Epidemiology Forecasting Analysis of Dengue Cases with Seasonal Autoregressive Integrated Moving Average in Davao City, Philippines

Abstract

Dengue is a mosquito-borne illness and an important public health problem in the Philippines, with the highest number of dengue cases in Southeast Asia. The Philippine government has created a dengue surveillance map to track illness transmission; however, model-based methods for forecasting and detecting dengue have not been imposed and are significantly sought-after throughout the country. In this study, a suitable time-based model for predicting dengue cases was used. Using the seasonal autoregressive integrated moving average (SARIMA) model, this study developed a forecasting model to predict dengue cases in Davao City, Philippines from to 2020–2025 based on data gathered from the Department of Health Region XI from years 2013–2019. The root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as criteria for choosing an accurate forecasting model, and the QGIS version 3.16. was used for mapping. The results showed a monthly variation in the spike of dengue cases, peaking during July and August in both past and forecasted cases. The combined forecast model was found to be the best-fitting model for predicting future dengue incidences in the administrative districts of Davao City for the years 2020–2025.