Spatiotemporal Dengue Forecasting for Sustainable Public Health in Bandung, Indonesia: A Comparative Study of Classical, Machine Learning, and Bayesian Models

I Gede Nyoman Mindra Jaya (), Yudhie Andriyana, Bertho Tantular, Sinta Septi Pangastuti and Farah Kristiani
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I Gede Nyoman Mindra Jaya: Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia
Yudhie Andriyana: Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia
Bertho Tantular: Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia
Sinta Septi Pangastuti: Department of Statistics, Universitas Padjadjaran, Sumedang 45363, Indonesia
Farah Kristiani: Department of Mathematics, Parahyangan Catholic University, Kota Bandung 40141, Indonesia

Sustainability, 2025, vol. 17, issue 15, 1-26

Abstract: Accurate dengue forecasting is essential for sustainable public health planning, especially in tropical regions where the disease remains a persistent threat. This study evaluates the predictive performance of seven modeling approaches—Seasonal Autoregressive Integrated Moving Average (SARIMA), Extreme Gradient Boosting (XGBoost), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Convolutional LSTM (CNN–LSTM), and a Bayesian spatiotemporal model—using monthly dengue incidence data from 2009 to 2023 in Bandung City, Indonesia. Model performance was assessed using MAE, sMAPE, RMSE, and Pearson’s correlation (R). Among all models, the Bayesian spatiotemporal model achieved the best performance, with the lowest MAE (5.543), sMAPE (62.137), and RMSE (7.482), and the highest R (0.723). While SARIMA and XGBoost showed signs of overfitting, the Bayesian model not only delivered more accurate forecasts but also produced spatial risk estimates and identified high-risk hotspots via exceedance probabilities. These features make it particularly valuable for developing early warning systems and guiding targeted public health interventions, supporting the broader goals of sustainable disease management.

Keywords: sustainability; SDGs; Bayesian; machine and deep learning; spatiotemporal; dengue disease; forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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