ArticlePloS one2021
Predicting malaria epidemics in Burkina Faso with machine learning.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- The use of artificial intelligence based modelling techniques in One Health-related infectious disease studies in Sub-Saharan Africa: a review.Frontiers in artificial intelligence · 2026Pooled it
- Machine Learning Used in Communicable Disease Control: A Scoping Review.Public health reviews · 2026Pooled it
- Pooled it
- Advances in mosquito-borne disease surveillance using machine learning.New microbes and new infections · 2026Article
- Interdisciplinary human-centric architectural framework for One Health surveillance, early warning, and response: leveraging AI modelling and IoT environmental monitoring.Frontiers in public health · 2026Article
- Advancing Early Warning Systems for Malaria: Progress, challenges, and future directions - A scoping review.PLOS global public health · 2025Article
- An optimized transfer learning approach integrating deep convolutional feature extractors for malaria parasite classification in erythrocyte microscopy.Frontiers in medicine · 2025Article
- Impact of Climate Variability and Interventions on Malaria Incidence and Forecasting in Burkina Faso.International journal of environmental research and public health · 2024Article
- Information and Communication Technology to Enhance the Implementation of the Integrated Management of Childhood Illness: A Systematic Review and Meta-Analysis.Mayo Clinic proceedings. Digital health · 2024Review
- Prediction of the Risk Distributions for Anopheles sinensis, a Vector for Malaria in Shanghai, China.The American journal of tropical medicine and hygiene · 2023Article
- Climatic, land-use and socio-economic factors can predict malaria dynamics at fine spatial scales relevant to local health actors: Evidence from rural Madagascar.PLOS global public health · 2023Article
- Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurately forecasting the case rate of malaria would enable key decision makers to intervene months before the onset of any outbreak, potentially saving lives. Until now, methods that forecast malaria have involved complicated numerical simulations that model transmission through a community. Here we present the first data-driven malaria epidemic early warning system that can predict the 13-week case rate in a primary health facility in Burkina Faso. Using the extraordinarily high-fidelity data of infant consultations taken from the Integrated e-Diagnostic Approach (IeDA) system that has been rolled out throughout Burkina Faso, we train a combination of Gaussian Processes and Random Forest Regressors to estimate the weekly number of malaria cases over a 13 week period. We test our algorithm on historical epidemics and find that for our lowest threshold for an epidemic alert, our algorithm has 30% precision with > 99% recall at raising an alert. This rises to > 99% precision and 5% recall for the high alert threshold. Our two-tailed predictions have an average 1σ and 2σ precision of 5 cases and 30 cases respectively.
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