AI-based prediction of dengue incidence using climatic, environmental, and socio-demographic factors: an ensemble random forest approach with agile system development
1
Issued Date
2026-12-01
Resource Type
eISSN
14712334
Scopus ID
2-s2.0-105043428331
Pubmed ID
42082932
Journal Title
BMC Infectious Diseases
Volume
26
Issue
1
Rights Holder(s)
SCOPUS
Bibliographic Citation
BMC Infectious Diseases Vol.26 No.1 (2026)
Suggested Citation
Satoto T.B.T., Pascawati N.A., Frutos R., Erizal, Garjito T.A., Salim M.F. AI-based prediction of dengue incidence using climatic, environmental, and socio-demographic factors: an ensemble random forest approach with agile system development. BMC Infectious Diseases Vol.26 No.1 (2026). doi:10.1186/s12879-026-13270-1 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/117813
Title
AI-based prediction of dengue incidence using climatic, environmental, and socio-demographic factors: an ensemble random forest approach with agile system development
Author's Affiliation
Xiamen University
Universitas Gadjah Mada
Universitas Airlangga
Badan Riset dan Inovasi Nasional
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Interactions Hôtes-Vecteurs-Parasites-Environnement Dans les Maladies Tropicales Négligées dues aux Trypanosomatides (INTERTRYP)
Universitas Respati Yogyakarta
Universitas Gadjah Mada
Universitas Airlangga
Badan Riset dan Inovasi Nasional
Faculty of Medicine Ramathibodi Hospital, Mahidol University
Interactions Hôtes-Vecteurs-Parasites-Environnement Dans les Maladies Tropicales Négligées dues aux Trypanosomatides (INTERTRYP)
Universitas Respati Yogyakarta
Corresponding Author(s)
Other Contributor(s)
Abstract
Background: Dengue transmission in Indonesia is shaped by interacting climatic, environmental, and socio-demographic factors, yet most forecasting systems remain static and vulnerable to data shifts. There is a critical need for adaptive, data-driven early-warning frameworks that integrate multiple predictor domains while preventing methodological biases such as information leakage. This study aimed to develop a Random Forest (RF)–based predictive model embedded within an Agile System Development workflow to forecast monthly dengue case counts in Yogyakarta. Methods: Monthly dengue case counts from five districts (2017–2022) were modeled using multi-domain predictors. All preprocessing steps—including imputation, standardization, correlation screening, VIF diagnostics, and Negative Binomial GLM–based feature screening—were performed exclusively on the 2017–2021 training subset, with parameters applied unchanged to the 2022 test set. The GLM served solely as a leakage-free exploratory screening tool. A Random Forest model was trained using optimized hyperparameters (500 trees, max depth 10) and evaluated through temporal testing. Model reliability was assessed using calibration curves, prediction-interval metrics, and a one-month early-warning classification evaluated with sensitivity, specificity, PPV, and NPV. Results: The RF model achieved strong predictive performance (R² = 0.86; RMSE = 5.72), exceeding the GLM benchmark (R² = 0.64). Rainfall lag-1, temperature, and humidity emerged as dominant predictors, complemented by built-up area and population density. Calibration indicated good agreement across routine transmission ranges, with reduced reliability during outbreak peaks. The early-warning component demonstrated high sensitivity (0.82) and strong negative predictive value (0.86), supporting its use as a decision-support indicator of elevated transmission risk. Conclusion: The proposed Agile–AI framework demonstrates the potential to deliver accurate dengue risk predictions with interpretable uncertainty estimates within a flexible, multi-domain early-warning architecture. While external validation and further refinement are required, the framework offers a scalable foundation for adaptive dengue surveillance and targeted vector-control decision support in dynamic tropical settings.