Dataset Information

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

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.

SUBMITTER: Brown BJ

PROVIDER: S-EPMC7522256 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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Publications

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

Brown Biobele J BJ Manescu Petru P Przybylski Alexander A AA Caccioli Fabio F Oyinloye Gbeminiyi G Elmi Muna M Shaw Michael J MJ Pawar Vijay V Claveau Remy R Shawe-Taylor John J Srinivasan Mandayam A MA Afolabi Nathaniel K NK Rees Geraint G Orimadegun Adebola E AE Ajetunmobi Wasiu A WA Akinkunmi Francis F Kowobari Olayinka O Osinusi Kikelomo K Akinbami Felix O FO Omokhodion Samuel S Shokunbi Wuraola A WA Lagunju Ikeoluwa I Sodeinde Olugbemiro O Fernandez-Reyes Delmiro D

Scientific reports 20200928 1

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 produc ...[more]

PMID: 32985514

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Dataset Information

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

Publications

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

Similar Datasets

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