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    Jessica E. Steele, Pål Roe Sundsøy, Carla Pezzulo, Victor A. Alegana, Tomas J. Bird, Joshua Blumenstock, Johannes Bjelland, Kenth Engø-Monsen, Yves-Alexandre de Montjoye, Asif M. Iqbal, Khandakar N. Hadiuzzaman, Xin Lu, Erik Wetter, Andrew J. Tatem, Linus Bengtsson. Mapping poverty using mobile phone and satellite data. Journal of The Royal Society Interface, February 2017 Volume 14, issue 127. DOI: 10.1098/rsif.2016.0690

    BACKGROUND: 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poverty rely heavily on census data, which in most low- and middle-income countries (LMICs) are unavailable or out-of-date.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.

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    Christopher T. Lloyd, Alessandro Sorichetta & Andrew J. Tatem. High resolution global gridded data for use in population studies. Scientific Data 4, Article number: 170001 (2017) doi:10.1038/sdata.2017.1

    BACKGROUND: 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.

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    Andrew J. Tatem. WorldPop, open data for spatial demography. Scientific Data 4, Article number: 170004 (2017) doi:10.1038/sdata.2017.4

    BACKGROUND: 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.

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    Catherine Linard, Caroline W. Kabaria, Marius Gilbert, Andrew J. Tatem, Andrea E. Gaughan, Forrest R. Stevens, Alessandro Sorichetta, Abdisalan M. Noor & Robert W. Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009. Published online: 11 Jan 2017. DOI: 10.1080/17538947.2016.1275829

    BACKGROUND: Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields.

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    Caroline O. Buckee, Andrew J. Tatem, C. Jessica E. Metcalf. Seasonal Population Movements and the Surveillance and Control of Infectious Diseases. (In Press Corrected Proof) November 16, 2016. DOI: 10.1016/j.pt.2016.10.006

    BACKGROUND: 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.

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    Elisabeth zu Erbach-Schoenberg, Victor A. Alegana, Alessandro Sorichetta, Catherine Linard, Christoper Lourenço, Nick W. Ruktanonchai, Bonita Graupe, Tomas J. Bird, Carla Pezzulo, Amy Wesolowski and Andrew J. Tatem. Dynamic denominators: the impact of seasonally varying population numbers on disease incidence estimates. Population Health MetricsAdvancing innovation in health measurement201614:35. DOI: 10.1186/s12963-016-0106-0

    BACKGROUND: 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.

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    Nita Bharti, Ali Djibo, Andrew J. Tatem, Bryan T. Grenfell & Matthew J. Ferrari. Measuring populations to improve vaccination coverage. Scientific Reports 5, Article number: 34541 (2016). doi:10.1038/srep34541

    BACKGROUND: 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.

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    Corrine W. Ruktanonchai, Nick W. Ruktanonchai, Andrea Nove, Sofia Lopes, Carla Pezzulo, Claudio Bosco, Victor A. Alegana, Clara R. Burgert, Rogers Ayiko, Andrew SEK Charles, Nkurunziza Lambert, Esther Msechu, Esther Kathini, Zoë Matthews, Andrew J. Tatem. Equality in Maternal and Newborn Health: Modelling Geographic Disparities in Utilisation of Care in Five East African Countries. PLoS ONE 11(8): e0162006. doi: 10.1371/journal.pone.0162006

    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.

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    Sarah Neal, Corrine Ruktanonchai, Venkatraman Chandra-Mouli, Zoë Matthews and Andrew J. Tatem. Mapping adolescent first births within three east African countries using data from Demographic and Health Surveys: exploring geospatial methods to inform policy. Reproductive Health201613:98. DOI: 10.1186/s12978-016-0205-1

    BACKGROUND: 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.

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    Alessandro Sorichetta, Tom J. Bird, Nick W. Ruktanonchai, Elisabeth zu Erbach-Schoenberg, Carla Pezzulo, Natalia Tejedor, Ian C. Waldock, Jason D. Sadler, Andres J. Garcia, Luigi Sedda & Andrew J. Tatem. Mapping internal connectivity through human migration in malaria endemic countries Scientific Data 3, Article number: 160066 (2016). doi:10.1038/sdata.2016.66

    BACKGROUND: 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.

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    Xin Lu, David J. Wrathall, Pål Roe Sundsøy, Md. Nadiruzzaman, Erik Wetter, Asif Iqbal, Taimur Qureshi, Andrew J. Tatem, Geoffrey S. Canright, Kenth Engø-Monsen, Linus Bengtsson. Detecting climate adaptation with mobile network data in Bangladesh: anomalies in communication, mobility and consumption patterns during cyclone Mahasen Climatic Change (2016). DOI: 10.1007/s10584-016-1753-7

    BACKGROUND: 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 < 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.

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    C. Edson Utazi, Sujit K. Sahu, Peter M. Atkinson, Natalia Tejedor, Andrew J. Tatem. A probabilistic predictive Bayesian approach for determining the representativeness of health and demographic surveillance networks Spatial Statistics 17 (2016) 161–178. doi:10.1016/j.spasta.2016.05.006

    BACKGROUND: 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.

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    Victor A. Alegana, Peter M. Atkinson, Christopher Lourenço, Nick W. Ruktanonchai, Claudio Bosco, Elisabeth zu Erbach-Schoenberg, Bradley Didier, Deepa Pindolia, Arnaud Le Menach, Stark Katokele, Petrina Uusiku & Andrew J. Tatem. Advances in mapping malaria for elimination: fine resolution modelling of Plasmodium falciparum incidence. Scientific Reports 6, Article number: 29628 (2016). doi:10.1038/srep29628

    BACKGROUND: 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.

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    Nirav N. Patel, Forrest R. Stevens, Zhuojie Huang, Andrea E. Gaughan, Iqbal Elyazar, Andrew J. Tatem. Improving Large Area Population Mapping Using Geotweet Densities. Trans. in GIS. (2016). doi:10.1111/tgis.12214

    BACKGROUND: 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.

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    Nick W. Ruktanonchai, Patrick DeLeenheer, Andrew J. Tatem, Victor A. Alegana, T. Trevor Caughlin, Elisabeth zu Erbach-Schoenberg, Christopher Lourenço, Corrine W. Ruktanonchai, David L. Smith. Identifying Malaria Transmission Foci for Elimination Using Human Mobility Data PLoS Comput Biol 12(4): e1004846. http://dx.doi.org/10.1371/journal.pcbi.1004846

    BACKGROUND: 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.

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    Nick W. Ruktanonchai, Darlene Bhavnani, Alessandro Sorichetta, Linus Bengtsson, Keith H. Carter, Roberto C. Córdoba, Arnaud Le Menach, Xin Lu, Erik Wetter, Elisabeth zu Erbach-Schoenberg and Andrew J. Tatem. Census-derived migration data as a tool for informing malaria elimination policy Malaria Journal201615:273 DOI: 10.1186/s12936-016-1315-5

    BACKGROUND: 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.

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    Andrea E. Gaughan, Forrest R. Stevens, Zhuojie Huang, Jeremiah J. Nieves, Alessandro Sorichetta, Shengjie Lai, Xinyue Ye, Catherine Linard, Graeme M. Hornby, Simon I. Hay, Hongjie Yu & Andrew J. Tatem. Spatiotemporal patterns of population in mainland China, 1990 to 2010. Scientific Data 3, Article number: 160005 (2016) DOI: 10.1038/sdata.2016.5

    BACKGROUND: 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 ~18% 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 (~100 m) 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.

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    Xin Lu,David J. Wrathall,Pål Roe Sundsøy,Md. Nadiruzzaman,Erik Wetter,Asif Iqbal,Taimur Qureshi,Andrew Tatem,Geoffrey Canright,Kenth Engø-Monsen,Linus Bengtsson. Unveiling hidden migration and mobility patterns in climate stressed regions: A longitudinal study of six million anonymous mobile phone users in Bangladesh. Global Environmental Change May 2016, doi:10.1016/j.gloenvcha.2016.02.002

    BACKGROUND: 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.

