@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}

}