Trend analysis and prediction of viral hepatitis incidence in China, 2009-2018

Abstract

Objective: To explore the time series characteristics of 5 types of viral hepatitis in China and predict their incidence through effective models. Methods: The monthly incidence data of 5 types of viral hepatitis (A, B, C, D and unspecified) in China from 2009 to 2018 were collected for descriptive and time series analyses, decomposition methods were used to explore the seasonality in the form of seasonal indices and the long-term trend in the form of a linear regression model. Autoregressive integrated moving average (ARIMA) models were established for each type of viral hepatitis. Results: From 2009 to 2018, a total of 14 856 990 cases of viral hepatitis were reported, the seasonal index range of 5 types of viral hepatitis were all lower than 1, the seasonality of hepatitis E was significant, and its incidence was unimodal, but no obvious seasonality characteristics were observed for other four types of viral hepatitis. The incidences of hepatitis A, hepatitis E and unspecified hepatitis remained at lower levels, showing slow declines. Although the cases of hepatitis B accounted for the highest proportion (79.59%, 11 824 262/14 856 990) among 5 types of viral hepatitis, the decline was fastest (-0.01/100 000). The incidence of hepatitis C was on rise, and the rate of increase remained stable (0.005/100 000). The predicted incidences of 5 types of viral hepatitis in China from January 2009 to December 2018 fitted by ARIMA model were consistent with the actual incidences, and the mean absolute error percentage (MAPE) ranged from 3.756 8 to 8.068 3. Conclusions: Time series analysis on surveillance data is useful for better understanding the incidence of the viral hepatitis. The ARIMA model has good application value in the short-term prediction of viral hepatitis incidence in China.