Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa

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Abstract

Over 200 million malaria cases globally lead to half-million deaths annually. The development of malaria prevalence prediction systems to support malaria care pathways has been hindered by lack of data, a tendency towards universal “monolithic” models (one-size-fits-all-regions) and a focus on long lead time predictions. Current systems do not provide short-term local predictions at an accuracy suitable for deployment in clinical practice. Here we show a data-driven approach that reliably produces one-month-ahead prevalence prediction within a densely populated all-year-round malaria metropolis of over 3.5 million inhabitants situated in Nigeria which has one of the largest global burdens of P. falciparum malaria. We estimate one-month-ahead prevalence in a unique 22-years prospective regional dataset of > 9 × 104 participants attending our healthcare services. Our system agrees with both magnitude and direction of the prediction on validation data achieving MAE ≤ 6 × 10–2, MSE ≤ 7 × 10–3, PCC (median 0.63, IQR 0.3) and with more than 80% of estimates within a (+ 0.1 to − 0.05) error-tolerance range which is clinically relevant for decision-support in our holoendemic setting. Our data-driven approach could facilitate healthcare systems to harness their own data to support local malaria care pathways.

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Brown, B. J., Manescu, P., Przybylski, A. A., Caccioli, F., Oyinloye, G., Elmi, M., … Fernandez-Reyes, D. (2020). Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa. Scientific Reports, 10(1). https://doi.org/10.1038/s41598-020-72575-6

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