Climatic and social health determinants of malaria incidence in Tete, Mozambique: Bayesian spatio-temporal modeling of routine data (2016-2022).

This study aimed to investigate the spatio-temporal patterns and to assess climatic and social determinants of health as predictors of malaria incidence in Tete Province, Mozambique, from 2016 to 2022. The analysis was based on the INLA (Integrated Nested Laplace Approximations) approach, applied to a hierarchical Bayesian model that employed a reparameterized Besag spatial structure, an AR1 temporal process, and Type IV space-time interactions. To capture seasonality, first- and second-order Fourier terms were incorporated. Malaria case data were obtained from the SISMA platform, climatic variables from the National Institute of Meteorology and NASA (National Aeronautics and Space Administration), literacy rate data from the National Institute of Statistics, and mosquito net distribution data from social services. The results indicated that precipitation and illiteracy rate were significant predictors, associated with an estimated increase of 6.7% and 1.6% in malaria incidence, respectively. Temporally, incidence tends to rise shortly after the onset of the rainy season and decline during the winter months. Spatially, the districts of Macanga, Chiuta, and Moatize showed higher risk, while Tete City exhibited lower incidence. The study concludes that climatic and social factors, together with spatio-temporal dynamics, play a decisive role in shaping malaria transmission dynamics in the region.