1. Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.
- Author
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Brown BJ, Manescu P, Przybylski AA, Caccioli F, Oyinloye G, Elmi M, Shaw MJ, Pawar V, Claveau R, Shawe-Taylor J, Srinivasan MA, Afolabi NK, Rees G, Orimadegun AE, Ajetunmobi WA, Akinkunmi F, Kowobari O, Osinusi K, Akinbami FO, Omokhodion S, Shokunbi WA, Lagunju I, Sodeinde O, and Fernandez-Reyes D
- Subjects
- Africa South of the Sahara epidemiology, Africa, Western epidemiology, Humans, Models, Theoretical, Prevalence, Prospective Studies, Malaria epidemiology, Urban Population
- 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 × 10
4 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.- Published
- 2020
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