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    Robin Wilson, Elisabeth zu Erbach-Schoenberg, Maximilian Albert, Daniel Power, Simon Tudge, Miguel Gonzalez, Sam Guthrie, Heather Chamberlain, Christopher Brooks, Christopher Hughes, Lenka Pitonakova, Caroline Buckee, Xin Lu, Erik Wetter, Andrew Tatem, Linus Bengtsson. Rapid and near Real-time Assessments of Population Displacement Using Mobile Phone Data Following Disasters: The 2015 Nepal Earthquake. PLOS Currents Disasters. 2016 Feb 24 . Edition 1. doi: 10.1371/currents.dis.d073fbece328e4c39087bc086d694b5c.

    Introduction: Sudden impact disasters often result in the displacement of large numbers of people. These movements can occur prior to events, due to early warning messages, or take place post-event due to damages to shelters and livelihoods as well as a result of long-term reconstruction efforts. Displaced populations are especially vulnerable and often in need of support. However, timely and accurate data on the numbers and destinations of displaced populations are extremely challenging to collect across temporal and spatial scales, especially in the aftermath of disasters. Mobile phone call detail records were shown to be a valid data source for estimates of population movements after the 2010 Haiti earthquake, but their potential to provide near real-time ongoing measurements of population displacements immediately after a natural disaster has not been demonstrated.

    Methods: A computational architecture and analytical capacity were rapidly deployed within nine days of the Nepal earthquake of 25th April 2015, to provide spatiotemporally detailed estimates of population displacements from call detail records based on movements of 12 million de-identified mobile phones users.

    Results: Analysis shows the evolution of population mobility patterns after the earthquake and the patterns of return to affected areas, at a high level of detail. Particularly notable is the movement of an estimated 390,000 people above normal from the Kathmandu valley after the earthquake, with most people moving to surrounding areas and the highly-populated areas in the central southern area of Nepal.

    Discussion: This analysis provides an unprecedented level of information about human movement after a natural disaster, provided within a very short timeframe after the earthquake occurred. The patterns revealed using this method are almost impossible to find through other methods, and are of great interest to humanitarian agencies.

    Link to Paper.

    Alessandro Sorichetta, Graeme M. Hornby, Forrest R. Stevens, Andrea E. Gaughan, Catherine Linard, Andrew J. Tatem, 2015, High-resolution gridded population datasets for Latin America and the Caribbean in 2010, 2015, and 2020, Scientific Data, ​doi:10.1038/sdata.2015.45

    The Latin America and the Caribbean region is one of the most urbanized regions in the world, with a total population of around 630 million that is expected to increase by 25% by 2050. In this context, detailed and contemporary datasets accurately describing the distribution of residential population in the region are required for measuring the impacts of population growth, monitoring changes, supporting environmental and health applications, and planning interventions. To support these needs, an open access archive of high-resolution gridded population datasets was created through disaggregation of the most recent official population count data available for 28 countries located in the region. These datasets are described here along with the approach and methods used to create and validate them. For each country, population distribution datasets, having a resolution of 3 arc seconds (approximately 100m at the equator), were produced for the population count year, as well as for 2010, 2015, and 2020. All these products are available both through the WorldPop Project website and the WorldPop Dataverse Repository.

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    Liang Mao, Xiao Wu, Zhuojie Huang, Andrew J. Tatem, 2015, Modeling monthly flows of global air travel passengers: An open-access data resource, Journal of Transport Geography, 56:60, doi:10.1016/j.jtrangeo.2015.08.017

    The global flow of air travel passengers varies over time and space, but analyses of these dynamics and their integration into applications in the fields of economics, epidemiology and migration, for example, have been constrained by a lack of data, given that air passenger flow data are often difficult and expensive to obtain. Here, these dynamics are modeled at a monthly scale to provide an open-access spatio-temporally resolved data source for research purposes (www.vbd-air.com/data). By refining an annual-scale model of Huang et al. (2013), we developed a set of Poisson regression models to predict monthly passenger volumes between directly connected airports during 2010. The models not only performed well in the United States with an overall accuracy of 93%, but also showed a reasonable confidence in estimating air passenger volumes in other regions of the world. Using the model outcomes, this research studied the spatio-temporal dynamics in the world airline network (WAN) that previous analyses were unable to capture. Findings on the monthly variation of WAN offer new knowledge for dynamic planning and strategy design to address global issues, such as disease pandemics and climate change

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    Ebener, Steeve, Guerra-Arias, Maria, Campbell, James, Tatem, Andrew J., Moran, Allisyn, Amoako Johnson, Fiifi, Fogstad, Helga, Stenberg, Karin, Neal, Sarah, Bailey, Patricia, Porter, Reid and Matthews, Zoe, 2015, The geography of maternal and newborn health: the state of the art, International Journal of Health Geographics, 14:19

    As the deadline for the millennium development goals approaches, it has become clear that the goals linked to maternal and newborn health are the least likely to be achieved by 2015. It is therefore critical to ensure that all possible data, tools and methods are fully exploited to help address this gap. Among the methods that are under-used, mapping has always represented a powerful way to ‘tell the story’ of a health problem in an easily understood way. In addition to this, the advanced analytical methods and models now being embedded into Geographic Information Systems allow a more in-depth analysis of the causes behind adverse maternal and newborn health (MNH) outcomes. This paper examines the current state of the art in mapping the geography of MNH as a starting point to unleashing the potential of these under-used approaches. Using a rapid literature review and the description of the work currently in progress, this paper allows the identification of methods in use and describes a framework for methodological approaches to inform improved decision-making. The paper is aimed at health metrics and geography of health specialists, the MNH community, as well as policy-makers in developing countries and international donor agencies.

    Link to Paper.

    Sorichetta, Alessandro; Graeme M. Hornby; Forrest R. Stevens; Andrea E. Gaughan; Catherine Linard; Andrew J. Tatem, 2015, Americas Datasets , Harvard Dataverse, V1

    This Dataverse contains peer-reviewed socio-economic and demographic spatial datasets, developed in the framework of the WorldPop Project (www.worldpop.org.uk), for supporting development, environmental, and health applications in Africa, Asia, Latin America and the Caribbean. The methods used to create both these datasets and the associated metadata are designed with full open access and operational application in mind, and are transparent, shareable, and fully documented. All WorldPop datasets available here and through the project website are licensed under a Creative Commons Attribution 4.0 International License and have been contributed to the GEOSS Data Collection of Open Resources for Everyone (GEOSS Data CORE). They are increasingly used by researchers, policy makers, government agencies, international organizations, and foundations including, among others, The World Bank, WHO, UNFPA, UNOCHA, Bill & Melinda Gates Foundation, and Clinton Health Access Initiative (CHAI).

    Link to Repository.

    Wesolowski A, O'Meara WP, Eagle N, Tatem AJ and Buckee CO, 2015, Evaluating Spatial Interaction Models for Regional Mobility in Sub-Saharan Africa, PLoS Computational Biology, 11(7): e1004267. doi:10.1371/journal.pcbi.1004267

    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.

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    Alegana V.A., Atkinson P.M., Pezzulo C, Sorichetta A, Weiss D., Bird T, Erbach-Schoenberg E and Tatem AJ, 2015, Fine resolution mapping of population age-structures for health and development applications, Journal of the Royal Society Interface, 12:105,  DOI: 10.1098/rsif.2015.0073

    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.

    Link to Paper.

    Bharti, N, Lu, X, Bengtsson, L, Wetter, E, and Tatem AJ, 2015, Remotely measuring populations during a crisis by overlaying two data sources, International Health, 7 (2): 90-98.

    Background: 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. 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. Results: 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. Conclusions: 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.

    Link to Paper.

    Stevens,F.R., Gaughan, A.E., Linard, C. and Tatem A.J., 2015, Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data, PLoS ONE, 10(2): e0107042. doi:10.1371/journal.pone.0107042.

    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.

    Link to Paper.

    Deville, P., Linard, C., Martin, S., Gilbert, M., Stevens, F.R., Gaughan, A.E., Blondel, V.D. and Tatem, A.J., 2014, Dynamic population mapping using mobile phone data. Proceedings of the National Academy of Sciences (doi:10.1073/pnas.1408439111).

    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.

    Link to Paper.

    Mertes, C.M., Schneider, A., Sulla-Menashe, D., Tatem, A.J. and Tan, B., 2014, Detecting change in urban areas at continental scales with MODIS data. Remote Sensing of Environment, 1-17. (doi:10.1016/j.rse.2014.09.023).

    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 250 m 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 250 m 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.

    Link to Paper.

