Veneel Kumar PonnadaCivil and Environmental Engineering Department,College of Engineering & Computing Florida International University, Miami, FL 33174AbstractMalaria transmission models commonly incorporate spatial environmental and climate variability for making regional predictions of disease risk. However a mismatch of these models typical spatial resolutions and the characteristic scale of malaria vector population dynamics may confound disease risk predictions in areas of high spatial hydrological variability such as the Sahel region of Africa.This model is applied and compared to two African Sahel villages, Banizoumbou and Zindarou Niger, to predict internal variability in malaria vector mosquito populations that lead to variations in malaria transmission. Using a high resolution, small-scale distributed hydrology model that incorporates remotely sensed data for land cover and topography, they simulate the formation and persistence of the pools constituting the primary breeding habitat of Anopheles Gambiae s.l mosquitoes. For each individual adult mosquito the model tracks attributes relevant to population dynamics and malaria transmission, which are updated as mosquitoes interact with the environment, humans, and animals. Weekly field observations war made in 2005 and 2006. A 16% increase in rainfall between the two years
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