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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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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]

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