    Patel, Nirav N., Angiuli, Emanuele, Gamba, Paolo, Gaughan, Andrea, Lisini, Gianni, Stevens, Forrest R., Tatem, Andrew J. and Trianni, Giovanna, 2014, Multitemporal settlement and population mapping from Landsat using Google Earth Engine. International Journal of Applied Earth Observation and Geoinformation, 35, 199-208. (doi:10.1016/j.jag.2014.09.005).

    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.

    Link to Paper.

    Gaughan AE, Stevens FR, Linard C, Patel N and Tatem AJ, 2014, Exploring nationally and regionally defined models for large area population mapping, International Journal of Digital Earth, DOI:10.1080/17538947.2014.965761

    Interactions between humans, diseases and the environment take place across a range of temporal and spatial scales, making accurate, contemporary data on human population distributions critical for a variety of disciplines. Methods for disaggregating census data to finer-scale, gridded population density estimates continue to be refined as computational power increases and more detailed census, input, and validation data sets become available. However, the availability of spatially detailed census data still varies widely by country. In this study, we develop quantitative guidelines for choosing regionally parameterized census count disaggregation models over country-specific models. We examine underlying methodological considerations for improving gridded population data sets for countries with coarser scale census data by investigating regional versus country-specific models used to estimate density surfaces for redistributing census counts. Consideration is given to the spatial resolution of input census data using examples from East Africa and Southeast Asia. Results suggest that for many countries more accurate population maps can be produced by using regionally parameterized models where more spatially refined data exists than that which is available for the focal country. This study highlights the advancement of statistical toolsets and considerations for underlying data used in generating widely used gridded population data.


    T. Alex Perkins, Andres J. Garcia, Valerie A. Paz-Soldan, Steven T. Stoddard, Robert C. Reiner, Jr., Gonzalo Vazquez-Prokopec, Donal Bisanzio, Amy C. Morrison, Eric S. Halsey, Tadeusz J. Kochel, David L. Smith, Uriel Kitron, Thomas W. Scott, Andrew J. Tatem, 2014, Theory and data for simulating fine-scale human movement in an urban environment, Journal of the Royal Society Interface, 6:11

    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.

    Link to Paper.

    Andres J. Garcia, Deepa K. Pindolia, Kenneth K. Lopiano and Andrew J. Tatem, 2014, Modeling internal migration flows in sub-Saharan Africa using census microdata, doi: 10.1093/migration/mnu036

    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.

    Link to Paper.

    Tatem, A.J. (2014) Mapping population and pathogen movements. International Health (doi:10.1093/inthealth/ihu006) 6, 5-11.

    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.

    Link to Paper.

    Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z, 2014, Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births, International Journal of Health Geographics, 13:2

    Background: 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. Methods: This paper outlines demographic mapping methods based on freely available data for the production of high resolution datasets depicting estimates of numbers of people, women of childbearing age, live births and pregnancies, and distribution of comprehensive EmONC facilities in four large high burden countries: Afghanistan, Bangladesh, Ethiopia and Tanzania. Satellite derived maps of settlements and land cover were constructed and used to redistribute areal census counts to produce detailed maps of the distributions of women of childbearing age. Household survey data, UN statistics and other sources on growth rates, age specific fertility rates, live births, stillbirths and abortions were then integrated to convert the population distribution datasets to gridded estimates of births and pregnancies. Results and conclusions: These estimates, which can be produced for current, past or future years based on standard demographic projections, can provide the basis for strategic intelligence, planning services, and provide denominators for subnational indicators to track progress. The datasets produced are part of national midwifery workforce assessments conducted in collaboration with the respective Ministries of Health and the United Nations Population Fund (UNFPA) to identify disparities between population needs, health infrastructure and workforce supply. The datasets are available to the respective Ministries as part of the UNFPA programme to inform midwifery workforce planning and also publicly available through the WorldPop population mapping project.

    Link to Paper.

    Linard, Catherine, Tatem, Andrew J. and Gilbert, Marius, 2013, Modelling spatial patterns of urban growth in AfricaApplied Geography, 44, 23-32.

    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.

    Link to Paper.

    Tatem, Andrew J., Garcia, Andres J., Snow, Robert W., Noor, Abdisalan M., Gaughan, Andrea E.,Gilbert, Marius and Linard, Catherine, 2013, Millennium development health metrics: where do Africa's children and women of childbearing age live? Population Health Metrics, 11, (1), 11.

    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. Here we outline the development of high resolution age- and sex-structured spatial population datasets for Africa in 2000-2015 built from over a million measurements from more than 20,000 subnational units, increasing input data detail from previous studies by over 400-fold. We analyze the large spatial variations seen within countries and across the continent for key MDG indicator groups, focusing on children under 5 and women of childbearing age, and find that substantial differences in health and development indicators can result through using only national level statistics, compared to accounting for subnational variation. Progress toward meeting the MDGs will be measured through national-level indicators that mask substantial inequalities and heterogeneities across nations. Cartographic approaches are providing opportunities for quantitative assessments of these inequalities and the targeting of interventions, but demographic spatial datasets to support such efforts remain reliant on coarse and outdated input data for accurately locating risk groups. We have shown here that sufficient data exist to map the distribution of key vulnerable groups, and that doing so has substantial impacts on derived metrics through accounting for spatial demographic heterogeneities that exist within nations across Africa.

    Link to Paper.

    Wesolowski, Amy, Buckee, Caroline O., Pindolia, Deepa K., Eagle, Nathan, Smith, David L., Garcia, Andres J. and Tatem, Andrew J., 2013, The use of census migration data to approximate human movement patterns across temporal scales. PLoS ONE, 8, (1), e52971.

    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.

    Link to Paper.

    Caughlin, T. Trevor, Ruktanonchai, Nick, Acevedo, Miguel A., Lopiano, Kenneth K., Prosper, Olivia,Eagle, Nathan and Tatem, Andrew J., 2013, Place-based attributes predict community membership in a mobile phone communication network. PLoS ONE, 8, (2), e56057.

    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.

    Link to Paper.

    Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015PLoS ONE, 8(2): e55882. 

    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. The accuracies of 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 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.

    Link to Paper.

    Tatem, A.J., Adamo, S., Bharti, N., Burgert, C.R., Castro, M., Dorelien, A., Fink, G., Linard, C., Mendelsohn, J., Montana, L., Montgomery, M.R., Nelson, A., Noor, A.M. , Pindolia, D.,Yetman, G. and Balk, D., 2012, Mapping populations at risk: Improving spatial demographic data for infectious disease modeling and deriving health metrics, Population Health Metrics, 10: 8. 

    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. 

    Where risks are heterogeneous across population groups or space, or dependent on transmission between individuals, spatial data on human population distributions and demographic structures are required to estimate infectious disease risks, burdens, and dynamics. The disease impact in terms of morbidity, mortality, and speed of spread varies substantially with demographic profiles, so that identifying the most exposed or affected populations becomes a key aspect of planning and targeting interventions. Subnational breakdowns of population counts by age and sex are routinely collected during national censuses and maintained in finer detail within microcensus data. Moreover, demographic and health surveys continue to collect representative and contemporary samples from clusters of communities in low-income countries where census data may be less detailed and not collected regularly. Together, these freely available datasets form a rich resource for quantifying and understanding the spatial variations in the sizes and distributions of those most at risk of disease in low income regions, yet at present, they remain unconnected data scattered across national statistical offices and websites.

    In this paper we discuss the deficiencies of existing spatial population datasets and their limitations on epidemiological analyses. We review sources of detailed, contemporary, freely available and relevant spatial demographic data focusing on low income regions where such data are often sparse, and highlight the value of incorporating these through a set of examples of their application in disease studies. Moreover, the importance of acknowledging, measuring and accounting for uncertainty in spatial demographic datasets is outlined. Finally, a strategy for building an open-access database of spatial demographic data that is tailored to epidemiological applications is put forward.

    Link to Paper.

    Pindolia, D.K., Garcia, A.J., Wesolowski, A., Smith, D.L., Buckee, C.O., Noor, A.M., Snow, R.W. and Tatem, A.J., 2012 Human movement data for malaria control and elimination strategic planning, Malaria Journal, 11: 205.

    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 Systems (GIS) framework for analysing and modelling human population and Plasmodium falciparum malaria infection movements.

    Link to Paper.

    Linard, C. and Tatem, A.J., 2012, Large-scale spatial population databases in infectious disease research, International Journal of Health Geographics, 11, 7.

