@article{nokey,

title = {A stochastic approach to integerize floating-point estimates in gridded population mapping},

author = {Wenbin Zhang and Alessandro Sorichetta and Charlie Frye and Natalia Tejedor-Garavito and Weixuan Fang and Duygu Cihan and Dorothea Woods and Gregory Yetman and Jason Hilton and Andrew J. Tatem and Maksym Bondarenko},

url = {https://doi.org/10.1080/13658816.2025.2568721},

doi = {10.1080/13658816.2025.2568721},

year  = {2025},

date = {2025-10-01},

journal = {International Journal of Geographical Information Science},

pages = {1–17},

abstract = {Gridded population datasets are increasingly relied upon for spatial planning, resource allocation, and disaster response due to their flexible integration with other spatial data layers. These datasets are typically produced by disaggregating population counts from administrative units into grid cells, yielding non-integer values that preserve overall counts. However, floating-point cell values are often difficult for users to interpret, and standard rounding approaches may introduce aggregation errors at administrative levels that affect planning decisions. Here, we present a stochastic integerisation method that preserves total population and demographic proportions, and compare it with existing approaches. The method separates the value of each cell into integer and decimal parts, and probabilistically allocates the residual based on decimal magnitudes. Applying the method to gridded population data shows that it effectively reduces unrealistic population predictions in uninhabited areas. The results also demonstrate that the new integerisation method can effectively convert floating-point population estimates into integers while preserving both spatial distribution and demographic proportions, such as age-sex structures. These findings highlight the performance of the proposed integerisation method to generate reliable gridded population distribution datasets across diverse contexts that are easier to interpret, particularly for areas with sparse populations or complex geometries of underlying administrative units.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Tackling public health data gaps through Bayesian high-resolution population estimation: A case study of Kasaï-Oriental, Democratic Republic of the Congo},

author = {Gianluca Boo and Edith Darin and Heather R. Chamberlain and Roland Hosner and Pierre K. Akilimali and Henri Marie Kazadi and Chibuzor C. Nnanatu and Attila N. Lázár and Andrew J. Tatem},

url = {https://doi.org/10.1371/journal.pgph.0005072},

year  = {2025},

date = {2025-09-04},

journal = {PLOS Global Public Health },

abstract = {Most low- and middle-income countries face significant public health challenges, exacerbated by the lack of reliable demographic data supporting effective planning and intervention. In such data-scarce settings, statistical models combining geolocated survey data with geospatial datasets enable the estimation of population counts at high spatial resolution in the absence of dependable demographic data sources. This study introduces a Bayesian model jointly estimating building and population counts, combining geolocated survey data and gridded geospatial datasets. The model provides population estimates for the Kasaï-Oriental province, Democratic Republic of the Congo (DRC), at a spatial resolution of approximately one hectare. Posterior estimates are aggregated across health zones and health areas to offer probabilistic insights into their respective populations. The model exhibits a –0.28 bias, 0.47 inaccuracy, and 0.95 imprecision using scaled residuals, with robust 95% credible intervals. The estimated population of Kasaï-Oriental for 2024 is approximately 4.1 million, with a credible range of 3.4 to 4.8 million. Aggregations by health zones and health areas reveal significant variations in population estimates and uncertainty levels, particularly between the provincial capital, Mbuji-Mayi and the rural hinterland. High-resolution Bayesian population estimates allow flexible aggregation across spatial units while providing probabilistic insights into model uncertainty. These estimates offer a unique resource for the public health community working in Kasaï-Oriental, for instance, in support of a better-informed allocation of vaccines to different operational boundaries based on the upper bound of the 95% credible intervals.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geostatistical and machine learning approaches for high-resolution mapping of vaccination coverage},

author = {C. Edson Utazi and Ortis Yankey and Somnath Chaudhuri and Iyanuloluwa D. Olowe and M. Carolina Danovaro-Holliday and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/10.1016/j.sste.2025.100744},

