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Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation

Andrew J Tatem123*, Susana Adamo4, Nita Bharti5, Clara R Burgert6, Marcia Castro7, Audrey Dorelien8, Gunter Fink7, Catherine Linard109, Mendelsohn John11, Livia Montana7, Mark R Montgomery1216, Andrew Nelson13, Abdisalan M Noor14, Deepa Pindolia1142, Greg Yetman4 and Deborah Balk15

Author Affiliations

1 Department of Geography, University of Florida, Gainesville, USA

2 Emerging Pathogens Institute, University of Florida, Gainesville, USA

3 Fogarty International Center, National Institutes of Health, Bethesda, USA

4 Center for International Earth Science Information Network (CIESIN), Columbia University, New York, USA

5 Ecology and Evolutionary Biology, Princeton University, Princeton, USA

6 Demographic and Health Surveys, International Health and Development Division, ICF International, Washington DC, USA

7 Department of Global Health and Population, Harvard School of Public Health, Boston, USA

8 Office of Population Research and Woodrow Wilson School of Public and International Affairs, Princeton University, Princeton, USA

9 Biological Control and Spatial Ecology, Université Libre de Bruxelles, Brussels, Belgium

10 Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Brussels, Belgium

11 Research and Information Services of Namibia, Windhoek, Namibia

12 The Population Council, New York, USA

13 The International Rice Research Institute, Los Banos, Philippines

14 Malaria Public Health and Epidemiology Group, Centre for Geographic Medicine, KEMRI - University of Oxford - Wellcome Trust Research Programme, Nairobi, Kenya

15 School of Public Affairs, Baruch College, City University New York, New York, USA

16 Department of Economics, Stony Brook University, New York, USA

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Population Health Metrics 2012, 10:8  doi:10.1186/1478-7954-10-8

Published: 16 May 2012


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.

Population; Epidemiology; Demography; Disease mapping