  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.
