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Predicting malaria epidemics in Burkina Faso with machine learning

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  • David Harvey
  • Wessel Valkenburg
  • Amara Amara

Abstract

Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here we present the first data-driven malaria epidemic early warning system that can predict the 13-week case rate in a primary health facility in Burkina Faso. Using the extraordinarily high-fidelity data of infant consultations taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the weekly number of malaria cases over a 13 week period. We test our algorithm on historical epidemics and find that for our lowest threshold for an epidemic alert, our algorithm has 30% precision with > 99% recall at raising an alert. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively.

Suggested Citation

  • David Harvey & Wessel Valkenburg & Amara Amara, 2021. "Predicting malaria epidemics in Burkina Faso with machine learning," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
    • Handle: RePEc:plo:pone00:0253302
    DOI: 10.1371/journal.pone.0253302
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    References listed on IDEAS

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    1. Manal Alghamdi & Mouaz Al-Mallah & Steven Keteyian & Clinton Brawner & Jonathan Ehrman & Sherif Sakr, 2017. "Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project," PLOS ONE, Public Library of Science, vol. 12(7), pages 1-15, July.
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