Assessing the impact of climate and control interventions on spatio-temporal malaria dynamics using a stochastic metapopulation model.

Despite intensive malaria control efforts, the lowlands of western Kenya continue to experience high malaria transmission. Spatial and temporal variations in climatic factors, interventions, parasite dispersal, and human travel, influence malaria incidence in moderate-to-high transmission areas. Additionally, population movement facilitates the importation of parasites from endemic to non-endemic areas, sustaining infections where local transmission would otherwise be unsustainable. The aim of this work was to develop a process-based stochastic metapopulation transmission model that accounts for key mechanisms of malaria dynamics, such as immunity, infectivity, and migration, while considering both the host and vector mobility. The model also incorporates and quantifies the effects of malaria interventions and climate variability at the local scale. Unlike existing models that often consider these drivers in isolation, our framework captures their joint influence within a single, mechanistic system. We show that, between 2008 and 2019, the developed metapopulation model accurately captured the effects of small-scale heterogeneity at the subpopulation level in western Kenya. Although demonstrated in a Kenyan context, the model is generalisable to other endemic regions and can support localized forecasting and intervention planning under future climate scenarios. Finaly, we assess its potential to forecast malaria incidence at the spatial-unit level, by integrating future climatic conditions with intervention scenarios.