year  = {2025},

date = {2025-08-23},

journal = {Spatial and Spatio-temporal Epidemiology},

volume = {54},

number = {100744},

abstract = {Recently, there has been a growing interest in the production of high-resolution maps of vaccination coverage. These maps have been useful for uncovering geographic inequities in coverage and improving targeting of interventions to reach marginalized populations. Different methodological approaches have been developed for producing these maps using mostly geolocated household survey data and geospatial covariate information. However, it remains unclear how much the predicted coverage maps produced by the various methods differ, and which methods yield more reliable estimates. Here, we explore the predictive performance of these methods and resulting implications for spatial prioritization to fill this gap. Using Nigeria Demographic and Health Survey as a case study, we generate 1 × 1 km and district level maps of indicators of vaccination coverage using geostatistical, machine learning (ML) and hybrid methods and evaluate predictive performance via cross-validation. Our results show similar predictive performance for five of the seven methods investigated, although two geostatistical approaches are the best performing methods. The worst-performing methods are two ML approaches. We find marked differences in spatial prioritization using these methods, which could potentially result in missing important underserved populations, although broad similarities exist. Our study can help guide map production for other health and development metrics.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{amouzou20252025,

title = {The 2025 report of the Lancet Countdown to 2030 for women's, children's, and adolescents' health: tracking progress on health and nutrition},

author = {Agbessi Amouzou and Aluisio J D Barros and Jennifer Requejo and Cheikh Faye and Nadia Akseer and Eran Bendavid and Cauane Blumenberg and Josephine Borghi and Sama El Baz and Frederik Federspiel and Leonardo Z Ferreira and Elizabeth Hazel and Sam Heft-Neal and Franciele Hellwig and Li Liu and Abdoulaye Maïga and Melinda Munos and Catherine Pitt and Yusra Ribhi Shawar and Jeremy Shiffman and Yvonne Tam and Neff Walker and Pierre Akilimali and Leontine Alkema and Paoli Behanzin and Peter Binyaruka and Zulfiqar Bhutta and Andrea Blanchard and Hannah Blencowe and Ellen Bradley and Nouria Brikci and Beatriz Caicedo-Velásquez and Anthony Costello and Winfred Dotse-Gborgbortsi and Shams El Arifeen and Majid Ezzati and Lynn P Freedman and Michel Guillot and Claudia Hanson and Rebecca Heidkamp and Luis Huicho and Chimaraoke Izugbara and Safia S Jiwani and Caroline Kabiru and Helen Kiarie and Mary Kinney and Fati Kirakoya-Samadoulougou and Joy Lawn and Nyovani Madise and Gouda Roland Mesmer Mady and Bruno Masquelier and Dessalegn Melesse and Kristine Nilsen and Jamie Perin and Usha Ram and Marina Romanello and Ghada E Saad and Sudha Sharma and Estelle M Sidze and Paul Spiegel and Hannah Tappis and Andrew J Tatem and and others},

doi = {10.1016/S0140-6736(25)00151-5},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {The Lancet},

volume = {405},

number = {10488},

issue = {10488},

pages = {1505–1554},

publisher = {Elsevier},

abstract = {In line with previous progress reports by Countdown to 2030 for Women's, Children's, and Adolescents' Health, this report analyses global and regional trends and inequalities in health determinants, survival, nutritional status, intervention coverage, and quality of care in reproductive, maternal, newborn, child and adolescent health (RMNCAH) and nutrition, as well as country health systems, policies, financing, and prioritisation. The focus is on low-income and middle-income countries (LMICs) where 99% of maternal deaths and 98% of child and adolescent deaths (individuals aged 0–19 years) occur, with special attention to sub-Saharan Africa and South Asia.

