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Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018

Author

Listed:
  • Jing Cong

    (Department of Epidemiology, Binzhou Medical University, YanTai 264003, China)

  • Mengmeng Ren

    (Department of Epidemiology, Binzhou Medical University, YanTai 264003, China)

  • Shuyang Xie

    (Department of Biochemistry and Molecular Biology, Binzhou Medical University, YanTai 264003, China)

  • Pingyu Wang

    (Department of Epidemiology, Binzhou Medical University, YanTai 264003, China
    Department of Biochemistry and Molecular Biology, Binzhou Medical University, YanTai 264003, China)

Abstract

Seasonal influenza is one of the mandatorily monitored infectious diseases, in China. Making full use of the influenza surveillance data helps to predict seasonal influenza. In this study, a seasonal autoregressive integrated moving average (SARIMA) model was used to predict the influenza changes by analyzing monthly data of influenza incidence from January 2005 to December 2018, in China. The inter-annual incidence rate fluctuated from 2.76 to 55.07 per 100,000 individuals. The SARIMA (1, 0, 0) × (0, 1, 1) 12 model predicted that the influenza incidence in 2018 was similar to that of previous years, and it fitted the seasonal fluctuation. The relative errors between actual values and predicted values fluctuated from 0.0010 to 0.0137, which indicated that the predicted values matched the actual values well. This study demonstrated that the SARIMA model could effectively make short-term predictions of seasonal influenza.

Suggested Citation

  • Jing Cong & Mengmeng Ren & Shuyang Xie & Pingyu Wang, 2019. "Predicting Seasonal Influenza Based on SARIMA Model, in Mainland China from 2005 to 2018," IJERPH, MDPI, vol. 16(23), pages 1-8, November.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:23:p:4760-:d:291646
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    References listed on IDEAS

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