摘要：Hotspots constitute the major reservoir for residual malaria transmission, with higher malaria incidence than neighbouring areas, and therefore, have the potential to form the cornerstone for successful intervention strategies. Detection of malaria hotspots is hampered by their heterogenous spatial distribution, and the laborious nature and low sensitivity of the current methods used to assess transmission intensity. We adopt ecological theory underlying foraging in herbivorous insects to vector mosquito host seeking and modelling of fine-scale landscape features at the village level. The overall effect of environmental variables on the density of indoor mosquitoes, sporozoite infected mosquitoes, and malaria incidence, was determined using generalized linear models. Spatial analyses were used to identify hotspots for malaria incidence, as well as malaria vector density and associated sporozoite prevalence. We identify household occupancy and location as the main predictors of vector density, entomological inoculation rate and malaria incidence. We propose that the use of conventional vector control and malaria interventions, integrated with their intensified application targeting predicted hotspots, can be used to reduce malaria incidence in endemic and residual malaria settings.
其他摘要：Abstract Hotspots constitute the major reservoir for residual malaria transmission, with higher malaria incidence than neighbouring areas, and therefore, have the potential to form the cornerstone for successful intervention strategies. Detection of malaria hotspots is hampered by their heterogenous spatial distribution, and the laborious nature and low sensitivity of the current methods used to assess transmission intensity. We adopt ecological theory underlying foraging in herbivorous insects to vector mosquito host seeking and modelling of fine-scale landscape features at the village level. The overall effect of environmental variables on the density of indoor mosquitoes, sporozoite infected mosquitoes, and malaria incidence, was determined using generalized linear models. Spatial analyses were used to identify hotspots for malaria incidence, as well as malaria vector density and associated sporozoite prevalence. We identify household occupancy and location as the main predictors of vector density, entomological inoculation rate and malaria incidence. We propose that the use of conventional vector control and malaria interventions, integrated with their intensified application targeting predicted hotspots, can be used to reduce malaria incidence in endemic and residual malaria settings.
