Multiple change point analysis and time series forecast of brucellosis reports in Xinjiang, China from 2010 to 2024

Brucellosis represents a significant global challenge; however, epidemiological research on brucellosis in Xinjiang from a change point perspective has been lacking. This study aims to identify significant change points and trends, as well as forecast the number of brucellosis cases in Xinjiang, China, thereby offering recommendations for its prevention and control.

Change points were identified through binary segmentation of the full dataset. The Autoregressive Integrated Moving Average (ARIMA) Model, Support Vector Regression (SVR), and ARIMA-SVR models were employed to forecast the number of reported brucellosis cases. Model performance was evaluated using RMSE, MAE, and MAPE, and the optimal model was selected to predict the monthly cases from 2025 to 2027.

The results showed five change points in the monthly brucellosis time series. The highest average number of reported brucellosis cases occurred between the fifth change point (January 2023) and the end of the series (December 2024). The ARIMA-SVR model outperformed both the ARIMA and SVR models in predicting brucellosis cases. It is noteworthy that the forecasted results indicate that brucellosis cases will remain at historically high levels over the next three years, with the peak months potentially shifting from June to May and July.

Change point analysis holds significant value in the field of epidemiology. The ARIMA-SVR model is suitable for predicting the incidence of brucellosis in Xinjiang, China. It is anticipated that the disease burden of brucellosis in Xinjiang will remain at a high level in the future, and local health authorities should continue to implement stringent targeted prevention and control measures. These research findings provide valuable insights for subsequent epidemiological studies and the development of a brucellosis early warning system.