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September 10, 2025SustainabilityOpen Access

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

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The Bayesian spatiotemporal model achieved the lowest MAE of 5.543 in dengue forecasting.
Bayesian forecasting provided accurate spatial risk estimates and identified high-risk hotspots effectively.
SARIMA and XGBoost indicated overfitting, while Bayesian methods showed enhanced prediction accuracy.
The study covers dengue incidence in Bandung from 2009 to 2023, highlighting public health management needs.

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Implication

Comparative analysis demonstrates Bayesian model's superior predictive power in dengue incidence forecasting, suggesting better public health strategies.

Cite This Study

Jaya et al. (2025) studied this question.

synapsesocial.com/papers/68c1afb954b1d3bfb60e6f7b https://doi.org/10.3390/su17156777

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Implication

Comparative analysis demonstrates Bayesian model's superior predictive power in dengue incidence forecasting, suggesting better public health strategies.

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

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Implication

Cite This Study

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

Key Points

Discussion

Implication

Cite This Study

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