Abstract
Scrub typhus poses a serious public health risk globally. Forecasting the occurrence of the disease is essential for policymakers to develop prevention and control strategies. This study investigated the application of modelling techniques to predict the occurrence of scrub typhus and establishes an early warning system aimed at providing a foundational reference for its effective prevention and control. In this study, the monthly occurrence of scrub typhus in Ganzhou City from January 2008 to December 2022 was utilized as the training set for the first part of the analysis, while the data from January 2008 to December 2019 served as the training set for the second part. Based(1) on these data, the SARIMA model, the BPNN model, and the combined SARIMA-BPNN model were developed and validated using data from January to December 2023. The most effective model was then selected to predict the number of occurrences of scrub typhus for the years 2024 and 2025, respectively. The root mean square error (RMSE) and mean absolute error (MAE) of the BPNN (3-9-1) model, developed using data from January 2008 to December 2022, were 8.472 and 6.4, respectively. In contrast, the RMSE and MAE of the combined SARIMA-BPNN (1-9-1) model, constructed using data from January 2008 to December 2019, were 19.361 and 16.178, respectively. In addition, the BPNN (3-9-1) model predicted 284 cases of scrub typhus in Ganzhou City for 2024, and 163 cases for 2025. The BPNN (3-9-1) model demonstrated strong applicability in predicting the monthly occurrence of scrub typhus. Furthermore, incorporating three years of data on the occurrence of new crown outbreaks when developing a predictive model for infectious diseases can substantially enhance prediction accuracy.