Explainable Machine Learning for Predicting Global Malaria Incidence From 2000 to 2022 With Climatic and Extreme Weather Conditions Across 78 Endemic Countries: A Retrospective Ecological Study
Md. Abu Bokkor Shiddik, Md. Abdullah Al Fahad SohanABSTRACT
Background and Aims
Malaria remains a major global health concern, causing substantial morbidity and mortality, especially in tropical and subtropical regions with limited healthcare resources. Climatic conditions and extreme weather events are increasingly recognized as critical determinants of malaria transmission, yet their relative contributions remain poorly quantified. This study applied machine learning (ML) and explainable AI (XAI) to identify the key climatic and extreme weather determinants of malaria, assess their relative importance, and predict incidence.
Methods
Malaria incidence was analyzed across 78 endemic countries (2000–2022) using climatic and extreme weather variables from the World Health Organization (WHO), the EM‐DAT International Disaster Database, and the Global Data Lab. Random forest (RF) model was trained to predict malaria incidence, with performance assessed using the coefficient of determination ( R 2 ), root mean square error (RMSE), and mean absolute error (MAE). To enhance interpretability and identify influential predictors, explainable AI (XAI) was applied using SHapley Additive exPlanations (SHAP).
Results
Malaria incidence declined steadily across the 78 endemic countries over the study period, remaining highest in Sub‐Saharan Africa. Random forest achieved the best overall predictive performance for malaria incidence ( R 2 = 0.88, 95% CI: 0.84–0.92; RMSE = 39.21, 95% CI: 35.29–43.13; MAE = 27.33, 95% CI: 23.41–31.25) with gradient boosting performing comparably. SHAP analysis identified the leading predictors associated with malaria incidence as average annual surface temperature and average annual precipitation, followed by storm‐ and flood‐affected population metrics. Climatic variables contributed substantially more to model predictions than disaster‐related indicators.
Conclusion
Random forest integrated with explainable AI provides an effective and interpretable framework for predicting malaria incidence and identifying its key climatic predictors, while quantifying the secondary contribution of extreme weather exposure. These findings can support evidence‐based, climate‐adaptive public health planning for malaria prevention and control.