Recognising the urgency of reaching the Sustainable Development Goal (SDG) for health, SDG 3, and health-related targets by 2030, the report assesses whether the momentum needed to reach these goals has been sustained, accelerated, stagnated, or regressed in comparison with the Millennium Development Goal (MDG) period (2000–15). Although most health and health-related indicators continue to show progress, there has been a notable slowdown in the rate of improvement after 2015, falling well short of the pace needed to achieve the 2030 SDG targets. This deceleration in pace contrasts sharply with the aspired grand convergence in health, characterised by drastic reductions in mortality and RMNCAH inequalities, which was expected to occur during the SDG period based on the assumption that the spectacular progress achieved during the MDG period would continue unabated. Multiple threats, external and internal to the RMNCAH health community, must be addressed to safeguard the gains in RMNCAH and nutrition and to accelerate progress. Furthermore, a large gap between sub-Saharan Africa, especially West and Central Africa, and other parts of the world persists for many indicators, necessitating further prioritisation of this region.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{seidler2025subnational,

title = {Subnational variations in the quality of household survey data in sub-Saharan Africa},

author = {Valentin Seidler and Edson C Utazi and Amelia B Finaret and Sebastian Luckeneder and Gregor Zens and Maksym Bodarenko and Abigail W Smith and Sarah EK Bradley and Andrew J Tatem and Patrick Webb},

url = {https://doi.org/10.1038/s41467-025-58776-5},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {Nature Communications},

volume = {16},

number = {1},

pages = {3771},

publisher = {Nature Publishing Group UK London},

abstract = {Nationally representative household surveys collect geocoded data that are vital to tackling health and other development challenges in sub-Saharan Africa. Scholars and practitioners generally assume uniform data quality but subnational variation of errors in household data has never been investigated at high spatial resolution. Here, we explore within-country variation in the quality of most recent household surveys for 35 African countries at 5 × 5 km resolution and district levels. Findings show a striking heterogeneity in the subnational distribution of sampling and measurement errors. Data quality degrades with greater distance from settlements, and missing data as well as imprecision of estimates add to quality problems that can result in vulnerable remote populations receiving less than optimal services and needed resources. Our easy-to-access geospatial estimates of survey data quality highlight the need to invest in better targeting of household surveys in remote areas.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{chamberlain2025assessing,

title = {Assessing the impact of building footprint dataset choice for health programme planning: a case study of indoor residual spraying (IRS) in Zambia},

author = {Heather R Chamberlain and Derek Pollard and Anna Winters and Silvia Renn and Olena Borkovska and Chisenga Abel Musuka and Garikai Membele and Attila N Lazar and Andrew J Tatem},

url = {https://doi.org/10.1186/s12942-025-00398-7},

year  = {2025},

date = {2025-01-01},

urldate = {2025-01-01},

journal = {International Journal of Health Geographics},

volume = {24},

number = {1},

pages = {13},

publisher = {Springer},

abstract = {The increasing availability globally of building footprint datasets has brought new opportunities to support a geographic approach to health programme planning. This is particularly acute in settings with high disease burdens but limited geospatial data available to support targeted planning. The comparability of building footprint datasets has recently started to be explored, but the impact of utilising a particular dataset in analyses to support decision making for health programme planning has not been studied. In this study, we quantify the impact of utilising four different building footprint datasets in analyses to support health programme planning, with an example of malaria vector control initiatives in Zambia.

Using the example of planning indoor residual spraying (IRS) campaigns in Zambia, we identify priority locations for deployment of this intervention based on criteria related to the area, proximity and counts of building footprints per settlement. We apply the same criteria to four different building footprint datasets and quantify the count and geographic variability in the priority settlements that are identified.

We show that nationally the count of potential priority settlements for IRS varies by over 230% with different building footprint datasets, considering a minimum threshold of 25 sprayable buildings per settlement. Differences are most pronounced for rural settlements, indicating that the choice of dataset may bias the selection to include or exclude settlements, and consequently population groups, in some areas.

