Spatio-temporal modeling of malaria with seasonal variation using real data from Thailand

Malaria continues to pose a major public health burden, with transmission shaped by interactions between human hosts, mosquito vectors, and seasonal environmental forces. This study develops a spatio-temporal reaction–diffusion SVIR malaria model that incorporates vaccination, human and mosquito mobility, and a sinusoidal seasonal variation in transmission rates. The model is rigorously analyzed to ensure well-posedness and to characterize threshold dynamics through the basic reproduction number. Real malaria incidence data from Thailand are used for parameter estimation, providing an epidemiologically grounded framework for simulation. Sensitivity analysis highlights the parameters most responsible for driving transmission patterns. Numerical experiments, carried out using finite-difference and meshless methods, show that seasonal forcing plays a central role in shaping incidence peaks, while diffusion smooths localized outbreaks and supports persistent low-level transmission across space. The results indicate that combining vaccination with targeted vector control and mobility-aware interventions can substantially reduce the long-term disease burden. Overall, the model offers a realistic tool for understanding malaria spread in heterogeneous environments and informs strategies for effective, data-driven control.