我国2012-2021年4种肝炎流行趋势的时间序列分析和预测
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南京医科大学康达学院

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南京医科大学康达学院第二期品牌专业建设工程资助项目(JX206000302), 南京医科大学康达学院医学信息模拟及预测科研团队资助项目(KD2022KYCXTD003)


Time series analysis and prediction of four hepatitis epidemic trends in China from 2012 to 2021
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    摘要:

    目的: 分析我国2012-2021年4种病毒性肝炎十年流行特征的季节性规律和长期趋势,探讨适合肝炎发病预测的时间序列模型,为科学防控肝炎提供参考依据和建议。方法: 对我国2012年1月至2021年12月甲型、乙型、丙型和戊型肝炎的月发病例数进行时间序列的季节性分解,建立季节自回归移动平均模型(ARIMA, Autoregressive Integrated Moving Average Model)和季节指数平滑模型,并对2022年1-8月4种肝炎的发病例数进行预测,并比较预测效果。结果: 每年3月份是各类肝炎发病的高发期,十年期间,甲型肝炎总体保持下降趋势,乙型肝炎总体趋势有升有降,近年来有上升趋势;丙型肝炎总体呈上升趋势;戊型肝炎总体保持平稳趋势。乙型、丙型和戊型肝炎月平均发病例数分别为甲型肝炎的57.06倍、11.50倍、1.35倍。季节指数平滑模型的预测效果要优于季节ARIMA模型。结论: 我国乙型和丙型肝炎发病人数众多,要加强重点防控。时间序列的季节性分解可用于分析肝炎流行特征的季节性规律和长期趋势,季节指数平滑模型包含水平、趋势和季节三个参数,能体现肝炎发病的流行规律,在肝炎发病预测中,具有模型简单、计算简便、预测精度高的优点。

    Abstract:

    Objective: To analyze the seasonal patterns and long-term trends of the ten year epidemic characteristics of four types of viral hepatitis in China from 2012 to 2021, explore a time series model suitable for predicting hepatitis incidence, and provide reference and suggestions for scientific prevention and control of hepatitis. Method: Seasonal decomposition of the time series was conducted on the monthly incidence of hepatitis A, B, C, and E in China from January 2012 to December 2021. A seasonal autoregressive integrated moving average model (ARIMA) and a seasonal index smoothing model were established to predict the incidence of four types of hepatitis from January to August 2022, and the predictive effects were compared. Result: March of each year is the peak period for various types of hepatitis, and during the ten-year period, the overall trend of hepatitis A remains decreasing; the overall trend of hepatitis B has been increasing and decreasing, with an upward trend in recent years; the overall trend of hepatitis C remains increasing; the overall trend of hepatitis E remains stable. The average monthly incidence of hepatitis B, C, and E is 57.06 times, 11.5 times, and 1.35 times higher than that of hepatitis A, respectively. The prediction performance of the seasonal index smoothing model is better than that of the seasonal ARIMA model. Conclusion: There are a large number of patients with hepatitis B and C in China, and key prevention and control measures should be strengthened. The seasonal decomposition of time series can be used to analyze the seasonal patterns and long-term trends of hepatitis epidemic characteristics. The seasonal index smoothing model includes three parameters: level, trend, and season, which can reflect the epidemic pattern of hepatitis incidence, in the prediction of hepatitis incidence, it has the advantages of simple model, simple calculation, and high prediction accuracy.

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  • 收稿日期:2023-09-24
  • 最后修改日期:2023-11-24
  • 录用日期:2023-12-11
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