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

Camille Morlighem1,2, Celia Chaiban1,2, Stefanos Georganos3

  • 1Department of Geography University of Namur Namur Belgium.

Geohealth
|October 9, 2023
PubMed
Summary
This summary is machine-generated.

Spatial optimization methods can improve intra-urban malaria risk mapping in sub-Saharan Africa (SSA) by addressing data displacement issues. However, the accuracy of these malaria risk maps is limited by the quality of epidemiological data.

Keywords:
DHSrandom forestremote sensingsub‐Saharan Africaurban malaria

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Area of Science:

  • Environmental science
  • Public health
  • Geographic information systems (GIS)

Background:

  • Vector-borne diseases like malaria are exacerbated by urban growth and climate change in sub-Saharan Africa (SSA).
  • Intra-urban malaria risk maps are crucial for targeted interventions in resource-limited settings.
  • Demographic and Health Surveys (DHS) offer national malaria data but are spatially displaced, limiting intra-urban scale application.

Purpose of the Study:

  • To predict intra-urban malaria risk in SSA cities (Dakar, Dar es Salaam, Kampala, Ouagadougou).
  • To investigate the efficacy of spatial optimization methods in overcoming DHS spatial displacement.
  • To identify potential adaptations for DHS sampling strategies for improved intra-urban malaria risk prediction.

Main Methods:

  • Malaria risk was modeled using a random forest regressor.
  • Remotely sensed covariates (urban climate, land cover, land use) were utilized.
  • Several spatial optimization approaches were tested to mitigate DHS coordinate displacement effects.

Main Results:

  • Spatial optimization techniques reduced the impact of DHS spatial displacement on predictive performance.
  • These methods incurred higher computational costs.
  • The percentage of variance explained remained low (30%-40%), indicating limitations due to epidemiological data quality.

Conclusions:

  • Spatial optimization shows promise for improving intra-urban malaria risk mapping despite data limitations.
  • Current methods cannot fully compensate for the inherent data quality issues in DHS for fine-scale mapping.
  • Adaptations to DHS sampling strategies are recommended to enhance reliability for intra-urban malaria risk prediction.