基于R语言的ARIMA模型对流感样病例发病趋势的预测

王晨; 郭倩; 周罗晶

doi:10.16462/j.cnki.zhjbkz.2018.09.020

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基于R语言的ARIMA模型对流感样病例发病趋势的预测

doi: 10.16462/j.cnki.zhjbkz.2018.09.020

王晨¹,
郭倩¹,
周罗晶^2, ,

1. 扬州大学医学院预防医学教研室, 江苏扬州 225001;
2. 扬州大学临床医学院(江苏省苏北人民医院)科技处, 江苏扬州 225001

基金项目:

江苏省预防医学科研课题（Y2015041）

详细信息

作者简介:
王晨(1993-),男,安徽安庆人,在读硕士研究生。主要研究方向:流行病与卫生统计学。

中图分类号: R511.7
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- 文章访问数: 713
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- 被引次数: 0
出版历程
- 收稿日期: 2018-03-03
- 修回日期: 2018-07-12

Forecast of incidence trend of influenza-like illness by the ARIMA model based on R

1. Preventive Medicine Department, Yangzhou Medical College, Yangzhou University, Yangzhou 225001, China;
2. Department of Science and Technology, Clinical Medical School of Yangzhou University(Subei People's Hospital of Jiangsu Province), Yangzhou 225001, China

摘要

摘要: 目的分析自回归滑动平均混合模型（the autoregressive integrated moving average，ARIMA）在流感样病例（influenza like illness，ILI）发病趋势预测的可行性，为流感防控提供技术支持。方法收集本院2013年第1周~2017年第26周由该院每日报告的ILI监测资料，运用R语言进行时间序列分析并建立预测模型。结果流感样病例就诊百分比（consultation rate of influenza like illness，ILI%）监测数据总体上呈现下降趋势，并且具有明显的季节性。最佳预测模型为ARIMA（0，1，1）（0，1，1）₅₂，该模型残差Box-Pierce检验结果为χ²=7.07（P=0.315）、χ²=17.22（P=0.142），提示残差为白噪声序列，预测结果实际值均在预测值的95%的置信区间（95% confidence interval，95%CI）内。结论 ARIMA模型可用于该院ILI短期发病趋势的预测。
- 流感样病例 /
- ARIMA模型 /
- 预测
Abstract: Objective To analyze the feasibility of predicting the incidence trend of influenza-like illness (ILI) by using the autoregressive integrated moving average (ARIMA) model, which provided technical support for influenza prevention and control. Methods The daily ILI monitoring data reported by the hospital from the 1st week of 2013 to the 26th week of 2017 were collected, a time series analysis was conducted and a prediction model was established with R. Results The overall consultation rate of influenza like illness (ILI%) monitoring data showed a downward trend and had obvious seasonal character. The best predictive model was ARIMA (0,1,1)(0,1,1)₅₂, of which the residual error Box-Pierce test result was χ²=7.07 (P=0.315), χ²=17.22 (P=0.142). The residual error was a white noise sequence, and the actual values of the prediction results were within the 95% confidence interval (95%CI) of the predicted value. Conclusion The ARIMA model can be available for the prediction of short-term incidence trend of ILI in this hospital.
- Influenza-like illness /
- ARIMA model /
- Prediction