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Development of An Automated Early Warning System for Malaria

Kathryn Colborn of University of Colorado Denver in the U.S. will develop a statistical model to predict future outbreaks of malaria and help identify the most effective intervention strategy. Current models can help work out where and why malaria outbreaks occur rather than predicting future outbreaks. They will use supervised machine learning to develop a set of predictive algorithms using available data including weather, demographics, and malaria incidence in children under five years old from Mozambique. From this set, the best algorithm for predicting malaria will be selected by cross validation on an independent dataset, and subsequently tested in the field using monthly malaria case reports.

More information about Design New Analytics Approaches for Malaria Elimination (Round 17)

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View the Grand Challenges partnership network

The Bill & Melinda Gates Foundation is part of the Grand Challenges partnership network. Visit grandchallenges.org to view the map of awarded grants across this network and grant opportunities from partners.