The results of this study show that the choice of building footprint dataset can have a considerable impact on the potential settlements identified for IRS, in terms of (i) their location and count, and (ii) the count of building footprints within priority settlements. The choice of dataset potentially has substantial implications for campaign planning, implementation and coverage assessment. Given the magnitude of the differences observed, further work should more broadly assess the sensitivity of health programme planning metrics to different building footprint datasets, and across a range of geographic contexts and health campaign types.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {A systematic review of using population-level human mobility data to understand SARS-CoV-2 transmission},

author = {Natalya Kostandova and Catherine Schluth and Rohan Arambepola and Fatumah Atuhaire and Sophie Bérubé and Taylor Chin and Eimear Cleary and Oscar Cortes-Azuero and Bernardo García-Carreras and Kyra H. Grantz and Matt D. T. Hitchings and Angkana T. Huang and Nishant Kishore and Shengjie Lai and Soren L. Larsen and Stacie Loisate and Pamela Martinez and Hannah R. Meredith and Ritika Purbey and Tanjona Ramiadantsoa and Jonathan Read and Benjamin L. Rice and Lori Rosman and Nick Ruktanonchai and Henrik Salje and Kathryn L. Schaber and Andrew J. Tatem and Jasmine Wang and Derek A. T. Cummings and Amy Wesolowski },

url = {https://doi.org/10.1038/s41467-024-54895-7},

year  = {2024},

date = {2024-12-03},

journal = {Nature Communications},

volume = {15},

number = {10504},

abstract = {The emergence of SARS-CoV-2 into a highly susceptible global population was primarily driven by human mobility-induced introduction events. Especially in the early stages, understanding mobility was vital to mitigating the pandemic prior to widespread vaccine availability. We conducted a systematic review of studies published from January 1, 2020, to May 9, 2021, that used population-level human mobility data to understand SARS-CoV-2 transmission. Of the 5505 papers with abstracts screened, 232 were included in the analysis. These papers focused on a range of specific questions but were dominated by analyses focusing on the USA and China. The majority included mobile phone data, followed by Google Community Mobility Reports, and few included any adjustments to account for potential biases in population sampling processes. There was no clear relationship between methods used to integrate mobility and SARS-CoV-2 data and goals of analysis. When considering papers focused only on the estimation of the effective reproductive number within the US, there was no clear relationship identified between this measure and changes in mobility patterns. Our findings underscore the need for standardized, systematic ways to identify the source of mobility data, select an appropriate approach to using it in analysis, and reporting.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geospatial Variation in Vaccination Coverage and Zero-Dose Prevalence at the District, Ward and Health Facility Levels Before and After a Measles Vaccination Campaign in Nigeria},

author = {C. Edson Utazi and Iyanuloluwa D. Olowe and H. M. Theophilus Chan and Winfred Dotse-Gborgbortsi and John Wagai and Jamila A. Umar and Sulaiman Etamesor and Brian Atuhaire and Biyi Fafunmi and Jessica Crawford and Adeyemi Adeniran and Andrew J. Tatem},

url = {https://doi.org/10.3390/vaccines12121299},

year  = {2024},

date = {2024-11-21},

journal = {Vaccines},

volume = {12},

number = {1299},

abstract = {Many measles endemic countries with suboptimal coverage levels still rely on vaccination campaigns to fill immunity gaps and boost control efforts. Depending on local epidemiological patterns, national or targeted campaigns are implemented, following which post-campaign coverage surveys (PCCSs) are conducted to evaluate their performance, particularly in terms of reaching previously unvaccinated children. Due to limited resources, PCCS surveys are designed to be representative at coarse spatial scales, often masking important heterogeneities in coverage that could enhance the identification of areas of poor performance for follow-up via routine immunization strategies. Here, we undertake geospatial analyses of the 2021 measles PCCS in Nigeria to map indicators of coverage measuring the individual and combined performance of the campaign and routine immunization (RI) at 1 × 1 km resolution and the ward and district levels in 13 states. Using additional geospatial datasets, we also produced estimates of numbers of unvaccinated children during the campaign and numbers of measles-containing vaccine (MCV) zero-dose children before and after the campaign at these levels and within health facility catchment areas. Our study revealed that although the campaign reduced the numbers of MCV zero-dose children in all the districts, areas of suboptimal campaign and RI performance with considerable numbers of zero-dose children remained. Our analyses further identified wards and health facility catchment areas with higher numbers of unvaccinated children within these areas. Our outputs provide a robust evidence base to plan and implement follow-up RI strategies and to guide future campaigns at flexible and operationally relevant spatial scales.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Disaggregating census data for population mapping using a Bayesian Additive Regression Tree model},

