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The causes of local variation in the prevalence of malaria were investigated in rural Gambia. Cross-sectional prevalence surveys were carried out among 1184 young children (aged 6 months–5 years) in 48 villages, at the end of the transmission season in 1996. Villages were categorized according to distance from the nearest vector breeding sites, and the patterns of malaria transmission, infection and disease compared. Children living in villages within 3 km of breeding sites experienced more infective bites, and higher prevalences of parasitaemia and spleen enlargement than less-exposed children living further away. Clinical illness, in contrast, was more common among infected children who were less exposed. Infected children living 3 km or more from breeding sites were more likely to have high-density parasitaemia (odds ratio [OR] = 1 · 98), fever (OR = 2 · 60) and high-density parasitaemia together with fever (OR = 3 · 17). Clinical attacks did not decline in older children, as seen amongst children who were more exposed. These findings show that significant differences in the risk of infection and clinical attacks can occur over very short distances. The age at which protective immunity is acquired may be delayed in villages where transmission intensity is lower, thus increasing the risk of a clinical attack following infection. Communities with the lowest vector densities may be those at greatest risk of disease.

Background For malaria control in Africa it is crucial to characterise the dispersal of its most efficient vector, Anopheles gambiae, in order to target interventions and assess their impact spatially. Our study is, we believe, the first to present a statistical model of dispersal probability against distance from breeding habitat to human settlements for this important disease vector. Methods/Principal Findings We undertook post-hoc analyses of mosquito catches made in The Gambia to derive statistical dispersal functions for An. gambiae sensu lato collected in 48 villages at varying distances to alluvial larval habitat along the River Gambia. The proportion dispersing declined exponentially with distance, and we estimated that 90% of movements were within 1.7 km. Although a ‘heavy-tailed’ distribution is considered biologically more plausible due to active dispersal by mosquitoes seeking blood meals, there was no statistical basis for choosing it over a negative exponential distribution. Using a simple random walk model with daily survival and movements previously recorded in Burkina Faso, we were able to reproduce the dispersal probabilities observed in The Gambia. Conclusions/Significance Our results provide an important quantification of the probability of An. gambiae s.l. dispersal in a rural African setting typical of many parts of the continent. However, dispersal will be landscape specific and in order to generalise to other spatial configurations of habitat and hosts it will be necessary to produce tractable models of mosquito movements for operational use. We show that simple random walk models have potential. Consequently, there is a pressing need for new empirical studies of An. gambiae survival and movements in different settings to drive this development.

Understanding local variability in malaria transmission risk is critically important when designing intervention or vaccine trials. Using a combination of field data, satellite image analysis, and GIS modeling, we developed a high-resolution map of malaria entomological inoculation rates (EIR) in The Gambia, West Africa. The analyses are based on the variation in exposure to malaria parasites experienced in 48 villages in 1996 and 21 villages in 1997. The entomological inoculation rate (EIR) varied from 0 to 166 infective bites per person per rainy season. Detailed field surveys identified the major Anopheles gambiae s.l. breeding habitats. These habitats were mapped by classification of a LANDSAT TM satellite image with an overall accuracy of 85%. Village EIRs decreased as a power function based on the breeding areas size and proximity. We use this relationship and the breeding habitats to map the variation in EIR over the entire 2500-km2 study area.