Abstract
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spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data.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.
Highlights
Mapping and modelling methods used to study the spatial distribution and spread of vector-borne and directly transmitted infectious diseases are becoming increasingly widespread and sophisticated as the field of spatial epidemiology grows
The available documentation of LandScan only enables a general understanding of the methodologies used. These global population distribution datasets that have been created at spatial resolutions of finer than 1 degree have been used in various epidemiological studies, and these are reviewed below
Spatial methods and tools are widely used in infectious disease research and have led to significant advances in our understanding of disease dynamics, surveillance and control [1,2,17,18]
Summary
Mapping and modelling methods used to study the spatial distribution and spread of vector-borne and directly transmitted infectious diseases are becoming increasingly widespread and sophisticated as the field of spatial epidemiology grows. Large variations exist in the spatial resolution of available census data, as the ways in which national territories are divided and the administrative level at which population data are collected and summarized vary by country. The available documentation of LandScan only enables a general understanding of the methodologies used These global population distribution datasets that have been created at spatial resolutions of finer than 1 degree have been used in various epidemiological studies, and these are reviewed below. Different sources of error and uncertainty are associated with these population datasets, which generally arise from (i) the input
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