High Resolution Malaria Risk Mapping in Mutasa District, Zimbabwe: Implications for Regaining Control

Mufaro Kanyangarara and her PhD thesis adviser, Luke Mullany of the Johns Hopkins Bloomberg School of Public Health Department of International Health, have been looking into the challenges of controlling and evCategorical maps of predicted household malaria risk and uncertaintyentually eliminating malaria in a multi-country context in southern Africa. We are sharing abstracts from her pioneering work including the following which explores high resolution risk mapping in Zimbabwe near the Mozambique border.

Background: In Zimbabwe, more than half of malaria cases are concentrated in Manicaland province, where malaria continues to rebound despite intensified control strategies. The objectives of this study were to develop a prediction model based on high-resolution environmental risk factors and obtain seasonal malaria risk maps for Mutasa District, one of the worst affected districts in Manicaland Province.

Methods: Household RDT status was obtained from ongoing community-based surveys in Mutasa District from October 2012 through April 2015. While environmental variables were extracted from remote sensing data sources and linked to household RDT status. Logistic regression was used to model the probability of household positivity as a function of the environmental covariates. Model prediction performance and overall model fit were examined. Model predictions and prediction standard errors were generated and inverse distance weighting was used to generate smoothed maps of malaria risk and prediction uncertainty by season.

Results: Between October 2012 and April 2015, 398 households participated in the household surveys. Ninety-six individuals representing 66 households tested RDT positive. Household malaria risk was significantly higher among households sampled during the rainy season and further from the Mozambique border, while malaria risk was lower in sparsely populated areas as well as households located at higher elevations during the rainy season. The resulting maps predicted elevated risk during the rainy season particularly in low-lying areas bordering with Mozambique. In contrast, the risk of malaria was low across the study area during the dry season with foci of malaria scattered along the northern, western and south-eastern peripheries of the study area.

Conclusion: This study provides evidence for the significant heterogeneity of malaria, which was strongly linked to elevation, house density and proximity to the Mozambique border. These findings underscore the need for strong cross-border malaria control initiatives to complement country specific interventions.

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