Cui Weihong, Lyu Yanling, Liu Haiyun, Ma He, Zhang Shuxia, Li Na, Liu Juan. Incidence characteristics and prediction of hepatitis E in Yantai, Shandong, 2007–2021[J]. Disease Surveillance, 2023, 38(8): 929-933. DOI: 10.3784/jbjc.202301050562
Citation: Cui Weihong, Lyu Yanling, Liu Haiyun, Ma He, Zhang Shuxia, Li Na, Liu Juan. Incidence characteristics and prediction of hepatitis E in Yantai, Shandong, 2007–2021[J]. Disease Surveillance, 2023, 38(8): 929-933. DOI: 10.3784/jbjc.202301050562

Incidence characteristics and prediction of hepatitis E in Yantai, Shandong, 2007–2021

  •   Objective  To investigate the incidence of hepatitis E in Yantai, Shandong province, and evaluate the application of seasonal model in the prediction of the incidence of hepatitis E.
      Methods  SAS 9.2 was used to analyze the incidence of hepatitis E in Yantai from 2007 to 2021 and seasonal autoregressive integrated moving average (SARIMA) models were constructed. The best model was selected to predict the incidence of hepatitis E from 2022 to 2023.
      Results  The incidence of hepatitis E in Yantai showed a fluctuating decline trend with obvious seasonality, the incidence peak was during February-March and the sub-peak was during September-October. The optimal model of SARIMA (1,0,1) (0,1,1)12 was constructed after data smoothing, parameter estimation and model testing. The Mean Absolute Percentage Error (MAPE) value was 10.034%, indicating good fitting effect, and the predication indicated that the monthly incidence of hepatitis E would show a fluctuating decline trend in the following two years, it would be about 0.206 per 100 000 by the end of 2023. The incidence of hepatitis E would be significantly higher in the coastal urban areas of Zhifu, Fushan, Laishan and Penglai than in other areas.
      Conclusion  SARIMA model has a good short-term prediction ability for the incidence of hepatitis E. Attention needs to be paid to the hepatitis E prevention and control in the coastal areas of northern and eastern Yantai according to the prediction results.
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