Abstract
BackgroundMalaria transmission rates in Africa can vary dramatically over the space of a few kilometres. This spatial heterogeneity reflects variation in vector mosquito habitat and presents an important obstacle to the efficient allocation of malaria control resources. Malaria control is further complicated by combinations of vector species that respond differently to control interventions. Recent modelling innovations make it possible to predict vector distributions and extrapolate malaria risk continentally, but these risk mapping efforts have not yet bridged the spatial gap to guide on-the-ground control efforts.Methodology/Principal FindingsWe used Maximum Entropy with purpose-built, high resolution land cover data and other environmental factors to model the spatial distributions of the three dominant malaria vector species in a 94,000 km2 region of east Africa. Remotely sensed land cover was necessary in each vector's niche model. Seasonality of precipitation and maximum annual temperature also contributed to niche models for Anopheles arabiensis and An. funestus s.l. (AUC 0.989 and 0.991, respectively), but cold season precipitation and elevation were important for An. gambiae s.s. (AUC 0.997). Although these niche models appear highly accurate, the critical test is whether they improve predictions of malaria prevalence in human populations. Vector habitat within 1.5 km of community-based malaria prevalence measurements interacts with elevation to substantially improve predictions of Plasmodium falciparum prevalence in children. The inclusion of the mechanistic link between malaria prevalence and vector habitat greatly improves the precision and accuracy of prevalence predictions (r2 = 0.83 including vector habitat, or r2 = 0.50 without vector habitat). Predictions including vector habitat are unbiased (observations vs. model predictions of prevalence: slope = 1.02). Using this model, we generate a high resolution map of predicted malaria prevalence throughout the study region.Conclusions/SignificanceThe interaction between mosquito niche space and microclimate along elevational gradients indicates worrisome potential for climate and land use changes to exacerbate malaria resurgence in the east African highlands. Nevertheless, it is possible to direct interventions precisely to ameliorate potential impacts.
Highlights
Malaria is the leading cause of death in African children, accounting for approximately 20% of all-cause mortality in children under the age of five [1]
Below average separability was measured between rice/irrigated agriculture and broadleafed deciduous forest, and between acacia scrubland and rainfed crops, which could affect the prediction of vector habitat due to the importance of these land cover types for vector species in Tanzania
Satellite-derived data is being increasingly employed to generate maps and predictive models of malaria risk, usually at a global or continental scale [3,8,10,12]. While these maps provide useful indications of the distribution and dynamics of malaria vectors and/or malaria transmission across the African continent, and may be used to project large-scale changes in malaria distribution under different climate change scenarios [10,49], they are of limited operational use for targeting malaria control interventions [4]
Summary
Malaria is the leading cause of death in African children, accounting for approximately 20% of all-cause mortality in children under the age of five [1]. Ecological niche models use these environmental factors to predict the generalized distributions of malaria vectors across Africa [8,9,10,11]. These models can be used to predict malaria risk continentally [12]. Malaria transmission rates in Africa can vary dramatically over the space of a few kilometres. This spatial heterogeneity reflects variation in vector mosquito habitat and presents an important obstacle to the efficient allocation of malaria control resources. Recent modelling innovations make it possible to predict vector distributions and extrapolate malaria risk continentally, but these risk mapping efforts have not yet bridged the spatial gap to guide on-the-ground control efforts
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