Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province.

Malaria remains a notable public health challenge in endemic regions, with an estimated 263 million cases and 579,000 malaria-related deaths globally in 2023. Climate and environmental factors, such as temperature, rainfall, and the Normalised Difference Vegetation Index (NDVI), play a crucial role in malaria transmission. While statistical models aid in malaria prediction, Bayesian methods remain underutilised despite their ability to incorporate prior knowledge into predictive models. The major contribution of this study is to develop a Bayesian malaria prediction model incorporating climate and environmental data. Both objective and subjective prior distributions are evaluated to determine their effectiveness in improving model performance. The results indicate that a subjective prior outperforms other priors. Additionally, Ehlanzeni (Mpumalanga), Vhembe and Mopani districts (Limpopo) are identified as high-risk malaria regions. The findings suggest that malaria transmission peaks in summer and autumn, particularly in areas where temperatures during the night range from 12°C-20°C, rainfall is moderate (100–200 mm), and NDVI exceeds 0.6. Malaria risk intensifies following months of accumulated rainfall, creating optimal mosquito breeding conditions. These insights may assist malaria control programmes in developing targeted interventions, such as early warning systems and vector management strategies. Future research will explore Bayesian machine learning for malaria prediction.