Explainable Spatio-Temporal Graph Neural Networks for Dengue Outbreak Prediction: Environmental Data Integration and Policy Recommendation Framework in Yogyakarta, Indonesia

Background Dengue fever is among the most rapidly spreading vector-borne diseases globally and a critical public health challenge in Indonesia, requiring evidence-based decisions supported by interpretable artificial intelligence (AI) systems rather than black-box prediction models with limited practical utility. Objective To demonstrate how an explainable Spatio-Temporal Graph Neural Network (STGNN) can generate actionable policy recommendations for dengue control by integrating environmental and surveillance data through interpretable AI mechanisms. Methods We developed Explain Dengue, a web-based platform implementing an STGNN with multi-head temporal attention, a graph attention network (GAT), and bidirectional Long Short-Term Memory (LSTM) networks, trained on dengue surveillance data (2021–2025) from Yogyakarta Province, Indonesia, using a chronological 70/10/20 train/validation/test split and an explicit 4-week forecast horizon, validated on 1300 weekly observations across five regions. Results Across five independently trained models, the STGNN achieved a mean (SD) test MAE of 7.50 (±0.48) cases/week and R2 of 0.265 (±0.102), outperforming seasonal-naive, gradient-boosting, and LSTM-only baselines. Using a statistical endemic-channel risk classification method (binary alert/no alert), the system achieved 77.6% accuracy (84.6% recall, 45.2% precision for alerts). Learned spatial attention was highly reproducible across seeds (r = 0.95), but an ablation-based faithfulness check found it was not a reliable causal explanation of predictions (r = −0.11), a limitation we discuss transparently. Conclusion A strictly held-out STGNN can outperform baselines for weekly dengue forecasting, offering a more evidence-based foundation for deploying explainable AI in vector-borne disease control, while surfacing an important caveat about the causal interpretability of learned attention.