Mathematical models are important tools for decision making in the control of infectious diseases, and malaria was one of the first infections for which such modeling was applied. However, there is still an urgent need for new models that can compare the potential impact of a comprehensive range of malaria interventions. To address this need we have developed a platform for stochastic simulations of malaria infections, nested within simulations of individuals in human populations.
The simulations of malaria infections are linked to models of interventions and health systems, epidemiology to predict the impacts of interventions on infection, morbidity, mortality, health services use and costs. We use numerous field datasets to optimise parameter estimates. By using a volunteer computing system we obtain the enormous computational power required for model fitting, sensitivity analysis, and exploration of many different intervention strategies.
The project provides a general platform for comparing, fitting, and evaluating different model structures, and for quantitative prediction of effects of different interventions and integrated control programs.
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