Description
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Human malaria is one of the most important public health problems in many African countries. In this book, a C++ - based, stochastic, spatially-explicit, predictive, computational model was developed, which is biologically rich, weather data-driven, and parameterized by field data, to simulate Anopheles metapopulation dynamics towards understanding and validating the seasonal dynamics of this vector. This is aimed at providing a potential tool towards achieving the reduction and suppression of this vector. It is also to provide insight into effective, efficient and novel control strategies towards the eradication of malaria. Results produced by the model from several simulations were validated with real-life CDC light trap, CBT and HLC (Human Landing Catch) Anopheles mosquitos' field trap collection data from Macha, Zambia. The resulting model was shown to be a good, effective and potential tool for malaria control. This is a good book for computer scientists, computational biologists, high-profile researchers, public health analysts and health professionals all over the world.
