Large-scale spatial population databases in infectious disease research

International Journal of Health Geographics

Andrew J Tatem¹  Catherine Linard² 

[1] Fogarty International Center, National Institutes of Health, Bethesda, MD 20892, USA;Fonds National de la Recherche Scientifique (F.R.S.-FNRS), Rue d'Egmont 5, B-1000 Brussels, Belgium

关键词: Health metrics;    Spatial demography;    Infectious diseases;    Global;    Human population;   

Others  :  811956
DOI  :  10.1186/1476-072X-11-7

 received in 2012-01-11, accepted in 2012-03-20,  发布年份 2012

【 摘 要 】

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spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data.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.

【 授权许可】

   
2012 Linard and Tatem; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

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Figure 3.

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