The Impact of Climate Variability on the Spatial Dynamics of Malaria in the Global South: A Systematic Review of Modelling Studies, with a Focus on Vulnerable Populations

<title>Abstract</title> <p>Malaria remains one of the infectious diseases most strongly influenced by climate variability and climate change, with transmission dynamics closely linked to environmental conditions that affect both mosquito vectors and parasite development. Across malaria-endemic regions of the Global South, changes in temperature, rainfall patterns, humidity, and ecological suitability are altering malaria transmission, while rapid population growth, land-use change, and persistent socioeconomic inequalities may further increase the risk of disease resurgence and geographic expansion. This systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to synthesize modelling studies published between 2010 and 2025 that examined the influence of climatic and environmental factors on malaria transmission in the Global South. Searches were conducted in PubMed, Embase, Scopus, and Web of Science. Eligible studies applied spatial, statistical, machine-learning, or hybrid modelling approaches to human malaria data and incorporated climatic or environmental predictors. Data were extracted on geographic setting, modelling methodology, climatic determinants, validation practices, and consideration of vulnerable populations. A total of 39 studies met the inclusion criteria. Most studies were conducted in Africa (n = 26), followed by Asia (n = 11) and the Middle East and North Africa (n = 2). Mathematical and statistical models were the most frequently used approaches, followed by machine-learning and hybrid models. Temperature and rainfall emerged as the most consistently reported climatic drivers of malaria transmission, being examined in 92.3% and 76.9% of studies, respectively. Other important predictors included humidity, vegetation indices, elevation, and land surface temperature. Thirteen studies explicitly examined vulnerable populations, particularly children under five years of age, pregnant women, and low-income rural communities, highlighting the combined effects of climate variability, poverty, and limited access to healthcare. Machine-learning and hybrid approaches generally demonstrated superior predictive performance, whereas statistical models provided greater interpretability. The evidence indicates that climate variability is a major determinant of malaria risk across the Global South. Future research should prioritize integrated modelling frameworks that combine climatic, environmental, and socioeconomic data to support climate-resilient malaria control, strengthen early-warning systems, and improve protection of vulnerable populations.</p>