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Optimizing Child Nutrition Investments for Increased Impact in High-Risk Populations in Kenya

Anthony Ngugi of Aga Khan University in Kenya will use a modeling approach to determine the optimal allocation of limited child nutrition budgets that will most effectively reduce mortality and morbidities, like stunting and anemia, caused by malnutrition. They will use the Optima Nutrition modeling tool, which combines cost functions with an epidemiological model, to make predictions about the cost-efficacy of different funding allocations, for example on food fortification or education. They will focus on the 24 counties in Kenya with the highest burdens of malnutrition and assemble local academics and collaborators at the National Ministry of Health to help collect and harmonize data for the modelling analysis, including existing nutrition-related datasets and health budgets. They will run and validate the model, and then test different optimization algorithms to identify the most effective funding allocations.

More information about Data Science Approaches to Improve Maternal and Child Health in Africa

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