We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.
- Authors
Brown, Biobele J.; Manescu, Petru; Przybylski, Alexander A.; Caccioli, Fabio; Oyinloye, Gbeminiyi; Elmi, Muna; Shaw, Michael J.; Pawar, Vijay; Claveau, Remy; Shawe-Taylor, John; Srinivasan, Mandayam A.; Afolabi, Nathaniel K.; Rees, Geraint; Orimadegun, Adebola E.; Ajetunmobi, Wasiu A.; Akinkunmi, Francis; Kowobari, Olayinka; Osinusi, Kikelomo; Akinbami, Felix O.; Omokhodion, Samuel
- 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.
- Subjects
MALARIA; PLASMODIUM falciparum; DISEASE prevalence; MEDICARE; FEVER
- Publication
Scientific Reports, 2020, Vol 10, Issue 1, pN.PAG
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-020-72575-6