Malaria Risk Modelling and Prediction, V ulnerability of Communities to M alaria in the C ontext of Climate Change in the Northern part of Benin, West Africa

Abstract

Africa stands as the most susceptible continent to the ramifications of changes in climate. The repercussions of climate change on human well-being have garnered increased scrutiny in recent times. Malaria, a prevalent vector-borne ailment, emerges as one of the principal diseases profoundly influenced by climatic variations in West Africa. Malaria is the leading cause of mortality in Benin. Malaria Prevention and Reduction Poses Significant Challenges in Benin due to Prevalent Poverty, environmental challenges, and the economic status of the country. This study aims to model the effect of climate change and vegetation health on the transmission of malaria and develop an intelligent outbreak warning system for the prediction of the incidence of malaria in Northern Benin. In addition, the study assesses the vulnerability of the community to malaria. Monthly data on climatic variables and the number of malaria cases were collected over the period 1991 to 2021 and 2009 to 2021 respectively. As well as the weekly Vegetation Health Index data. The study used Mann-Kendall, Sen's slope, and PETITT tests to characterise the climate of the study area. Pearson correlation and structural equation model were used to assess climate change’s impact on the malaria transmission. Different regression algorithms were applied to model the impact of malaria transmission due to climate change. We predict the incidence of malaria in northern Benin over 2021-2050 period using Cordex Africa data. An online malaria early warning web application was developed using the Streamlit framework. The impact of vegetation on the infection of malaria was determined, and a malaria forecast model was developed using vegetation health indices. The vulnerability of the community is assessed using socio-economic and environmental data. PCA was used to determine the weight of each indicator. The findings revealed that temperature and relative humidity are the major climatic factors influencing malaria transmission in northern Benin. An advanced model for malaria epidemics predicts 82% malaria incidence, with an increase in 2021–2050 under RCP4.5 and RCP8.5 and a decrease under RCP8.5 over 2021-2030. The web-based application developed predicts accurately the malaria outbreak risk. Moisture (TCI) predicts 75% of the monthly malaria cases during intense mosquito activities, while 78% of the monthly occurrence of malaria is predicted by the Vegetation Health Index (VHI) and soil temperature index (TCI). Materi, Cobli, Boukoumbe, and Perere districts exhibit the highest vulnerability to malaria in northern Benin. The findings of this research provide tools for stakeholders and policymakers at different levels. They can use these tools to take target actions to lessen the transmission of malaria in Benin and mitigate the impact of climate change on the community’s health by developing adaptation strategies.