Application of mathematical modeling for malaria control decision-making in settings of varying transmission intensity

Planning for the control of Plasmodium falciparum malaria at the population level demands models of malaria epidemiology that provide realistic quantitative prediction of likely epidemiological outcomes of a wide range of control strategies. This project applies mathematical modeling parameterized both generally and with site-specific field data to better understand transmission dynamics of malaria across sites with varying transmission intensity and seasonality, primarily the highlands of western Kenya and in the lowlands of Zambia's Southern Province. Simulation results explore possible epidemiological scenarios for malaria in the presence and absence of a mix of control interventions, and for different amounts and patterns of seasonality of transmission. Together with a cost effectiveness analysis, results form the basis of recommendations for control programs. Individual-based stochastic models of malaria epidemiology were developed by the Swiss Tropical and Public Health Institute (Swiss TPH). To provide the site-specific parameters needed to fit the models to the study areas data on existing entomological, demographic, intervention deployment and health systems was gathered from field studies conducted by collaborating institutes and a literature review. Model simulations were run on an ensemble of models with multiple random seeds on the OpenMalaria simulator. Simulation outputs were compared to the observed data from the study areas in order to assess the validity of the model and a sensitivity analysis was conducted to address uncertainty. The model was then used to predict the impact of different combinations of malaria control interventions, and the impact of different seasonal transmission patterns, on impact measures. The models were able to simulate the transmission patterns of malaria in the study areas of western Kenyan highlands and Zambia lowlands and gain insight into the potential impact of malaria control interventions currently being un- or under- utilized in these areas. Despite the ability of mathematical modeling to be used to translate between measures of malaria transmission and indicators of disease burden in areas where sparse data renders evidence-based programmatic decision-making challenging, these models remain largely inaccessible to program managers. Results from such models can provide public health officials with accurate estimates of transmission, by seasonal pattern, that are necessary for assessing and tailoring malaria control and elimination programs to specific settings.