Time Series Model-Based Assessment of the Impact of COVID-19 on Hepatitis E in China and Prediction of Its Epidemic Trend

Abstract

Background: The COVID-19 pandemic not only had a long-term impact on healthcare but also changed the epidemic trends of diseases. The impact of COVID-19 on hepatitis E is still unclear. Objectives: The aims of the study were to assess the impact of the COVID-19 pandemic on hepatitis E incidence and establish a prediction model to predict the trend of hepatitis E in China. Methods: Monitored data on the incidence of hepatitis E in China from January 2012 to July 2022 were collected. The causal impact of the COVID-19 pandemic on hepatitis E incidence in China was explored using intervention analysis under the Bayesian structured time series (BSTS) model. The BSTS and autoregressive (AR) integrated moving average (ARIMA) models were established using training and testing sets, respectively, and the predictive performance of the models was compared. Results: It was found that there were seasonal fluctuations in the hepatitis E incidence in China. The number of monthly average hepatitis E cases decreased by 32% (95% CI: -40% ~ -23%) from January to December 2020 owing to the COVID-19 pandemic (probability of causal effect: 99.89%, P = 0.001). From January 2020 to July 2022, it decreased by 15% (95% CI: -21% ~ -9.4%). Because the error indicators of mean absolute error (MAD), mean absolute percentage error (MAPE), root mean square error (RMSE), and root mean square percentage error (RMSPE) under the BSTS model were smaller than those under the ARIMA model, the prediction accuracy of the BSTS model was higher. Conclusions: During the COVID-19 pandemic, the overall incidence rate of hepatitis E in China decreased as a result of COVID-19. The BSTS model has strong application value to forecast the hepatitis E trend in China.