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

Brown, BJ; Manescu, P; Przybylski, AA; Caccioli, F; Oyinloye, G; Elmi, M; Shaw, MJ; ... Fernandez-Reyes, D; + view all (2020) Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa. Scientific Reports , 10 (1) , Article 15918. 10.1038/s41598-020-72575-6. Green open access

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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 × 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.

Type: Article
Title: Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1038/s41598-020-72575-6
Publisher version: https://doi.org/10.1038/s41598-020-72575-6
Language: English
Additional information: © 2020 Springer Nature Limited. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
Keywords: Computer science, Infectious diseases, Malaria, Public health
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Life Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10111414
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