Large-scale spatial population databases in infectious disease research

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spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data.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

Figure 1

Spatial and temporal characteristics of available census data. a) Year of the last national census data available (data source: GeoHive [41]) and b) average spatial resolution (ASR) of census data used in the construction of Gridded Population of the World version 3 (GPW3) and the Global Rural Urban Mapping Project (GRUMP). The ASR measures the effective resolution of administrative units in kilometers. It is calculated as the square root of the land area divided by the number of administrative units [42]. It can be thought of as the "cell size" if all units in a country were square and of equal size.

Figure 2

Schematic illustrations of population distribution modelling methods. The population of two administrative units A and B (with total population in A = 8 and total population in B = 16) are redistributed according to different population distribution modelling approaches (areal weighted, pycnophylactic and dasymetric). In the dasymetric method, a higher weight was attributed to the red hatched area.

Figure 3

Selected examples of existing global and continental population datasets. LandScan 2008, GRUMP beta version, GPW3, UNEP Africa and AfriPop for a) a region in Kenya where census data is very detailed and b) a region of Angola where census data is coarse.