    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.

    Link to Paper.

    Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743.

    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.

    Link to Paper.

    Tatem, A.J. and Linard, C., 2011, Population mapping of poor countries, Nature, 474, 36.

    Global population maps can be valuable for quantifying populations at risk, such as those near nuclear power plants (Nature 472, 400-401; 2011). But the uncertainties inherent in such data sets must be acknowledged. The census data used in map construction for rich countries are recent and detailed. The same is often not true for poorer countries. For example, Angola's last census was in 1970, broken down into just 18 districts. Estimates of its current total resident population vary from 13.3 million to 19 million, according to the US Census Bureau and the United Nations, respectively. When such outdated and coarse-resolution data are subject to different modelling assumptions by different groups, it can lead to substantially divergent estimates of population distributions and, consequently, populations at risk. Uncertainties in and between global population maps should be more widely discussed, and a greater effort made to quantify them. Furthermore, spatially referenced demographic data used in map construction are often scattered across national statistical offices and websites. A centralized, open-access, up-to-date database would benefit many fields that rely on population maps, and would require minimal investment.

    Link to Paper.

    Tatem, A.J., Campiz, N., Gething, P.W., Snow, R.W. and Linard, C., 2011, The effects of spatial population dataset choice on population at risk of disease estimates, Population Health Metrics, 9: 4.

    Background: 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.

    Methods: The recent construction of a global map of P. falciparum malaria endemicity enabled the testing of different gridded population datasets for providing estimates of PAR by endemicity class. The estimated population numbers within each class were calculated for each country using four different global gridded human population datasets: GRUMP (~1km spatial resolution), LandScan (~1km), UNEP Global Population Databases (~5km), and GPW3 (~5km). More detailed assessments of PAR variation and accuracy were conducted for three African countries where census data were available at a higher administrative-unit level than used by any of the four gridded population datasets.

    Results: The estimates of PAR based on the datasets varied by more than 10 million people for some countries, even accounting for the fact that estimates of population totals made by different agencies are used to correct national totals in these datasets and can vary by more than 5% for many low-income countries. In many cases, these variations in PAR estimates comprised more than 10% of the total national population. The detailed country-level assessments suggested that none of the datasets was consistently more accurate than the others in estimating PAR. The sizes of such differences among modeled human populations were related to variations in the methods, input resolution, and date of the census data underlying each dataset. Data quality varied from country to country within the spatial population datasets.

    Conclusions: Detailed, highly spatially resolved human population data are an essential resource for planning health service delivery for disease control, for the spatial modeling of epidemics, and for decision-making processes related to public health. However, our results highlight that for the low-income regions of the world where disease burden is greatest, existing datasets display substantial variations in estimated population distributions, resulting in uncertainty in disease assessments that utilize them. Increased efforts are required to gather contemporary and spatially detailed demographic data to reduce this uncertainty, particularly in Africa, and to develop population distribution modeling methods that match the rigor, sophistication, and ability to handle uncertainty of contemporary disease mapping and spread modeling. In the meantime, studies that utilize a particular spatial population dataset need to acknowledge the uncertainties inherent within them and consider how the methods and data that comprise each will affect conclusions.

    Link to Paper.

    Linard, C., Alegana, V.A., Noor, A.M., Snow, R.W. and Tatem, A.J., 2010, A high resolution spatial population database of Somalia for disease risk mapping, International Journal of Health Geographics, 9: 45.

    Background: 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. Results: Here land cover information derived from satellite imagery and existing settlement point datasets were used for the spatial reallocation of populations within census units. We used simple and semi-automated methods that can be implemented with free image processing software to produce an easily updatable gridded population dataset at 100 x 100 meters spatial resolution. The 2010 population dataset was matched to administrative population totals projected by the UN. Comparison tests between the new dataset and existing population datasets revealed important differences in population size distributions, and in population at risk of malaria estimates. These differences are particularly important in more densely populated areas and strongly depend on the settlement data used in the modelling approach. Conclusions: The results show that it is possible to produce detailed, contemporary and easily updatable settlement and population distribution datasets of Somalia using existing data. The 2010 population dataset produced is freely available as a product of the AfriPop Project and can be downloaded from: www.afripop.org.

    Link to Paper.

    Linard, C., Gilbert, M. and Tatem A.J., 2010, Assessing the use of global land cover data for guiding large area population distribution modelling, Geojournal, doi:10.1007/s10708-010-9364-8.

    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 areas 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.

    Link to Paper.

    Tatem, A.J., Noor, A.M., von Hagen, C., Di Gregorio, A., and S.I. Hay, High resolution settlement and population maps for low income nations: combining land cover and national census in East Africa. PLoS One, 2007. 2: p. e1298.

    BACKGROUND: Between 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 FINDINGS: We 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. CONCLUSIONS: We 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.

    Link to Paper.

    Tatem, A.J., Noor, A.M. and S.I. Hay, Assessing the accuracy of satellite derived global and national urban maps in Kenya. Remote Sensing of Environment, 2005. 96: p. 87-97.

    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.


    S.I. Hay, Noor, A.M., Nelson, A. And Tatem, A.J., The accuracy of human population maps for public health application. Tropical Medicine and International Health, 2005. 10: p. 1073-1086.

    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.


    Tatem, A.J., Noor, A.M. and S.I. Hay, Defining approaches to settlement mapping for public health management in Kenya using medium spatial resolution satellite imagery. Remote Sensing of Environment, 2004. 93: p. 42-52.

    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 nonsettlement 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.


    Tatem, A.J. and S.I. Hay, Measuring Urbanization Pattern and Extent for Malaria Research: A Review of Remote Sensing Approaches. Journal of Urban Health, 2004. 81: p. 363-376.

    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.



    Lucy S. Tusting , Christian Bottomley, Harry Gibson, Immo Kleinschmidt, Andrew J. Tatem, Steve W. Lindsay, Peter W. Gething. Housing Improvements and Malaria Risk in Sub-Saharan Africa: A Multi-Country Analysis of Survey Data. PLOS Medicine. 2017. DOI: http://dx.doi.org/10.1371/journal.pmed.1002234

    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).

    Link to Paper.

    Moritz U G Kraemer, Nuno R Faria, Robert C Reiner Jr, Nick Golding, Birgit Nikolay, Stephanie Stasse, Michael A Johansson, Henrik Salje, Ousmane Faye, G R William Wint, Matthias Niedrig, Freya M Shearer, Sarah C Hill, Robin N Thompson, Donal Bisanzio, Nuno Taveira, Heinrich H Nax, Bary S R Pradelski, Elaine O Nsoesie, Nicholas R Murphy, Isaac I Bogoch, Kamran Khan, John S Brownstein, Andrew J Tatem, Tulio de Oliveira, David L Smith, Amadou A Sall, Oliver G Pybus, Simon I Hay, Simon Cauchemez. Spread of yellow fever virus outbreak in Angola and the Democratic Republic of the Congo 2015–16: a modelling study. The Lancet Infectious Diseases. 2016. DOI: http://dx.doi.org/10.1016/S1473-3099(16)30513-8

    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.

    Link to Paper.

    Shengjie Lai, Nicola A. Wardrop, Zhuojie Huang, Claudio Bosco, Junling Sun, Tomas Bird, Amy Wesolowski, Sheng Zhou, Qian Zhang, Canjun Zheng, Zhongjie Li, Andrew J. Tatem & Hongjie Yu. Plasmodium falciparum malaria importation from Africa to China and its mortality: an analysis of driving factors. Scientific Reports 6, Article number: 39524. 2016. doi:10.1038/srep39524

    Background: 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, 8653 P. 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 (P = 0.006), parasite prevalence in Africa (P < 0.001), and the amount of official development assistance from China (P < 0.001) 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.

    Link to Paper.

    Victor A. Alegana, Simon P. Kigozi, Joaniter Nankabirwa, Emmanuel Arinaitwe, Ruth Kigozi, Henry Mawejje, Maxwell Kilama, Nick W. Ruktanonchai, Corrine W. Ruktanonchai, Chris Drakeley, Steve W. Lindsay, Bryan Greenhouse, Moses R. Kamya, David L. Smith, Peter M. Atkinson, Grant Dorsey and Andrew J. Tatem. Spatio-temporal analysis of malaria vector density from baseline through intervention in a high transmission setting. Parasites & Vectors 2016 9:637. DOI: 10.1186/s13071-016-1917-3

    Background: 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.

