Machine Learning Models for Dengue Forecasting in Singapore
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Abstract (arXiv)
With emerging prevalence beyond traditionally endemic regions, the global burden of dengue disease is forecasted to be one of the fastest growing. With limited direct treatment or vaccination currently available, prevention through vector control is widely believed to be the most effective form of managing outbreaks. This study examines traditional state space models (moving average, autoregressive, ARIMA, SARIMA), supervised learning techniques (XGBoost, SVM, KNN) and deep networks (LSTM, CNN, ConvLSTM) for forecasting weekly dengue cases in Singapore. Meteorological data and search engine trends were included as features for ML techniques. Forecasts using CNNs yielded lowest RMSE in weekly cases in 2019.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 1 of its 42 references that are in the map. First posted on arXiv 29 Jun 2024.
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References · 1
- 2016XGBoost: A Scalable Tree Boosting Systemcited by 5,166
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