Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys

Camille Morlighem, Celia Chaiban, Stefanos Georganos, Oscar Brousse, Nicole P.M. Van Lipzig, Eleónore Wolff, Sebastien Dujardin, Catherine Linard

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Abstract

Vector-borne diseases, such as malaria, are affected by the rapid urban growth and climate change in sub-Saharan Africa (SSA). In this context, intra-urban malaria risk maps act as a key decision-making tool for targeting malaria control interventions, especially in resource-limited settings. The Demographic and Health Surveys (DHS) provide a consistent malaria data source for mapping malaria risk at the national scale, but their use is limited at the intra-urban scale because survey cluster coordinates are randomly displaced for ethical reasons. In this research, we focus on predicting intra-urban malaria risk in SSA cities—Dakar, Dar es Salaam, Kampala and Ouagadougou—and investigate the use of spatial optimization methods to overcome the effect of DHS spatial displacement. We modeled malaria risk using a random forest regressor and remotely sensed covariates depicting the urban climate, the land cover and the land use, and we tested several spatial optimization approaches. The use of spatial optimization mitigated the effects of DHS spatial displacement on predictive performance. However, this comes at a higher computational cost, and the percentage of variance explained in our models remained low (around 30%–40%), which suggests that these methods cannot entirely overcome the limited quality of epidemiological data. Building on our results, we highlight potential adaptations to the DHS sampling strategy that would make them more reliable for predicting malaria risk at the intra-urban scale.
Original languageEnglish
Article numbere2023GH000787
JournalGeoHealth
Volume7
Issue number10
DOIs
Publication statusPublished - 6 Oct 2023

Funding

This research was funded by the Belgian Federal Science Policy Office (BELSPO) under the STEREO III programme as part of the REACT (SR/00/337) and REACTION (SR/13/218) projects. Camille Morlighem is a Research Fellow of the Fonds de la Recherche Scientifique (F.R.S.-FNRS). Oscar Brousse is also supported by the Wellcome Trust HEROIC project (Grant 216035/Z/19/Z). The authors thank Robert W. Snow (Population Health Unit, KEMRI-Wellcome Trust Research Programme, Nairobi, Kenya and Centre for Tropical Medicine & Global Health, Nuffield Department of Clinical Medicine, University of Oxford, UK) for assembling and providing the malaria parasite prevalence data sets. This research was funded by the Belgian Federal Science Policy Office (BELSPO) under the STEREO III programme as part of the REACT (SR/00/337) and REACTION (SR/13/218) projects. Camille Morlighem is a Research Fellow of the Fonds de la Recherche Scientifique (F.R.S.\u2010FNRS). Oscar Brousse is also supported by the Wellcome Trust HEROIC project (Grant 216035/Z/19/Z). The authors thank Robert W. Snow (Population Health Unit, KEMRI\u2010Wellcome Trust Research Programme, Nairobi, Kenya and Centre for Tropical Medicine & Global Health, Nuffield Department of Clinical Medicine, University of Oxford, UK) for assembling and providing the malaria parasite prevalence data sets.

FundersFunder number
KEMRI-Wellcome Trust Research Programme
Fonds De La Recherche Scientifique - FNRS
Wellcome Trust216035, 216035/Z/19/Z
Belgian Federal Science Policy OfficeSR/00/337
REACTIONSR/13/218

    Keywords

    • DHS
    • random forest
    • remote sensing
    • sub-Saharan Africa
    • urban malaria

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