    Link to Paper.

    Prof Andrew J Tatem, PhD, Peng Jia, PhD, Dariya Ordanovich, MSc, Michael Falkner, BSc, Zhuojie Huang, PhD, Rosalind Howes, PhD, Prof Simon I Hay, DSc, Peter W Gething, PhD, Prof David L Smith, PhD. The geography of imported malaria to non-endemic countries: a meta-analysis of nationally reported statistics. The Lancet Infectious Diseases , Volume 0 , Issue 0. DOI: 10.1016/S1473-3099(16)30326-7

    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.

    Link to Paper.

    Pezzulo, Carla, Tomas Bird, Edson C. Utazi, Alessandro Sorichetta, Andrew J. Tatem, Jennifer Yourkavitch, and Clara R. Burgert-Brucker. Geospatial modeling of child mortality across 27 countries in Sub-Saharan Africa. DHS Spatial Analysis Report No. 13. Rockville, Maryland, USA: ICF International.

    Background: Preventable mortality of children has been targeted as one of the UN’s Sustainable Development Goals for the 2015-30 period. Global decreases in child mortality (4q1) have been seen, although sub-Saharan Africa remains an area of concern, with child mortality rates remaining high relative to global averages or even increasing in some cases. Furthermore, the spatial distribution of child mortality in sub-Saharan Africa is highly heterogeneous. Thus, research that identifies primary risk factors and protective measures in the geographic context of sub-Saharan Africa is needed. In this study, household survey data collected by The Demographic and Health Surveys (DHS) Program aggregated at DHS sub-national area scale are used to evaluate the spatial distribution of child mortality (age 1 to 4) across 27 sub-Saharan Africa countries in relation to a number of demographic and health indicators collected in the DHS surveys. In addition, this report controls for spatial variation in potential environmental drivers of child mortality by modeling it against a suite of geospatial datasets. These datasets vary across the study area in an autoregressive spatial model that accounts for the spatial autocorrelation present in the data. This study shows that socio-demographic factors such as birth interval, stunting, access to health facilities and literacy, along with geospatial factors such as prevalence of Plasmodium falciparum malaria, variety of ethnic groups, mean temperature, and intensity of lights at night can explain up to 60% of the variance in child mortality across 255 DHS sub-national areas in the 27 countries. Additionally, three regions - Western, Central, and Eastern Africa - have markedly different mortality rates. By identifying the relative importance of policy-relevant socio-demographic and environmental factors, this study highlights priorities for research and programs targeting child mortality over the next decade.

    Link to Paper.

    Corinne E. Armstrong, Melisa Martínez-Álvarez, Neha S. Singh, Theopista John, Hoviyeh Afnan-Holmes, Chris Grundy, Corrine W. Ruktanochai, Josephine Borghi, Moke Magoma, Georgina Msemo, Zoe Matthews, Gemini Mtei and Joy E. Lawn. Subnational variation for care at birth in Tanzania: is this explained by place, people, money or drugs? BMC Public HealthBMC series – open, inclusive and trusted 2016 16(Suppl 2):795 DOI: 10.1186/s12889-016-3404-3

    Background: 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).

    Link to Paper.

    Regan Early, Bethany A. Bradley, Jeffrey S. Dukes, Joshua J. Lawler, Julian D. Olden, Dana M. Blumenthal, Patrick Gonzalez, Edwin D. Grosholz, Ines Ibañez, Luke P. Miller, Cascade J. B. Sorte & Andrew J. Tatem. Global threats from invasive alien species in the twenty-first century and national response capacities. Nature Communications 7, Article number: 12485 (2016). doi:10.1038/ncomms12485

    Background: 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.

    Link to Paper.

    T. Alex Perkins, Amir S. Siraj, Corrine W. Ruktanonchai, Moritz U. G. Kraemer & Andrew J. Tatem. Model-based projections of Zika virus infections in childbearing women in the Americas. Nature Microbiology 1, Article number: 16126 (2016). doi:10.1038/nmicrobiol.2016.126

    Background: 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.

    Link to Paper.

    Amy Wesolowski, Keitly Mensah, Cara E. Brook, Miora Andrianjafimasy, Amy Winter, Caroline O. Buckee, Richter Razafindratsimandresy, Andrew J. Tatem, Jean-Michel Heraud, C. Jessica E. Metcalf. Introduction of rubella-containing-vaccine to Madagascar: implications for roll-out and local elimination. J. R. Soc. Interface 2016 13 20151101; DOI: 10.1098/rsif.2015.1101. Published 27 April 2016

    Background: 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.

    Link to Paper.

    Jane P Messina, Moritz UG Kraemer, Oliver J Brady, David M Pigott, Freya M Shearer, Daniel J Weiss, Nick Golding, Corrine W Ruktanonchai, Peter W Gething, Emily Cohn, John S Brownstein, Kamran Khan Andrew J Tatem, Thomas Jaenisch, Christopher JL Murray, Fatima Marinho, Thomas W Scott, Simon I Hay Mapping global environmental suitability for Zika virus. eLife 2016;10.7554/eLife.15272; DOI: http://dx.doi.org/10.7554/eLife.15272

    Background: 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.

    Link to Paper.

    Robert C Reiner Jr, Arnaud Le Menach, Simon Kunene, Nyasatu Ntshalintshali, Michelle S Hsiang, T Alex Perkins, Bryan Greenhouse, Andrew J Tatem, Justin M Cohen, David L Smith Mapping residual transmission for malaria elimination. eLife 2015;4:e09520; DOI: http://dx.doi.org/10.7554/eLife.09520

    Background: 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.

    Link to Paper.

    Xia Zhao, David L. Smith and Andrew J. Tatem. Exploring the spatiotemporal drivers of malaria elimination in Europe.Malaria Journal 2016 15:122. 4 March 2016. DOI: 10.1186/s12936-016-1175-z

    Background: 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.

    Methods: Spatial datasets on changes in climate, wealth, life expectancy, urbanization, and land use trends over the past century were combined with datasets depicting the reduction in malaria transmission across 31 European countries, and the relationships were explored. Moreover, the conditions in current malaria-eliminating countries were compared with those in Europe at the time of declining transmission and elimination to assess similarities.

    Results/conclusions: Indicators relating to socio-economic improvements such as wealth, life expectancy and urbanization were strongly correlated with the decline of malaria in Europe, whereas those describing climatic and land use changes showed weaker relationships. Present-day malaria-elimination countries have now arrived at similar socio-economic indicator levels as European countries at the time malaria elimination was achieved, offering hope for achievement of sustainable elimination.

    Keywords: Malaria elimination Europe GIS Malaria risk mapping

    Link to Paper.

    Bosomprah, Samuel, Tatem, Andrew J., Dotse-Gborgbortsi, Winfred, Aboagye, Patrick and Matthews, Zoe. Spatial distribution of emergency obstetric and newborn care services in Ghana: using the evidence to plan interventions.International Journal of Gynecology & Obstetrics. 132, (1), 130-134. (doi:10.1016/j.ijgo.2015.11.004).

    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.

    Link to Paper.

    Bhatt, Samir, Weiss, Daniel J., Mappin, Bonnie, Dalrymple, Ursula, Cameron, Ewan, Bisanzio, Donal, Smith, David L., Moyes, Catherine L., Tatem, Andrew J., Lynch, Michael, Fergus, Cristin A., Yukich, Joshua, Bennett, Adam, Eisele, Thomas P., Kolaczinski, Jan, Cibulskis, Richard E., Hay, Simon I. and Gething, Peter W. Coverage and system efficiencies of insecticide-treated nets in Africa from 2000 to 2017. eLife. 4, 1-49. (doi:10.7554/eLife.09672).

    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 'inefficiencies': 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%.

    Link to Paper.

    Pybus, Oliver G., Tatem, Andrew J. and Lemey, Philippe. Virus evolution and transmission in an ever more connected world. Proceedings of The Royal Society B Biological Sciences. (2015) 282, (1821), 1-10. (doi:10.1098/rspb.2014.2878).

    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.

    Link to Paper.

    Jia, Peng, Sankoh, Osman and Tatem, Andrew J. Mapping the environmental and socioeconomic coverage of the INDEPTH international health and demographic surveillance system network. Health & Place. (2015) 36, 88-96. (doi:10.1016/j.healthplace.2015.09.009).

