Bayesian spatio-temporal modeling and prediction of malaria cases in Tanzania mainland (2016-2023): unveiling associations with climate and intervention factors.

backgroundMalaria continues to pose a significant global health challenge, affecting approximately 200 million individuals annually and resulting in an estimated 600,000 deaths each year. In Tanzania, malaria ranks among the top five most commonly reported diseases in healthcare facilities, thus contributing to a substantial burden on the healthcare system. This study analyzed aggregated monthly malaria count data for the period 2016-2023, to explore spatio-temporal trends in malaria risk and assess the effects of climatic factors and vector control interventions across Tanzania mainland regions.

methodsThe Standardized Incidence Ratio (SIR) was used to assess malaria risk distribution, while a Bayesian spatio-temporal model using integrated nested Laplace approximations (INLA) was employed to evaluate the impact of climatic factors and vector control interventions. The model accounted for spatial and temporal effects by using a Conditional Autoregressive (CAR) dependence structure and a random walk of order two (RW2). The analysis was categorized into two age groups, with a cut-off at 5 years.

resultsThe study recorded a total of 23.4 million malaria cases in individuals aged 5 years and above, and 17.3 million cases in children under 5 years. The SIR and the model results identified regions with high malaria risk, and the model indicated that from 2016 to 2023, the malaria risk decreased by

conclusionThe environmental factors identified in this study, alongside the spatial mapping, are critical for devising targeted malaria control strategies, especially in regions where LLINs have reduced transmission. These findings are essential for identifying high-risk areas in endemic regions and for prioritizing immediate interventions.