author = {Ortis Yankey and Chigozie E. Utazi and Christopher C. Nnanatu and Assane N. Gadiaga and Thomas Abbot and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/10.1016/j.apgeog.2024.103416},

year  = {2024},

date = {2024-09-14},

journal = {Applied Geography},

volume = {174},

abstract = {Population data is crucial for policy decisions, but fine-scale population numbers are often lacking due to the challenge of sharing sensitive data. Different approaches, such as the use of the Random Forest (RF) model, have been used to disaggregate census data from higher administrative units to small area scales. A major limitation of the RF model is its inability to quantify the uncertainties associated with the predicted populations, which can be important for policy decisions. In this study, we applied a Bayesian Additive Regression Tree (BART) model for population disaggregation and compared the result with a RF model using both simulated data and the 2021 census data for Ghana. The BART model consistently outperforms the RF model in out-of-sample predictions for all metrics, such as bias, mean squared error (MSE), and root mean squared error (RMSE). The BART model also addresses the limitations of the RF model by providing uncertainty estimates around the predicted population, which is often lacking with the RF model. Overall, the study demonstrates the superiority of the BART model over the RF model in disaggregating population data and highlights its potential for gridded population estimates.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Identifying counter-urbanisation using Facebook's user count data},

author = {Qianwen Duan and Jessica Steele and Zhifeng Cheng and Eimear Cleary and Nick Ruktanonchai and Hal Voepel and Tim O'Riordan and Andrew J. Tatem and Alessandro Sorichetta and Shengjie Lai and Felix Eigenbrod},

url = {https://doi.org/10.1016/j.habitatint.2024.103113},

doi = {10.1016/j.habitatint.2024.103113},

year  = {2024},

date = {2024-06-04},

journal = {Habitat International},

volume = {150},

abstract = {Identifying the growing widespread phenomenon of counter-urbanisation, where people relocate from urban centres to rural areas, is essential for understanding the social and ecological consequences of the associated changes. However, its nuanced dynamics and complex characteristics pose challenges for quantitative analysis. Here, we used near real-time Facebook user count data for Belgium and Thailand, with missing data imputed, and applied the Seasonal-Trend decomposition using Loess (STL) model to capture subtle urban and rural population dynamics and assess counter-urbanisation. We identified counter-urbanisation in both Belgium and Thailand, evidenced by increases of 1.80% and 2.14% in rural residents (night-time user counts) and decreases of 3.08% and 5.04% in urban centre night-time user counts from March 2020 to May 2022, respectively. However, the counter-urbanisation in Thailand appears to be transitory, with rural users beginning to decline during both day and night as COVID-19 restrictions were lifted. By contrast, in Belgium, at the country level, there is as yet no evidence of a return to urban residences, though daytime numbers in rural areas are decreasing and in urban centres are increasing, suggesting an increase in commuting post-pandemic. These variation characteristics observed both between Belgium and Thailand and between day and night, extend the current understanding of counter-urbanisation. The use of novel social media data provides an effective quantitative perspective to comprehend counter-urbanisation in different settings.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Building footprint data for countries in Africa: To what extent are existing data products comparable?},

author = {Heather R. Chamberlain and Edith Darin and Wole Ademola Adewole and Warren C. Jochem and Attila N. Lazar and Andrew J. Tatem},