    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.

    Link to Paper.

    Sedda, Luigi, Qi, Qiuyin and Tatem, Andrew J. A geostatistical analysis of the association between armed conflicts and Plasmodium falciparum malaria in Africa, 1997–2010. Malaria Journal. (2015) 14, (1) (doi:10.1186/s12936-015-1024-5).

    Background: 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.

    Methods: In this analysis an explicit geostatistical, autoregressive, mixed model is employed to quantitatively assess the association between conflicts and variations in Plasmodium falciparum parasite prevalence across a 13-year period in sub-Saharan Africa.

    Results: Analyses of geolocated, malaria prevalence survey variations against armed conflict data in general showed a wide, but short-lived impact of conflict events geographically. The number of countries with decreased P. falciparum parasite prevalence (17) is larger than the number of countries with increased transmission (12), and notably, some of the countries with the highest transmission pre-conflict were still found with lower transmission post-conflict. For four countries, there were no significant changes in parasite prevalence. Finally, distance from conflicts, duration of conflicts, violence of conflict, and number of conflicts were significant components in the model explaining the changes in P. falciparum parasite rate.

    Conclusions: The results suggest that the maintenance of intervention coverage and provision of healthcare in conflict situations to protect vulnerable populations can maintain gains in even the most difficult of circumstances, and that conflict does not represent a substantial barrier to elimination goals.

    Link to Paper.

    Amy Wesolowski, C. J. E. Metcalf, Nathan Eagle, Janeth Kombich, Bryan T. Grenfell, Ottar N. Bjørnstad, Justin Lessler, Andrew J. Tatem, and Caroline O. Buckee, 2015, Quantifying seasonal population fluxes driving rubella transmission dynamics using mobile phone data, PNAS, doi: 10.1073/pnas.1423542112

    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.

    Link to Paper.

    A Schneider, C M Mertes, A J Tatem, B Tan, D Sulla-Menashe, S J Graves, N N Patel, J A Horton, A E Gaughan, J T Rollo, I H Schelly, F R Stevens and A Dastur, 2015, A new urban landscape in East–Southeast Asia, 2000–2010, Environmental Research Letters, 10 034002

    East–Southeast Asia is currently one of the fastest urbanizing regions in the world, with countries such as China climbing from 20 to 50% urbanized in just a few decades. By 2050, these countries are projected to add 1 billion people, with 90% of that growth occurring in cities. This population shift parallels an equally astounding amount of built-up land expansion. However, spatially-and temporally-detailed information on regional-scale changes in urban land or population distribution do not exist; previous efforts have been either sample-based, focused on one country, or drawn conclusions from datasets with substantial temporal/spatial mismatch and variability in urban definitions. Using consistent methodology, satellite imagery and census data for >1000 agglomerations in the East–Southeast Asian region, we show that urban land increased >22% between 2000 and 2010 (from 155 000 to 189 000 km2), an amount equivalent to the area of Taiwan, while urban populations climbed >31% (from 738 to 969 million). Although urban land expanded at unprecedented rates, urban populations grew more rapidly, resulting in increasing densities for the majority of urban agglomerations, including those in both more developed (Japan, South Korea) and industrializing nations (China, Vietnam, Indonesia). This result contrasts previous sample-based studies, which conclude that cities are universally declining in density. The patterns and rates of change uncovered by these datasets provide a unique record of the massive urban transition currently underway in East–Southeast Asia that is impacting local-regional climate, pollution levels, water quality/availability, arable land, as well as the livelihoods and vulnerability of populations in the region.

    Link to Paper.

    Sedda L, Tatem AJ, Morley DW, Atkinson PM, Wardrop NA, Pezzulo C, Sorichetta A, Kuleszo J, and Rogers DJ, 2015, Poverty, health and satellite-derived vegetation indices: their inter-spatial relationship in West Africa, International Health, 7 (2): 99-106

    Background: 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. Methods: 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. Results: 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. Conclusions: 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.

    Link to Paper.

    Tatem, A.J., 2014, Mapping the denominator: spatial demography in the measurement of progress, International Health, pii: ihu057

    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.

    Link to Paper.

    Metcalf CJ, Tatem AJ, Bjornstad ON, Lessler J, O'Reilly K, Takahashi S, Cutts F, Grenfell BT, 2014, Transport networks and inequities in vaccination: remoteness shapes measles vaccine coverage and prospects for elimination across Africa, Epidemiology and Infection, 14:1-10

    Measles vaccination is estimated to have averted 13·8 million deaths between 2000 and 2012. Persisting heterogeneity in coverage is a major contributor to continued measles mortality, and a barrier to measles elimination and introduction of rubella-containing vaccine. Our objective is to identify determinants of inequities in coverage, and how vaccine delivery must change to achieve elimination goals, which is a focus of the WHO Decade of Vaccines. We combined estimates of travel time to the nearest urban centre (⩾50 000 people) with vaccination data from Demographic Health Surveys to assess how remoteness affects coverage in 26 African countries. Building on a statistical mapping of coverage against age and geographical isolation, we quantified how modifying the rate and age range of vaccine delivery affects national coverage. Our scenario analysis considers increasing the rate of delivery of routine vaccination, increasing the target age range of routine vaccination, and enhanced delivery to remote areas. Geographical isolation plays a key role in defining vaccine inequity, with greater inequity in countries with lower measles vaccine coverage. Eliminating geographical inequities alone will not achieve thresholds for herd immunity, indicating that changes in delivery rate or age range of routine vaccination will be required. Measles vaccine coverage remains far below targets for herd immunity in many countries on the African continent and is likely to be inadequate for achieving rubella elimination. The impact of strategies such as increasing the upper age range eligible for routine vaccination should be considered.

    Link to Paper.

    Tatem, Andrew J., Huang, Zhuojie, Narib, Clothilde, Kumar, Udayan, Kandula, Deepika, Pindolia, Deepa K., Smith, David L., Cohen, Justin M., Graupe, Bonita, Uusiku, Petrina and Lourenco, Christopher (2014) Integrating rapid risk mapping and mobile phone call record data for strategic malaria elimination planning. Malaria Journal, 13, (52).

    Background: 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. Methods/Results: Here, using the example of Namibia, a method for targeting of interventions using surveillance data, satellite imagery, and mobile phone call records to support elimination planning is described. One year of aggregated movement patterns for over a million people across Namibia are analyzed, and linked with case-based risk maps built on satellite imagery. By combining case-data and movement, the way human population movements connect transmission risk areas is demonstrated. Communities that were strongly connected by relatively higher levels of movement were then identified, and net export and import of travellers and infection risks by region were quantified. These maps can aid the design of targeted interventions to maximally reduce the number of cases exported to other regions while employing appropriate interventions to manage risk in places that import them. Conclusions: The approaches presented can be rapidly updated and used to identify where active surveillance for both local and imported cases should be increased, which regions would benefit from coordinating efforts, and how spatially progressive elimination plans can be designed. With improvements in surveillance systems linked to improved diagnosis of malaria, detailed satellite imagery being readily available and mobile phone usage data continually being collected by network providers, the potential exists to make operational use of such valuable, complimentary and contemporary datasets on an ongoing basis in infectious disease control and elimination.

    Link to Paper.

    Gilbert, Marius, Golding, Nick, Zhou, Hang, Wint, G.R. William, Robinson, Timothy P., Tatem, Andrew J., Lai, Shengjie, Zhou, Sheng, Jiang, Hui, Guo, Danhuai, Huang, Zhi, Messina, Jane P., Xiao, Xiangming, Linard, Catherine, Van Boeckel, Thomas P., Martin, Vincent, Bhatt, Samir, Gething, Peter W., Farrar, Jeremy J., Hay, Simon I. and Yu, Hongjie (2014) Predicting the risk of avian influenza A H7N9 infection in live-poultry markets across Asia. Nature Communications, 5, 1-7.

    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.

    Link to Paper.

    Piel, Frédéric B., Tatem, Andrew J., Huang, Zhuojie, Gupta, Sunetra, Williams, Thomas N. and Weatherall, David J. (2014) Global migration and the changing distribution of sickle haemoglobin: a quantitative study of temporal trends between 1960 and 2000. The Lancet Global Health

    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.

    Link to Paper.

