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: Zeitschriftenbeitrag
Hinweise zur Quelle: article number: 102 (2025)
Enthalten in den Sammlungen:Publications without full text

Zur Langanzeige

Google ScholarTM

Prüfe

HAW Katalog

Prüfe

Volltext ergänzen

Feedback zu diesem Datensatz


Diese Ressource wurde unter folgender Copyright-Bestimmung veröffentlicht: Lizenz von Creative Commons Creative Commons