = {1},
pages = {218},
abstract = {The efficient allocation of financial resources for malaria control and the optimal distribution of appropriate interventions require accurate information on the geographic distribution of malaria risk and of the human populations it affects. Low population densities in rural areas and high population densities in urban areas can influence malaria transmission substantially. Here, the Malaria Atlas Project (MAP) global database of Plasmodium falciparum parasite rate (Pf PR) surveys, medical intelligence and contemporary population surfaces are utilized to explore these relationships and other issues involved in combining malaria risk maps with those of human population distribution in order to define populations at risk more accurately.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{10.1371/journal.pone.0001298,
title = {High Resolution Population Maps for Low Income Nations: Combining Land Cover and Census in East Africa},
author = {Andrew J. Tatem and Abdisalan M. Noor and Craig Hagen and Antonio Di Gregorio and Simon I. Hay},
url = {https://doi.org/10.1371/journal.pone.0001298},
doi = {10.1371/journal.pone.0001298},
year  = {2007},
date = {2007-01-01},
journal = {PLOS ONE},
volume = {2},
number = {12},
pages = {1-8},
publisher = {Public Library of Science},
abstract = {BackgroundBetween 2005 and 2050, the human population is forecast to grow by 2.7 billion, with the vast majority of this growth occurring in low income countries. This growth is likely to have significant social, economic and environmental impacts, and make the achievement of international development goals more difficult. The measurement, monitoring and potential mitigation of these impacts require high resolution, contemporary data on human population distributions. In low income countries, however, where the changes will be concentrated, the least information on the distribution of population exists. In this paper we investigate whether satellite imagery in combination with land cover information and census data can be used to create inexpensive, high resolution and easily-updatable settlement and population distribution maps over large areas.Methodology/Principal FindingsWe examine various approaches for the production of maps of the East African region (Kenya, Uganda, Burundi, Rwanda and Tanzania) and where fine resolution census data exists, test the accuracies of map production approaches and existing population distribution products. The results show that combining high resolution census, settlement and land cover information is important in producing accurate population distribution maps.ConclusionsWe find that this semi-automated population distribution mapping at unprecedented spatial resolution produces more accurate results than existing products and can be undertaken for as little as $0.01 per km2. The resulting population maps are a product of the Malaria Atlas Project (MAP: http://www.map.ox.ac.uk) and are freely available.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{https://doi.org/10.1111/j.1365-3156.2005.01487.x,
title = {The accuracy of human population maps for public health application},
author = {S. I. Hay and A. M. Noor and A. Nelson and A. J. Tatem},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1365-3156.2005.01487.x},
doi = {https://doi.org/10.1111/j.1365-3156.2005.01487.x},
year  = {2005},
date = {2005-01-01},
journal = {Tropical Medicine & International Health},
volume = {10},
number = {10},
pages = {1073-1086},
abstract = {Summary Objectives Human population totals are used for generating burden of disease estimates at global, continental and national scales to help guide priority setting in international health financing. These exercises should be aware of the accuracy of the demographic information used. Methods The analysis presented in this paper tests the accuracy of five large-area, public-domain human population distribution data maps against high spatial resolution population census data enumerated in Kenya in 1999. We illustrate the epidemiological significance, by assessing the impact of using these different human population surfaces in determining populations at risk of various levels of climate suitability for malaria transmission. We also describe how areal weighting, pycnophylactic interpolation and accessibility potential interpolation techniques can be used to generate novel human population distribution surfaces from local census information and evaluate to what accuracy this can be achieved. Results We demonstrate which human population distribution surface performed best and which population interpolation techniques generated the most accurate bespoke distributions. Despite various levels of modelling complexity, the accuracy achieved by the different surfaces was primarily determined by the spatial resolution of the input population data. The simplest technique of areal weighting performed best. Conclusions Differences in estimates of populations at risk of malaria in Kenya of over 1 million persons can be generated by the choice of surface, highlighting the importance of these considerations in deriving per capita health metrics in public health. Despite focussing on Kenya the results of these analyses have general application and are discussed in this wider context.},