    Pindolia, Deepa K., Garcia, Andres J., Huang, Zhuojie , Fik, Timothy, Smith, David L and Tatem, Andrew J., 2014, Quantifying cross-border movements and migrations for guiding the strategic planning of malaria control and eliminationMalaria Journal, 13, (1), 169.

    Background: 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. Methods: National census data were used to analyse and model cross-border migration and movement, using East Africa as an example. ‘Hotspots’ of origin-specific immigrants from neighbouring countries were identified for Kenya, Tanzania and Uganda. Populations of origin-specific migrants were compared to distance from origin country borders and population size at destination, and regression models were developed to quantify and compare differences in migration patterns. Migration data were then combined with existing spatially-referenced malaria data to compare the relative propensity for cross-border malaria movement in the region. Results: The spatial patterns and processes for immigration were different between each origin and destination country pair. Hotspots of immigration, for example, were concentrated close to origin country borders for most immigrants to Tanzania, but for Kenya, a similar pattern was only seen for Tanzanian and Ugandan immigrants. Regression model fits also differed between specific migrant groups, with some migration patterns more dependent on population size at destination and distance travelled than others. With these differences between immigration patterns and processes, and heterogeneous transmission risk in East Africa and the surrounding region, propensities to import malaria infections also likely show substantial variations. Conclusion: This was a first attempt to quantify and model cross-border movements relevant to malaria transmission and control. With national census available worldwide, this approach can be translated to construct a cross-border human and malaria movement evidence base for other malaria endemic countries. The outcomes of this study will feed into wider efforts to quantify and model human and malaria movements in endemic regions to facilitate improved intervention planning, resource allocation and collaborative policy decisions.

    Link to Paper.

    Xu, L., Wetter, E., Bharti, N., Tatem, A.J. and Bengtsson, L., 2013, Approaching the limit of predictability in human mobility, Nature Scienctific Reports, 3, 2923.

    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.

    Link to Paper.

    Pindolia DK, Garcia A, Huang Z, Smith DL, Alegana VA, Noor Am, Snow RW and Tatem AJ, 2013, The demographics of human and malaria movement and migration patterns in East Africa, Malaria Journal, 12, 397.

    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. National population censuses and household surveys provide individual-level migration, travel, and other data relevant for understanding malaria movement patterns. Together with existing spatially referenced malaria data and mathematical models, network analysis techniques were used to quantify the demographics of human and malaria movement patterns in Kenya, Uganda and Tanzania. Movement networks were developed based on connectivity and magnitudes of flow within each country and compared to assess relative differences between regions and demographic groups. Additional malaria-relevant characteristics, such as short-term travel and bed net use, were also examined. Patterns of human and malaria movements varied between demographic groups, within country regions and between countries. Migration rates were highest in 20--30 year olds in all three countries, but when accounting for malaria prevalence, movements in the 10--20 year age group became more important. Different age and sex groups also exhibited substantial variations in terms of the most likely sources, sinks and routes of migration and malaria movement, as well as risk factors for infection, such as short-term travel and bed net use. Census and survey data, together with spatially referenced malaria data, GIS and network analysis tools, can be valuable for identifying, mapping and quantifying regional connectivities and the mobility of different demographic groups. Demographically-stratified HPM and malaria movement estimates can provide quantitative evidence to inform the design of more efficient intervention and surveillance strategies that are targeted to specific regions and population groups.

    Link to Paper.

    Tatem, Andrew J, Gething, Peter W, Smith, David L and Hay, Simon I, 2013, Urbanization and the global malaria recessionMalaria Journal, 12, (1), 133.

    Background: 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. Methods: Global data at subnational spatial resolution on changes in malaria transmission intensity and urbanization trends over the past century were combined to examine the relationships seen over a range of spatial and temporal scales. Results/Conclusions: A consistent pattern of increased urbanization coincident with decreasing malaria transmission and elimination over the past century was found. Whilst it remains challenging to untangle whether this increased urbanization resulted in decreased transmission, or that malaria reductions promoted development, the results point to a close relationship between the two, irrespective of national wealth. The continuing rapid urbanization in malaria-endemic regions suggests that such malaria declines are likely to continue, particularly catalyzed by increasing levels of direct malaria control.

    Link to Paper.

    Smith, David L., Cohen, Justin M., Chiyaka, Christinah, Johnston, Geoffrey, Gething, Peter W.,Gosling, Roly, Buckee, Caroline O., Laxminarayan, Ramanan, Hay, Simon I. and Tatem, Andrew J., 2013, A sticky situation: the unexpected stability of malaria eliminationPhilosophical transactions of the Royal Society of London. Series B, Biological Sciences, 368, (1623), 20120145.

    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.

    Link to Paper.

    Cohen, Justin M., Dlamini, Sabelo, Novotny, Joseph M., Kandula, Deepika, Kunene, Simon andTatem, Andrew J., 2013, Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in SwazilandMalaria Journal, 12, 61.

    Background: 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. Methods: Household locations and travel histories of confirmed malaria patients during 2011 were recorded through routine surveillance by the Swaziland National Malaria Control Programme for the higher transmission months of January to April and the lower transmission months of May to December. Household locations for patients with no travel history to endemic areas were compared against a random set of background points sampled proportionate to population density with respect to a set of variables related to environment, population density, vector control, and distance to the locations of identified imported cases. Comparisons were made separately for the high and low transmission seasons. The Random Forests regression tree classification approach was used to generate maps predicting the probability of a locally acquired case at 100 m resolution across Swaziland for each season. Results: Results indicated that case households during the high transmission season tended to be located in areas of lower elevation, closer to bodies of water, in more sparsely populated areas, with lower rainfall and warmer temperatures, and closer to imported cases than random background points (all p < 0.001). Similar differences were evident during the low transmission season. Maps from the fit models suggested better predictive ability during the high season. Both models proved useful at predicting the locations of local cases identified in 2012. Conclusions: The high-resolution mapping approaches described here can help elimination programmes understand the epidemiology of a disappearing disease. Generating case-based risk maps at high spatial and temporal resolution will allow control programmes to direct interventions proactively according to evidence-based measures of risk and ensure that the impact of limited resources is maximized to achieve and maintain malaria elimination.

    Link to Paper.

    Metcalf, C.J.E., Cohen, C., Lessler, J., McAnerney, J.M., Ntshoe, G.M., Puren, A., Klepac, P., Tatem, A.,Grenfell, B.T. and Bjørnstad, O.N., 2013, Implications of spatially heterogeneous vaccination coverage for the risk of congenital rubella syndrome in South AfricaJournal of The Royal Society Interface, 10,(78), 20120756

    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.

    Link to Paper.

    Qi, Qiuyin, Guerra, Carlos A., Moyes, Catherine L., Elyazar, Iqbal R. F., Gething, Peter W., Hay, Simon I. and Tatem, Andrew J., 2012, The effects of urbanization on global Plasmodium vivax malaria transmissionMalaria Journal, 11, 403.

    Background: 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 (PvPR) surveys are used to explore the relationships between PvPR in urban and rural settings. Methods: The PvPR surveys were overlaid onto a map of global urban extents to derive an urban/rural assignment. The differences in PvPR values between urban and rural areas were then examined. Groups of PvPR surveys inside individual city extents (urban) and surrounding areas (rural) were identified to examine the local variations in PvPR values. Finally, the relationships of PvPR between urban and rural areas within the ranges of 41 dominant Anopheles vectors were examined. Results: Significantly higher PvPR values in rural areas were found globally. The relationship was consistent at continental scales when focusing on Africa and Asia only, but in the Americas, significantly lower values of PvPR in rural areas were found, though the numbers of surveys were small. Moreover, except for the countries in the Americas, the same trends were found at national scales in African and Asian countries, with significantly lower values of PvPR in urban areas. However, the patterns at city scales among 20 specific cities where sufficient data were available were less clear, with seven cities having significantly lower PvPR values in urban areas and two cities showing significantly lower PvPR in rural areas. The urban–rural PvPR differences within the ranges of the dominant Anopheles vectors were generally, in agreement with the regional patterns found. Conclusions: Except for the Americas, the patterns of significantly lower P. vivax transmission in urban areas have been found globally, regionally, nationally and by dominant vector species here, following trends observed previously for P. falciparum. To further understand these patterns, more epidemiological, entomological and parasitological analyses of the disease at smaller spatial scales are needed.

