A Comparative Study of Time Series Analysis for Infectious Disease Trends in Thailand.

In the present, the incidence of infectious diseases has been increasing in Thailand. Consequently, there is an imperative need to strategize and implement comprehensive measures for disease prevention, effective control, and precise outbreak prediction. Currently, time series analysis models have been developed to predict disease outbreaks, but there hasn't been a suitable comparison of models for different diseases, including Hand Foot Mouth, Dengue, and Measles. This study aims to use time series analysis to compare models for predicting the Hand Foot Mouth, Dengue (HFMD), and Measles using the Seasonal ARIMA (SARIMA) model. The study found that the model with the lowest Mean Absolute Percentage Error (MAPE) is for Hand Foot Mouth disease (MAPE 24.79%), which tends to have frequent outbreaks in July every year. On the other hand, the Measles model has the highest MAPE (52.21%). This underscores the critical importance for public health authorities to proactively address and mitigate the risk of potential outbreaks every year. Therefore, the choice of using the ARIMA model should take into consideration diseases with clear and distinct seasonal pattern characteristics. If the patterns are not clear, it may be necessary to consider using mathematical models for disease outbreak prediction.