biostudies-literatureOluwagbemi OONIAID NIH HHSNHGRI NIH HHSe68040https://www.ebi.ac.uk/biostudies/studies/S-EPMC3704604biostudies-literatureUnknown8(7)Anopheles mosquitoes transmit malaria, a major public health problem among many African countries. One of the most effective methods to control malaria is by controlling the Anopheles mosquito vectors that transmit the parasites. Mathematical models have both predictive and explorative utility to investigate the pros and cons of different malaria control strategies. We have developed a C++ based, stochastic spatially explicit model (ANOSPEX; Ano pheles Spatially-Explicit) to simulate Anopheles metapopulation dynamics. The model is biologically rich, parameterized by field data, and driven by field-collected weather data from Macha, Zambia. To preliminarily validate ANOSPEX, simulation results were compared to field mosquito collection data from Macha; simulated and observed dynamics were similar. The ANOSPEX model will be useful in a predictive and exploratory manner to develop, evaluate and implement traditional and novel strategies to control malaria, and for understanding the environmental forces driving Anopheles population dynamics.PloS oneANOSPEX: a stochastic, spatially explicit model for studying Anopheles metapopulation dynamics.PMC3704604R21AI088311R21 AI088311U41 HG006941Oluwagbemi OONorris DEFornadel CMAdebiyi EFRasgon JLfalseANOSPEX: a stochastic, spatially explicit model for studying Anopheles metapopulation dynamics.Anopheles mosquitoes transmit malaria, a major public health problem among many African countries. One of the most effective methods to control malaria is by controlling the Anopheles mosquito vectors that transmit the parasites. Mathematical models have both predictive and explorative utility to investigate the pros and cons of different malaria control strategies. We have developed a C++ based, stochastic spatially explicit model (ANOSPEX; Ano pheles Spatially-Explicit) to simulate Anopheles metapopulation dynamics. The model is biologically rich, parameterized by field data, and driven by field-collected weather data from Macha, Zambia. To preliminarily validate ANOSPEX, simulation results were compared to field mosquito collection data from Macha; simulated and observed dynamics were similar. The ANOSPEX model will be useful in a predictive and exploratory manner to develop, evaluate and implement traditional and novel strategies to control malaria, and for understanding the environmental forces driving Anopheles population dynamics.2013-01-01T00:00:00Z20132026-06-28T03:15:54.94Z2019-03-26T23:15:06ZS-EPMC37046042386184710.1371/journal.pone.0068040