Global Prediction of Dengue Incidence Using an Explainable Artificial Intelligence‐Driven ConvLSTM Integrating Environmental, Health, and Socio‐Economic Determinants

Background and Aims: Dengue fever is a rapidly expanding vector-borne disease that poses significant global epidemiological and public health challenges. Accurate and interpretable forecasting is essential for timely interventions, yet most models overlook spatiotemporal, sex-specific, and country-level heterogeneity in disease dynamics. This study aimed to develop a robust explainable AI (XAI) framework to predict dengue incidence globally and identify key environmental, health system, and socio-economic drivers. Methods: ². Feature contributions were assessed through multiple XAI approaches: SHAP values, permutation importance, ±50% perturbation sensitivity perturbations, integrated gradients (IG), and layer-wise relevance propagation (LRP). Results: ² = 0.7731), demonstrating suitability for national-level public health planning. Sex-specific analysis revealed that annual freshwater withdrawals (SHAP: 44.37%; IG: 0.41; LRP: 0.38) dominated male dengue incidence, whereas hospital bed density had a greater influence for females (SHAP: 31.86%; IG: 0.34; LRP: 0.32). Temperature anomalies contributed consistently to both sexes (SHAP: 11.51%; IG: 0.18; LRP: 0.17). Country-level contributions highlighted electricity access (India: 97.35%; Bangladesh: 89.62%) and UHC coverage (Bangladesh: 8.33%) as key socio-economic determinants, with environmental and healthcare factors such as community health resources (Afghanistan: 35.42%; Brazil: 9.00%) further shaping sex-specific patterns. Sensitivity analysis indicated dengue incidence varied from -65% to +91% under ±50% predictor perturbations, underscoring model responsiveness and targeted intervention potential. Conclusion: By integrating SHAP, IG, and LRP, the ConvLSTM-XAI framework provides transparent, sex-aware, and country-specific dengue forecasts. The results support targeted, climate-resilient, and equitable dengue control strategies.