BMC public health · 2025
Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models.
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Tang Dandan, Jin Yuanyuan, Hu XuanJie, Lin Dandan, Kapar Abiden, Wang YanJie, Yang Fang, Li Huling
Аннотация
OBJECTIVE: AIDS is a highly fatal infectious disease of Class B, and Xinjiang is a high-incidence region for AIDS in China. The core of prevention and control lies in early monitoring and early warning. This study aims to identify the best model for predicting the monthly AIDS incidence in Xinjiang, providing scientific evidence for AIDS prevention and control.
METHODS: Monthly AIDS incidence data from January 2004 to December 2020 in Xinjiang were collected. Six different models, including the ARIMA (2,1,2) model, ARIMA (2,1,2)-EGARCH (2,2) combined model, ARIMA (2,1,2)-TGARCH (1,1) combined model, ETS (A, A, A) model, XGBoost model, and LSTM model, were used for fitting and forecasting.
RESULTS: All models were able to capture the overall trend of the monthly AIDS incidence in Xinjiang. In terms of RMSE and MAE, the ETS (A, A, A) model performed the best, achieving the smallest values. For the MAPE metric, the ARIMA (2,1,2)-TGARCH (1,1) model performed the best. Considering RMSE, MAE, and MAPE together, the ETS (A, A, A) model was the best-performing model in this study. The LSTM model also showed good predictive performance, while the XGBoost model and ARIMA (2,1,2) model performed relatively poorly.
CONCLUSION: The ETS (A, A, A) model is the best model for predicting the monthly AIDS incidence in Xinjiang. Deep learning models (such as LSTM) have significant potential in time series forecasting. The XGBoost model and ARIMA (2,1,2) model may have limitations when handling time series data, and future improvements or combinations could enhance prediction performance.
METHODS: Monthly AIDS incidence data from January 2004 to December 2020 in Xinjiang were collected. Six different models, including the ARIMA (2,1,2) model, ARIMA (2,1,2)-EGARCH (2,2) combined model, ARIMA (2,1,2)-TGARCH (1,1) combined model, ETS (A, A, A) model, XGBoost model, and LSTM model, were used for fitting and forecasting.
RESULTS: All models were able to capture the overall trend of the monthly AIDS incidence in Xinjiang. In terms of RMSE and MAE, the ETS (A, A, A) model performed the best, achieving the smallest values. For the MAPE metric, the ARIMA (2,1,2)-TGARCH (1,1) model performed the best. Considering RMSE, MAE, and MAPE together, the ETS (A, A, A) model was the best-performing model in this study. The LSTM model also showed good predictive performance, while the XGBoost model and ARIMA (2,1,2) model performed relatively poorly.
CONCLUSION: The ETS (A, A, A) model is the best model for predicting the monthly AIDS incidence in Xinjiang. Deep learning models (such as LSTM) have significant potential in time series forecasting. The XGBoost model and ARIMA (2,1,2) model may have limitations when handling time series data, and future improvements or combinations could enhance prediction performance.