Machine Learning Models for Dengue Forecasting in Singapore
This work addresses dengue outbreak management through improved forecasting for public health in Singapore, but it is incremental as it applies existing methods to a specific dataset.
This study tackled the problem of forecasting weekly dengue cases in Singapore by comparing traditional state space models, supervised learning techniques, and deep networks, finding that CNNs achieved the lowest RMSE in 2019.
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.