Journal of Shandong University (Health Sciences) ›› 2018, Vol. 56 ›› Issue (12): 103-108.doi: 10.6040/j.issn.1671-7554.0.2018.551

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Application of multiple seasonal autoregressive integrated moving average model in the prediction of viral hepatitis A

LI Xinyang, LI Sujuan, LIU Xiaodi, FAN Antong, YAN Ping, LIU Hongqing   

  1. School of Public Health and Management, Weifang Medical University, Weifang 261053, Shandong, China
  • Published:2022-09-27

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Abstract: Objective To establish a multiple seasonal autoregressive integrated moving average(ARIMA)model, predict the national incidence of hepatitis A, and provide decision-making basis for the prevention and control of hepatitis A. Methods Based on the monthly data of the National Hepatitis A Report from Jan. 2011 to Dec. 2016, the multiple seasonal model of ARIMA was established, whose predictive effect was evaluated with the number of cases from Jan. to Dec. 2017. Results The parameters were below: unseasonal moving=0.282, seasonal moving=0.530, Akaike information criterion(AIC)=815.710, and Schwarz Bayesian criterion(SBC)=819.865. The significance test of the optimal model showed that P<0.05, the model residual white noise test showed that χ2=6.83, 12.38, 15.12 and 18.28 for 6, 12, 18 and 24 orders(P>0.05), and there were no significant differences. Based on the above parameters, ARIMA(0, 1, 1)(0, 1, 1)12 model was established, and the formula of the model was ∇∇12xt=(1-0.282B)(1-0.530B12)εt. Conclusion The ARIMA(0, 1, 1)(0, 1, 1)12 model can fit the incidence trend of hepatitis A. It can be used to predict the incidence and to provide a scientific basis for the prevention and control of hepatitis A in China.

Key words: Hepatitis A, Multiple season model of autoregressive integrated moving average model, Time series, Prediction

CLC Number: 

  • R512.6
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