| 沈忠周,马帅,曲翌敏,江宇.ARIMA模型在我国法定传染病报告数中的应用[J].Chinese journal of Epidemiology,2017,38(12):1708-1712 |
| ARIMA模型在我国法定传染病报告数中的应用 |
| Application of autoregressive integrated moving average model in predicting the reported notifiable communicable diseases in China |
| Received:May 02, 2017 |
| DOI：10.3760/cma.j.issn.0254-6450.2017.12.025 |
| KeyWord: 法定传染病 自回归求和移动平均乘积季节模型 预测 |
| English Key Word: Notifiable disease Autoregressive integrated moving average Prediction |
| FundProject: |
| Author Name | Affiliation | E-mail | | Shen Zhongzhou | School of Public Health, Peking Union Medical College, Beijing 100730, China | | | Ma Shuai | School of Public Health, Peking Union Medical College, Beijing 100730, China | | | Qu Yimin | School of Public Health, Peking Union Medical College, Beijing 100730, China | | | Jiang Yu | School of Public Health, Peking Union Medical College, Beijing 100730, China | wingedsky@gmail.com |
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| Abstract: |
| 目的 利用自回归移动平均乘积季节（ARIMA）模型建立适合我国法定传染病月报告发病数的预测模型，借此预测我国法定传染病的变化趋势。方法 利用R软件对2009年5月至2016年7月我国法定传染病月报告发病数据建立ARIMA模型，用2016年8月至2017年1月实际值与预测值进行比较，从而评价模型的预测性能。结果 我国法定传染病月报告发病数具有明显的季节性，且报告在每年2月出现最低峰，6月呈现最高峰；建立ARIMA （4，1，0）（1，1，1）12模型对我国法定传染病发病数进行预测，模型预测的最大相对误差为9.78%，最小为2.21%，平均值为5.39%。结论 ARIMA （4，1，0）（1，1，1）12乘积季节模型较好的拟合了我国法定传染病月报告发病数，可用于预测。 |
| English Abstract: |
| Objective 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.Methods 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.Results 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%.Conclusion 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. |
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