ArticleArchives of public health = Archives belges de sante publique2025
Unleashing the power of intelligence: revolutionizing malaria outbreak preparedness with an advanced warning system in Benin, West Africa.
Article in Archives of public health = Archives belges de sante publique, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Recent advances in machine learning and Bayesian modeling for tropical disease prediction, diagnosis, and risk analysis: a scoping review.BMC infectious diseases · 2026Article
- Artificial intelligence for precision malaria control: transforming surveillance, prediction, and intervention strategies.Frontiers in digital health · 2026Review
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Authors and funding
5 authors.
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Abstract
backgroundMalaria is a significant vector-borne disease that exhibits high sensitivity to climatic variations within the West African region. In Benin, the effective prevention and mitigation of malaria pose considerable challenges, primarily due to the prevailing conditions of poverty and environmental adversities. This study endeavours to devise an advanced system for early detection and warning of malaria outbreaks in the northern part of Benin, employing monthly time series data pertaining to climatic variables.
methodsMonthly climate data were sourced from Meteorological Agency of Benin (METEO-Benin), alongside malaria incidence data procured from the database of the Benin Ministry of Health, that covered the timeframe of 2009-2021. To ascertain the influence of climatic variables on malaria incidence, principal component analysis was applied. Subsequently, an intelligent model for forecasting malaria outbreaks was developed using support vector machine (SVM) algorithm. The developed model for malaria outbreaks was then employed to establish an intelligent system for warning and forecasting malaria incidence on a monthly basis, utilising the Meteostat platform, an online weather data service provider, in conjunction with the Streamlit framework. This application exhibits responsiveness and compatibility across all web browsers.
resultsRelative humidity and maximal temperature significantly influence malaria incidence in the northern region of Benin. SVM regression algorithm forecasts 80% prediction rate for malaria incidence. Consequently, the intelligent malaria outbreak warning system was successfully devised, enabling the automatic and manual prediction of monthly malaria incidence rates within the districts of northern Benin.
conclusionsThis system serves as a valuable tool for stakeholders and policymakers, facilitating proactive measures to curtail malaria transmission in Benin.
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