    Link to Paper.

    Wesolowski, A., Eagle, N., Tatem, A.J., Smith, D.L., Noor, A.M., Snow, R.W. and Buckee, C.O., 2012, Quantifying the impact of human mobility on malaria, Science, 338, 267-270.

    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.

    Link to Paper.

    Cohen, J.M., Woolsey, A.M., Sabot, O.J., Gething, P.W., Tatem, A.J. and Moonen, B., 2012, Optimizing investments in malaria treatment and diagnosis, Science, 338, 612-614.

    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.

    Link to Paper.

    Tatem, A.J., Hemelaar, J., Gray, R.R. and Salemi, M., 2012, Spatial accessibility and the spread of HIV-1 subtypes and recombinants, 26, (18), 2351-2360

    OBJECTIVE/DESIGN:: The global spread of HIV-1 group M has resulted in differential distributions of subtypes and recombinants, with the greatest diversity being found in sub-Saharan Africa. The explanations for the current subtype distribution patterns are likely multifactorial, but the promotion of human migrations and movements through transportation link availability and quality, summarised through 'accessibility', have been consistently cited as strong drivers. We sought to address the question of whether accessibility has been a significant factor in HIV-1 spread across mainland Africa through spatial analyses of molecular epidemiology, transport network and land cover data. METHODS:: The distribution of HIV-1 subtypes and recombinants in sub-Saharan Africa for the period 1998-2008 was mapped using molecular epidemiology data at a finer level of detail than ever before. Moreover, hypotheses on the role of distance, road network structure and accessibility in explaining the patterns seen were tested using spatial datasets representing African transport infrastructure, land cover and an accessibility model of landscape travel speed. RESULTS:: Coherent spatial patterns in HIV-1 subtype distributions across the continent exist, and a substantial proportion of the variance in the distribution and diversity patterns seen can be explained by variations in regional spatial accessibility. CONCLUSIONS:: The study confirms quantitatively the influence of transport infrastructure on HIV-1 spread within Africa, presents an approach for examining potential future impacts of road development projects and more generally, highlights the importance of accessibility in the spread of communicable diseases.

    Link to Paper.

    Mondal, P. and Tatem, A.J., 2012, Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding, PLoS ONE, 7(10), e48191.

    A better understanding of the impact of global climate change requires information on the locations and characteristics of populations affected. For instance, with global sea level predicted to rise and coastal flooding set to become more frequent and intense, high-resolution spatial population datasets are increasingly being used to estimate the size of vulnerable coastal populations. Many previous studies have undertaken this by quantifying the size of populations residing in low elevation coastal zones using one of two global spatial population datasets available - LandScan and the Global Rural Urban Mapping Project (GRUMP). This has been undertaken without consideration of the effects of this choice, which are a function of the quality of input datasets and differences in methods used to construct each spatial population dataset. Here we calculate estimated low elevation coastal zone resident population sizes from LandScan and GRUMP using previously adopted approaches, and quantify the absolute and relative differences achieved through switching datasets. Our findings suggest that the choice of one particular dataset over another can translate to a difference of more than 7.5 million vulnerable people for countries with extensive coastal populations, such as Indonesia and Japan. Our findings also show variations in estimates of proportions of national populations at risk range from ,0.1% to 45% differences when switching between datasets, with large differences predominantly for countries where coarse and outdated input data were used in the construction of the spatial population datasets. The results highlight the need for the construction of spatial population datasets built on accurate, contemporary and detailed census data for use in climate change impact studies and the importance of acknowledging uncertainties inherent in existing spatial population datasets when estimating the demographic impacts of climate change.

    Link to Paper.

    Tatem, A.J. and Smith, D.L., 2010, International population movements and regional Plasmodium falciparum malaria elimination strategies, Proceedings of the National Academy of Sciences, 107: 24.

    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 analysed with network analysis tools, P. 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 can inform the design of malaria elimination strategies by identifying regional communities of countries afforded protection from re-colonisation by surrounding regions of reduced migration. For more isolated countries, a nationally-focussed 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.

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    Snow RW, Alegana VA, Okiro EA, Gething PW, Patil P, Tatem A.J., Linard C, Moloney G, Borle M, Yusuf FE, Amran J, Noor AM. Estimating the Plasmodium falciparum morbidity and mortality burden 2005 and 2009 in Somalia: Combining models of population distribution, time-space changes in malaria infection risk and the epidemiology of malaria disease burden. Report prepared for UNICEF-Somalia, March 2010.

    To effectively guide malaria control and understand how interventions impact on transmission of the parasite it is important to map where people live in relation to the intensity of malaria transmission. We developed a model of human population settlement interpolated across space to define the distributions of people at risk of malaria. We used parasite prevalence data assembled from 1657 community surveys to spatially model the distribution of malaria risk at 1x1 km resolutions. This model showed a good correlation between predicted and observed estimates of infection in a withheld test data set (2005-07: R2 = 0.76; Mean error = -2.7%; 2008-09: R2 = 0.71; Mean error = -3.1%). We the created three strata of malaria risk that related to disease epidemiology. Following a search for malaria-specific incidence data on clinical attacks and direct causes of death due to Plasmodium falciparum we estimated the median estimates (and ranges) of disease outcome under the three transmission strata. Using the combined models of population, infection and disease outcome we have estimated that in 2005 there may have been approximately 1.73 million clinical attacks of P. falciparum malaria according to the modeled malaria endemicity and population projections during this period. At the end of the period 2008-2009 populations exposed to high transmission had decreased dramatically; consequently the modeled predictions of the number of clinical attacks in 2009 was 57% lower than 2005 with approximately 740,000 clinical cases and a 67% reduction in malaria-specific mortality to approximately 7,460 deaths. The majority of transmission intensity change between 2005 and 2009 occurred in South Central Somalia. Whether the small incremental increase in insecticide treated net coverage (2005: 7% -2009: 22%) and increased investment in disease management were responsible for this change or changes in rainfall were responsible remains uncertain. The models however provide an opportunity to explore these plausibility arguments in more detail. Interestingly we compared other possible models of morbidity estimation using incomplete health information systems and assuming a fixed rate of non-presentation to the formal health facilities in Somalia and predicted a morbidity burden of 630,000 cases in 2009. While lower than the epidemiological model approach both approaches are within a presumed margin of uncertainty and these comparative approaches deserve further attention


    Tatem, A.J., Guerra, C.A., Kabaria, C.W., Noor, A.M., Hay, S.I. Human population, urban settlement patterns and their impact on Plasmodium falciparum malaria endemicity. Malaria Journal, 2008. 7:218.

    BACKGROUND: 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 (PfPR) 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. METHODS: First, an existing population surface was examined to determine if it was sufficiently detailed to be used reliably as a mask to identify areas of very low and very high population density as malaria free regions. Second, the potential of international travel and health guidelines (ITHGs) for identifying malaria free cities was examined. Third, the differences in PfPR values between surveys conducted in author defined rural and urban areas were examined. Fourth, the ability of various global urban extent maps to reliably discriminate these author-based classifications of urban and rural in the PfPR database was investigated. Finally, the urban map that most accurately replicated the author-based classifications was analysed to examine the effects of urban classifications on PfPR values across the entire MAP database. RESULTS: Masks of zero population density excluded many non-zero PfPR surveys, indicating that the population surface was not detailed enough to define areas of zero transmission resulting from low population densities. In contrast, the ITHGs enabled the identification and mapping of 53 malaria free urban areas within endemic countries. Comparison of PfPR survey results showed significant differences between author-defined urban and rural designations in Africa, but not for the remainder of the malaria endemic world. The Global Rural Urban Mapping Project (GRUMP) urban extent mask proved most accurate for mapping these author-defined rural and urban locations, and further sub-divisions of urban extents into urban and peri-urban classes enabled the effects of high population densities on malaria transmission to be mapped and quantified. CONCLUSION: The availability of detailed, contemporary census and urban extent data for the construction of coherent and accurate global spatial population databases is often poor. These known sources of uncertainty in population surfaces and urban maps have the potential to be incorporated into future malaria burden estimates. Currently, insufficient spatial information exists globally to identify areas accurately where population density is low enough to impact upon transmission. Medical intelligence does however exist to reliably identify malaria free cities. Moreover, in Africa, urban areas that have a significant effect on malaria transmission can be mapped.

    Link to Paper.