自回归积分滑动平均模型在中国法定报告传染病预测中的应用
[Application of autoregressive integrated moving average model in predicting the reported notifiable communicable diseases in China].
作者信息
Shen Z Z, Ma S, Qu Y M, Jiang Y
机构信息
School of Public Health, Peking Union Medical College, Beijing 100730, China.
出版信息
Zhonghua Liu Xing Bing Xue Za Zhi. 2017 Dec 10;38(12):1708-1712. doi: 10.3760/cma.j.issn.0254-6450.2017.12.025.
To develop the models for predicting the reported legally notifiable diseases in China. Autoregressive integrated moving average (ARIMA) model was applied to forecast the trend of diseases. Cases used for building the model were from of the records of Notifiable Infectious Diseases in China from May 2009 to July 2016 with R software and the model's predictive ability was tested by the data from August 2016 to January 2017. A strong seasonal nature was seen in the reported cases of notifiable communicable diseases, with the lowest point in February and highest peak in June. ARIMA (4, 1, 0) (1, 1, 1)(12) model was established by the team to forecast the notifiable communicable diseases. Data showed that the biggest and lowest relative errors appeared as 9.78% and 2.21%, respectively, with the mean of the relative error as 5.39%. Based on the results of this study, the ARIMA (4, 1, 0) (1, 1, 1)(12) model seemed to have had the sound prediction of notifiable communicable diseases in China.
为建立中国法定报告传染病的预测模型。应用自回归积分滑动平均（ARIMA）模型预测疾病趋势。用于构建模型的病例来自2009年5月至2016年7月中国法定传染病记录，使用R软件，并通过2016年8月至2017年1月的数据测试模型的预测能力。法定传染病报告病例呈现出强烈的季节性，2月最低，6月最高。该团队建立了ARIMA（4,1,0）（1,1,1）（12）模型来预测法定传染病。数据显示，最大相对误差和最小相对误差分别为9.78%和2.21%，相对误差平均值为5.39%。基于本研究结果，ARIMA（4,1,0）（1,1,1）（12）模型似乎对中国法定传染病有良好的预测效果。
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