Abstract
This study aims to understand the epidemic characteristics of acute hemorrhagic conjunctivitis (AHC) in China and to explore the application value of the Bayesian Time Structure Sequence (BSTS) model. The reported data of AHC cases in China were collected from January 2011 to October 2022. R software was used to construct the BSTS and the Differential Autoregressive Integrated Moving Average (ARIMA) models based on the AHC incidence data from January 2011 to December 2021. The prediction effect of both models was compared by using the data from January to October 2022, and finally the incidence of AHC in China from November 2022 to December 2023 was predicted by the BSTS model. The actual value of AHC incidence in July 2022 under the ARIMA model was not within 95% CI of the predicted value, and these under the BSTS model was within 95% CI of the predicted value. 26,474 new AHC cases were predicted using the BSTS model in China from November 2022 to December 2023. The prediction performance of the BSTS model was better than that of the ARIMA model, and it has a had high application value for the prediction of AHC epidemic trends.