AnoSpEx: A Spatially-Explicit Predictive Computational Model for Studying Anopheles Metapopulation Dynamics towards Malaria Control
Abstract:
Human malaria is one of the most important public health problems in many African countries, associated with high rates of mortality and morbidity. The disease presents a spectrum of complications ranging from mild and self-limiting illness to life threatening pathology. Malaria incidence has increased in Africa due to climate change, insecticide and drug resistance, and social/economic issues. As an infectious disease, malaria is most commonly transmitted through the bite of infected female Anopheles mosquitoes. Thus, one of the most effective methods to control the disease is by controlling the Anopheles mosquito vectors that transmit the parasites. Along this line, we developed a C++ based, stochastic spatially-explicit predictive computational model, 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 that can help achieve greater control of malaria. Understanding the seasonal dynamics of Anopheles mosquitoes would provide better platform for improving traditional and novel control interventions at suppressing and reducing the spread 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 mosquitoes field trap collection data from Macha, Zambia. The resulting model was shown to be a good, effective and potential tool for malaria control
ORDER COMPLETE MATERIAL (CHAPTER 1-5)
