Abstract:
Dengue fever is a growing public health concern in the Philippines, with rising
incidence rates and the need for more accurate early outbreak prediction tools.
Traditional methods of determining epidemic thresholds often rely solely on statistical
incidence rates and fail to account for dynamic factors such as climate
variability and lagged effects. Building on the work of Pantolla and Gonzaga,
this study introduces a system that combines threshold regression and machine
learning to forecast dengue incidence and detect periods of hyperendemicity and
epidemicity. The threshold regression model incorporates climate variables (rainfall,
temperature, and humidity) and identifies critical thresholds (71 and 89 cases)
for outbreak classification. The system also trains and compares multiple machine
learning models, including SVM, Random Forest, XGBoost, ARIMA, SARIMA,
RNN, GRU, and LSTM, with SVM achieving the best performance (MAPE =
33.58). This integrated approach enhances early warning capabilities and offers
an easy-to-use accessible platform for local health authorities to support timely
intervention and outbreak management.
