Forecasting Dengue Fever Incidence in Thailand Using ARIMA: Implications for Public Health Planning 10.32526/ennrj/24/20250069
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Abstract
Dengue fever remains a significant public health concern in Thailand, characterized by recurrent outbreaks and considerable morbidity. Understanding and forecasting temporal patterns of dengue incidence are essential for effective prevention and control strategies. This study analyzed monthly dengue fever incidence in Thailand from 2013 to 2024 and forecasted trends for 2025-2026 using the Autoregressive Integrated Moving Average (ARIMA) model. Data were obtained from the Bureau of Epidemiology, Department of Disease Control, and Ministry of Public Health. The optimal ARIMA (1,0,1) model was selected based on diagnostic criteria including the Autocorrelation Function (ACF), Partial Autocorrelation Function (PACF), and the Ljung-Box test. Model performance was evaluated using the Mean Absolute Percentage Error (MAPE), yielding 43.40%, and indicating moderately accurate predictions for planning purposes. The model successfully captured seasonal trends, with dengue incidence typically peaking mid-year. Forecasts for 2025-2026 indicate periodic fluctuations, with December 2026 projected to have the highest incidence (7,336 cases) and January 2025 the lowest (2,401 cases). While the ARIMA model demonstrated utility in forecasting general trends, its limitations include the inability to incorporate external variables such as climate, vector control programs, vector control efforts, or viral serotype shifts. Despite this, the findings provide actionable insights for public health planning and resource allocation aimed at mitigating future dengue outbreaks in Thailand.
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