Negative / Null Result ReportOpen accessMedicine· cited by 10

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; Farah Kristiani · 2025 · Sustainability

WASTE classifies this as Negative / Null Result Report · AI classification, approximate

The study found no significant effect — useful as a negative control or null benchmark for your own design.

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 asse

Abstract by I Gede Nyoman Mindra Jaya; Yudhie Andriyana; Bertho Tantular; Sinta Septi Pangastuti; Farah Kristiani, Sustainability (2025) — licensed CC BY 4.0.

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