Publisher DOI: | 10.1186/s13690-025-01554-y | Title: | Unleashing the power of intelligence : revolutionizing malaria outbreak preparedness with an advanced warning system in Benin, West Africa | Language: | English | Authors: | Gbaguidi, Gouvidé Jean Topanou, Nikita Leal Filho, Walter ![]() Agboka, Komi Ketoh, Guillaume K. |
Keywords: | Benin; Climate change; Early warning system; Malaria | Issue Date: | 10-Apr-2025 | Publisher: | Archives Belges de Médecine Sociale | Journal or Series Name: | Archives of public health | Volume: | 83 | Issue: | 1 | Abstract: | Background: Malaria 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. Methods: Monthly 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. Results: Relative 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. Conclusions: This system serves as a valuable tool for stakeholders and policymakers, facilitating proactive measures to curtail malaria transmission in Benin. |
URI: | https://hdl.handle.net/20.500.12738/18143 | ISSN: | 2049-3258 | Review status: | This version was peer reviewed (peer review) | Institute: | Department Gesundheitswissenschaften Fakultät Life Sciences Forschungs- und Transferzentrum Nachhaltigkeit und Klimafolgenmanagement |
Type: | Article | Additional note: | article number: 102 (2025) |
Appears in Collections: | Publications without full text |
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