Predictive Modelling of Climate-Driven Malaria Transmission for Optimal Control Using a Coupled SEIR-SEI and GIS Framework.

Climate change is a potent intensifier of vector-borne disease dynamics; however, its impact on malaria in pre-elimination settings remains poorly understood. A climate-sensitive transmission model integrated with 11 years of clinical surveillance and facility-level spatial risk data was applied to assess malaria elimination prospects in Zimbabwe's Mberengwa District. Under mid-century warming in a high-emission scenario, the peak infectious prevalence is projected to increase by more than 4-fold, and the duration of high-intensity transmission will increase from 213 to 250 days per year. Decadal seasonal oscillations in case counts were reproduced by the model, whereas recent overestimation reflected intensified elimination activities that suppressed transmission below climate-driven expectations. Sensitivity analysis identified temperature-dependent mosquito mortality as the dominant source of uncertainty, with insecticide-based indoor spraying being the most influential, modifiable intervention. Simulations indicate that high-coverage spraying alone can achieve elimination, and a fully integrated strategy, including bed nets, spraying and gametocyte-targeting therapy, can reduce transmission by 85%. Spatial mapping of 37 health facility catchments highlighted five high-priority hotspots concentrated in the southern and south-eastern health facilities, where climate exposure and the current burden of disease converge. These results provide a locally informed, climate-intelligent framework to support malaria elimination in the context of accelerating climate change.