Department of Geography, West African Science Service Centre On Climate Change and Adapted Land Use (WASCAL), Faculty of Human and Social Sciences, University of Lomé, Lomé, Togo.
Department of Zoology, Laboratory of Ecology and Ecotoxicology, Faculty of Sciences, University of Lomé, 1BP: 1515, Lomé, Togo.
BMC Public Health. 2024 Feb 13;24(1):450. doi: 10.1186/s12889-024-17847-w.
Malaria is one of the major vector-borne diseases most sensitive to climatic change in West Africa. The prevention and reduction of malaria are very difficult in Benin due to poverty, economic insatiability and the non control of environmental determinants. This study aims to develop an intelligent outbreak malaria early warning model driven by monthly time series climatic variables in the northern part of Benin.
Climate data from nine rain gauge stations and malaria incidence data from 2009 to 2021 were extracted from the National Meteorological Agency (METEO) and the Ministry of Health of Benin, respectively. Projected relative humidity and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4). A structural equation model was employed to determine the effects of climatic variables on malaria incidence. We developed an intelligent malaria early warning model to predict the prevalence of malaria using machine learning by applying three machine learning algorithms, including linear regression (LiR), support vector machine (SVM), and negative binomial regression (NBiR).
Two ecological factors such as factor 1 (related to average mean relative humidity, average maximum relative humidity, and average maximal temperature) and factor 2 (related to average minimal temperature) affect the incidence of malaria. Support vector machine regression is the best-performing algorithm, predicting 82% of malaria incidence in the northern part of Benin. The projection reveals an increase in malaria incidence under RCP4.5 and RCP8.5 over the studied period.
These results reveal that the northern part of Benin is at high risk of malaria, and specific malaria control programs are urged to reduce the risk of malaria.
疟疾是西非对气候变化最敏感的主要虫媒病之一。由于贫困、经济需求和环境决定因素不受控制，贝宁的疟疾预防和减少工作非常困难。本研究旨在开发一种由贝宁北部月时间序列气候变量驱动的智能疟疾暴发预警模型。
从国家气象局（METEO）和贝宁卫生部分别提取了 9 个雨量站的气候数据和 2009 年至 2021 年的疟疾发病率数据。从协调区域降尺度实验（CORDEX）的罗西中心区域大气区域气候模型（RCA4）模拟中获得预测相对湿度和温度。结构方程模型用于确定气候变量对疟疾发病率的影响。我们使用机器学习通过应用三种机器学习算法，包括线性回归（LiR）、支持向量机（SVM）和负二项式回归（NBiR），开发了一种智能疟疾预警模型来预测疟疾的流行率。
两个生态因素，因子 1（与平均平均相对湿度、平均最大相对湿度和平均最大温度有关）和因子 2（与平均最低温度有关）影响疟疾的发病率。支持向量机回归是表现最好的算法，预测贝宁北部 82%的疟疾发病率。预测显示，在研究期间，RCP4.5 和 RCP8.5 下疟疾发病率增加。
这些结果表明，贝宁北部地区疟疾风险较高，需要制定具体的疟疾控制计划来降低疟疾风险。
