ArticleScientific reports2020
Data-driven malaria prevalence prediction in large densely populated urban holoendemic sub-Saharan West Africa.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Ensemble Machine Learning for Malaria Diagnosis in Resource-Limited Settings Using Clinical and Demographic Features.Infectious disease reports · 2026Article
- Analysis of fractional-order model for the transmission dynamics of malaria via Caputo-Fabrizio and Atangana-Baleanu operators.Scientific reports · 2025Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- Extracellular vesicles could be a putative posttranscriptional regulatory mechanism that shapes intracellular RNA levels in Plasmodium falciparum.Nature communications · 2023Article
- Spatial Optimization Methods for Malaria Risk Mapping in Sub-Saharan African Cities Using Demographic and Health Surveys.GeoHealth · 2023Article
- Specialist hybrid models with asymmetric training for malaria prevalence prediction.Frontiers in public health · 2023Article
- Data-driven computational intelligence applied to dengue outbreak forecasting: a case study at the scale of the city of Natal, RN-Brazil.Scientific reports · 2022Article
- Formulation and In Vitro Evaluation of Oral Capsules from Liquid Herbal Antimalarials Marketed in Ghana.Journal of tropical medicine · 2021Article
Corrections and comments
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Authors and funding
24 authors.
Funding
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
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