url = {https://doi.org/10.1016/j.compenvurbsys.2024.102104},

doi = {10.1016/j.compenvurbsys.2024.102104},

year  = {2024},

date = {2024-03-22},

journal = {Computers, Environment and Urban Systems},

volume = {110},

abstract = {Growth and developments in computing power, machine-learning algorithms and satellite imagery spatiotemporal resolution have led to rapid developments in automated feature-extraction. These methods have been applied to create geospatial datasets of features such as roads, trees and building footprints, at a range of spatial scales, with national and multi-country datasets now available as open data from multiple sources. Building footprint data is particularly useful in a range of applications including mapping population distributions, planning resource distribution campaigns and in humanitarian response. In settings with well-developed geospatial data systems, such datasets may complement existing authoritative sources, but in data-scarce settings, they may be the only source of data. However, knowledge on the degree to which building footprint data products are comparable and can be used interchangeably, and the impact of selecting a particular dataset on subsequent analyses remains limited. For all countries in Africa, we review the available multi-country building footprint data products and analyse their similarities and differences in terms of building area and count metrics. We explore the variation between building footprint data products across a range of spatial scales, including sub-national administrative units and different settlement types. Our results show that the available building footprint data products are not interchangeable. There are clear differences in counts and total area of building footprints between the assessed data products, as well as considerable spatial heterogeneity in building footprint coverage and completeness.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Geospatial Analyses of Recent Household Surveys to Assess Changes in the Distribution of Zero-Dose Children and Their Associated Factors before and during the COVID-19 Pandemic in Nigeria},

author = {Justice Moses K. Aheto and Iyanuloluwa Deborah Olowe and Ho Man Theophilus Chan and Adachi Ekeh and Boubacar Dieng and Biyi Fafunmi and Hamidreza Setayesh and Brian Atuhaire and Jessica Crawford and Andrew J. Tatem and Chigozie Edson Utazi},

url = {https://doi.org/10.3390/vaccines11121830},

doi = {10.3390/vaccines11121830 },

year  = {2023},

date = {2023-12-08},

journal = {Vaccines},

volume = {11},

issue = {12},

abstract = {The persistence of geographic inequities in vaccination coverage often evidences the presence of zero-dose and missed communities and their vulnerabilities to vaccine-preventable diseases. These inequities were exacerbated in many places during the coronavirus disease 2019 (COVID-19) pandemic, due to severe disruptions to vaccination services. Understanding changes in zero-dose prevalence and its associated risk factors in the context of the COVID-19 pandemic is, therefore, critical to designing effective strategies to reach vulnerable populations. Using data from nationally representative household surveys conducted before the COVID-19 pandemic, in 2018, and during the pandemic, in 2021, in Nigeria, we fitted Bayesian geostatistical models to map the distribution of three vaccination coverage indicators: receipt of the first dose of diphtheria-tetanus-pertussis-containing vaccine (DTP1), the first dose of measles-containing vaccine (MCV1), and any of the four basic vaccines (bacilli Calmette-Guerin (BCG), oral polio vaccine (OPV0), DTP1, and MCV1), and the corresponding zero-dose estimates independently at a 1 × 1 km resolution and the district level during both time periods. We also explored changes in the factors associated with non-vaccination at the national and regional levels using multilevel logistic regression models. Our results revealed no increases in zero-dose prevalence due to the pandemic at the national level, although considerable increases were observed in a few districts. We found substantial subnational heterogeneities in vaccination coverage and zero-dose prevalence both before and during the pandemic, showing broadly similar patterns in both time periods. Areas with relatively higher zero-dose prevalence occurred mostly in the north and a few places in the south in both time periods. We also found consistent areas of low coverage and high zero-dose prevalence using all three zero-dose indicators, revealing the areas in greatest need. At the national level, risk factors related to socioeconomic/demographic status (e.g., maternal education), maternal access to and utilization of health services, and remoteness were strongly associated with the odds of being zero dose in both time periods, while those related to communication were mostly relevant before the pandemic. These associations were also supported at the regional level, but we additionally identified risk factors specific to zero-dose children in each region; for example, communication and cross-border migration in the northwest. Our findings can help guide tailored strategies to reduce zero-dose prevalence and boost coverage levels in Nigeria.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{nokey,