keywords = {areal weighting, census, dasymetric mapping, demography, Kenya, pycnophylactic interpolation, smart interpolation},
pubstate = {published},
tppubtype = {article}
}
@article{TATEM200587,
title = {Assessing the accuracy of satellite derived global and national urban maps in Kenya},
author = {A. J. Tatem and A. M. Noor and S. I. Hay},
url = {https://www.sciencedirect.com/science/article/pii/S0034425705000702},
doi = {https://doi.org/10.1016/j.rse.2005.02.001},
issn = {0034-4257},
year  = {2005},
date = {2005-01-01},
journal = {Remote Sensing of Environment},
volume = {96},
number = {1},
pages = {87-97},
abstract = {Ninety percent of projected global urbanization will be concentrated in low income countries. This will have considerable environmental, economic and public health implications for those populations. Objective and efficient methods of delineating urban extent are a cross-sectoral need complicated by a diversity of urban definition rubrics world-wide. Large-area maps of urban extents are becoming increasingly available in the public domain, as are a wide-range of medium spatial resolution satellite imagery. Here we describe the extension of a methodology based on Landsat ETM and Radarsat imagery to the production of a human settlement map of Kenya. This map was then compared with five satellite imagery-derived, global maps of urban extent at Kenya national-level, against an expert opinion coverage for accuracy assessment. The results showed the map produced using medium spatial resolution satellite imagery was of comparable accuracy to the expert opinion coverage. The five global urban maps exhibited a range of inaccuracies, emphasising that care should be taken with use of these maps at national and sub-national scale.},
keywords = {Accuracy assessment, Urban area mapping, Urbanization},
pubstate = {published},
tppubtype = {article}
}
@article{Tatem2004,
title = {Measuring urbanization pattern and extent for malaria research: A review of remote sensing approaches},
author = {A. J. Tatem and S. I. Hay},
url = {https://doi.org/10.1093/jurban/jth124},
doi = {10.1093/jurban/jth124},
issn = {1468-2869},
year  = {2004},
date = {2004-09-01},
journal = {Journal of Urban Health},
volume = {81},
number = {3},
pages = {363-376},
abstract = {Within the next 30 years, the proportion of urban dwellers will rise from under half to two thirds of the world's population. Such a shift will entail massive public health consequences, and most of this urban transition will occur in low-income regions of the world. Urban populations face very different health risks compared to those in rural areas, particularly in terms of malaria. To target effective and relevant public health interventions, the need for clear, consistent definitions of what determines urban areas and urban communities is paramount. Decision makers are increasingly seeing remote sensing as a cost-effective solution to monitoring urbanization at a range of spatial scales. This review focuses on the progress made within the field of remote sensing on mapping, monitoring, and modeling urban environments and examines existing challenges, drawbacks, and future prospects. We conclude by exploring' some of the particular relevance of these issues to malaria and note that they are of more general relevance to all those interested in urban public health.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
@article{TATEM200442,
title = {Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery},
author = {Andrew J. Tatem and Abdisalan M. Noor and Simon I. Hay},
url = {https://www.sciencedirect.com/science/article/pii/S0034425704001944},
doi = {https://doi.org/10.1016/j.rse.2004.06.014},
issn = {0034-4257},
year  = {2004},
date = {2004-01-01},
journal = {Remote Sensing of Environment},
volume = {93},
number = {1},
pages = {42-52},
abstract = {This paper presents an appraisal of satellite imagery types and texture measures for identifying and delineating settlements in four Districts of Kenya chosen to represent the variation in human ecology across the country. Landsat Thematic Mapper (TM) and Japanese Earth Resources Satellite-1 (JERS-1) synthetic aperture radar (SAR) imagery of the four districts were obtained and supervised per-pixel classifications of image combinations tested for their efficacy at settlement delineation. Additional data layers including human population census data, land cover, and locations of medical facilities, villages, schools and market centres were used for training site identification and validation. For each district, the most accurate approach was determined through the best correspondence with known settlement and non-settlement pixels. The resulting settlement maps will be used in combination with census data to produce medium spatial resolution population maps for improved public health planning in Kenya.},
keywords = {JERS-1 SAR, Kenya, Landsat TM, Neural network, Population, Public health, Settlement mapping, Texture},
pubstate = {published},
tppubtype = {article}
}