Article

Modeling malaria risk in East Africa at high-spatial resolution

Kenya Medical Research Institute/Wellcome Trust Research Programme, Nairobi, Kenya.
Tropical Medicine & International Health (Impact Factor: 2.3). 07/2005; 10(6):557-66. DOI: 10.1111/j.1365-3156.2005.01424.x
Source: PubMed

ABSTRACT Malaria risk maps have re-emerged as an important tool for appropriately targeting the limited resources available for malaria control. In Sub-Saharan Africa empirically derived maps using standardized criteria are few and this paper considers the development of a model of malaria risk for East Africa.
Statistical techniques were applied to high spatial resolution remotely sensed, human settlement and land-use data to predict the intensity of malaria transmission as defined according to the childhood parasite ratio (PR) in East Africa. Discriminant analysis was used to train environmental and human settlement predictor variables to distinguish between four classes of PR risk shown to relate to disease outcomes in the region.
Independent empirical estimates of the PR were identified from Kenya, Tanzania and Uganda (n = 330). Surrogate markers of climate recorded on-board earth orbiting satellites, population settlement, elevation and water bodies all contributed significantly to the predictive models of malaria transmission intensity in the sub-region. The accuracy of the model was increased by stratifying East Africa into two ecological zones. In addition, the inclusion of urbanization as a predictor of malaria prevalence, whilst reducing formal accuracy statistics, nevertheless improved the consistency of the predictive map with expert opinion malaria maps. The overall accuracy achieved with ecological zone and urban stratification was 62% with surrogates of precipitation and temperature being among the most discriminating predictors of the PR.
It is possible to achieve a high degree of predictive accuracy for Plasmodium falciparum parasite prevalence in East Africa using high-spatial resolution environmental data. However, discrepancies were evident from mapped outputs from the models which were largely due to poor coverage of malaria training data and the comparable spatial resolution of predictor data. These deficiencies will only be addressed by more random, intensive small areas studies of empirical estimates of PR.

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    • "Faso (Baragatti et al. 2009). Omumbo et al. (2005) identified urbanization as a predictor of malaria prevalence . Moffett et al. (2007) identified the growing population density as major factor in determining the malaria risk in Africa. "
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