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Large-scale spatial population databases in infectious disease research

Record type: Article

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spatial models must rely on existing global and regional datasets on population distribution, which are often based on outdated and coarse resolution data.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

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Citation

Linard, C. and Tatem, A.J. (2012) Large-scale spatial population databases in infectious disease research International Journal of Health Geographics, 11, p. 7. (doi:10.1186/1476-072X-11-7). (PMID:22433126).

More information

Published date: 20 March 2012
Keywords: human population, global, infectious diseases, spatial demography, health metrics
Organisations: University of Southampton, PHEW – P (Population Health)

Identifiers

Local EPrints ID: 344433
URI: http://eprints.soton.ac.uk/id/eprint/344433
ISSN: 1476-072X
PURE UUID: ce57ce16-04ad-4b7b-b3a8-3243bd2afca2
ORCID for A.J. Tatem: ORCID iD orcid.org/0000-0002-7270-941X

Catalogue record

Date deposited: 05 Nov 2012 12:23
Last modified: 18 Jul 2017 05:16

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