Exploratory data analysis and forecasting of dengue outbreaks in Pangasinan using the ARIMA model

Abstract

Dengue fever remains a critical public health concern in tropical countries like the Philippines, with Pangasinan frequently experiencing outbreaks due to favorable environmental conditions for mosquito breeding. Despite ongoing efforts to control the disease, the absence of a reliable forecasting tool limits the ability of health authorities to implement proactive measures. This study developed a forecasting model using the autoregressive integrated moving average (ARIMA) technique, following an initial exploratory data analysis (EDA) to identify trends and patterns in historical dengue case data from 2019 to 2024. The ARIMA model was trained and validated using historical data, capturing seasonal variations and projecting future dengue outbreaks. The evaluation metrics, including mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE), indicated that the model achieved an accuracy of approximately 78.3%, suggesting reasonable predictive capability. Forecasts for the year 2025 indicate a potential rise in dengue cases, particularly during peak seasons, aligning with observed historical trends. These predictions offer valuable insights for local health authorities, enabling them to plan targeted interventions, allocate resources efficiently, and mitigate the impact of future outbreaks. The study demonstrates the practical application of time series analysis in public health forecasting and provides a proactive tool tailored for the needs of Pangasinan.