title = {Impact of Urban Slum Residence on Coverage of Maternal, Neonatal, and Child Health Service Indicators in the Greater Accra Region of Ghana: an Ecological Time-Series Analysis, 2018-2021},

author = {Duah Dwomoh and Samuel Iddi and Seth Kwaku Afagbedzi and Natalia Tejedor-Garavito and Winfred Dotse-Gborgbortsi and Jim Wright and Andrew J Tatem and Kristine Nilsen },

url = {https://doi.org/10.1007/s11524-023-00812-0

},

doi = {10.1007/s11524-023-00812-0},

year  = {2023},

date = {2023-11-16},

journal = {Journal of Urban Health},

abstract = {Among other focus areas, the global Sustainable Development Goals (SDGs) 3 and 11 seek to advance progress toward universal coverage of maternal, neonatal, and child health (MNCH) services and access to safe and affordable housing and basic services by 2030. Governments and development agencies have historically neglected the health and well-being associated with living in urban slums across major capital cities in sub-Saharan Africa since health policies and programs have tended to focus on people living in rural communities. This study assessed the trends and compared inequities in MNCH service utilization between slum and non-slum districts in the Greater Accra region of Ghana. It analyzed information from 29 districts using monthly time-series Health Management Information System (HMIS) data on MNCH service utilization between January 2018 and December 2021. Multivariable quantile regression models with robust standard errors were used to quantify the impact of urban slum residence on MNCH service utilization. We assessed the inequality of MNCH coverage indicators between slum and non-slum districts using the Gini index with bootstrapped standard errors and the generalized Lorenz curve. The results indicate that rates of vaccination coverage and antenatal care (ANC) attendance have declined significantly in slum districts compared to those in non-slum districts. However, skilled birth delivery and postnatal care (PNC) were found to be higher in urban slum areas compared to those in non-urban slum areas. To help achieve the SDGs’ targets, it is important for the government of Ghana and other relevant stakeholders to prioritize the implementation of effective policies, programs, and interventions that will improve access to and utilization of ANC and immunization services among urban slum dwellers.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@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 = {A high-resolution daily global dataset of statistically downscaled CMIP6 models for climate impact analyses},

author = {Solomon Gebrechorkos and Julian Leyland and Louise Slater and Michel Wortmann and Philip J. Ashworth and Georgina L. Bennett and Richard Boothroyd and Hannah Cloke and Pauline Delorme and Helen Griffith and Richard Hardy and Laurence Hawker and Stuart McLelland and Jeffrey Neal and Andrew Nicholas and Andrew J. Tatem and Ellie Vahidi and Daniel R. Parsons and Stephen E. Darby},

url = {https://doi.org/10.1038/s41597-023-02528-x

},

doi = {10.1038/s41597-023-02528-x},

year  = {2023},

date = {2023-09-11},

urldate = {2023-09-11},

journal = {Scientific Data},

number = {611},

issue = {10},

abstract = {A large number of historical simulations and future climate projections are available from Global Climate Models, but these are typically of coarse resolution, which limits their effectiveness for assessing local scale changes in climate and attendant impacts. Here, we use a novel statistical downscaling model capable of replicating extreme events, the Bias Correction Constructed Analogues with Quantile mapping reordering (BCCAQ), to downscale daily precipitation, air-temperature, maximum and minimum temperature, wind speed, air pressure, and relative humidity from 18 GCMs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). BCCAQ is calibrated using high-resolution reference datasets and showed a good performance in removing bias from GCMs and reproducing extreme events. The globally downscaled data are available at the Centre for Environmental Data Analysis (https://doi.org/10.5285/c107618f1db34801bb88a1e927b82317) for the historical (1981–2014) and future (2015–2100) periods at 0.25° resolution and at daily time step across three Shared Socioeconomic Pathways (SSP2-4.5, SSP5-3.4-OS and SSP5-8.5). This new climate dataset will be useful for assessing future changes and variability in climate and for driving high-resolution impact assessment models.},

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 = {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}

}