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

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dc.creatorTatem, Andrew J.
dc.creatorAdamo, Susana
dc.creatorBharti, Nita
dc.creatorBurgert, Clara R.
dc.creatorCastro, Marcia
dc.creatorDorelien, Audrey
dc.creatorFink, Gunter
dc.creatorLinard, Catherine
dc.creatorJohn, Mendelsohn
dc.creatorMontana, Livia
dc.creatorMontgomery, Mark R.
dc.creatorNelson, Andrew
dc.creatorNoor, Abdisalan M.
dc.creatorPindolia, Deepa
dc.creatorYetman, Greg
dc.creatorBalk, Deborah
dc.date2012-12
dc.date2024-12-19T12:55:26Z
dc.date2024-12-19T12:55:26Z
dc.date.accessioned2026-06-27T04:08:00Z
dc.descriptionThe 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.
dc.identifierhttps://hdl.handle.net/10568/165772
dc.identifier.urihttp://hdl.handle.net/123456789/22615
dc.languageen
dc.publisherSpringer
dc.rightsOpen Access
dc.sourceTatem, Andrew J; Adamo, Susana; Bharti, Nita; Burgert, Clara R; Castro, Marcia; Dorelien, Audrey; Fink, Gunter; Linard, Catherine; John, Mendelsohn; Montana, Livia; Montgomery, Mark R; Nelson, Andrew; Noor, Abdisalan M; Pindolia, Deepa; Yetman, Greg and Balk, Deborah. 2012. Mapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation. Popul Health Metrics, Volume 10, no. 1
dc.subjectepidemiology
dc.subjectgeographical information systems
dc.subjectglobal positioning systems
dc.subjecthealth
dc.subjecthuman diseases
dc.titleMapping populations at risk: improving spatial demographic data for infectious disease modeling and metric derivation
dc.typeJournal Article

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Instituto Internacional de Investigación del Arroz (IRRI)