自回归滑动平均混合模型在辽宁省其他感染性腹泻发病预测中的应用

  目的  通过对2007 — 2017年辽宁省各月其他感染性腹泻发病情况分析，建立自回归滑动平均混合模型（ARIMA），为辽宁省其他感染性腹泻的预防控制提供参考依据。  方法  收集国家人口与健康科学数据中心公共卫生科学数据中心提供的《辽宁省2007 — 2017年各月其他感染性腹泻数据》中辽宁省2007 — 2016年各月其他感染性腹泻发病率，运用SPSS 25.0软件对数据进行分析，以发病率建立的时间序列构建ARIMA模型，对2017年各月发病率进行预测，根据实际值评价模型预测的准确性。  结果  2007 — 2016年辽宁省其他感染性腹泻发病率时间序列为非平稳性时间序列，由图形观察及多次验证，确定以下4种备选模型:ARIMA （1，1，1）（0，1，0）₁₂，ARIMA （1，1，1） （1，1，0）₁₂，ARIMA （1，1，1） （0，1，1）₁₂，ARIMA （1，1，1） （1，1，1）₁₂，通过Ljung-Box检验和比较贝叶斯信息准则（BIC）值，最终确定ARIMA（1，1，1）（0，1，1）₁₂为最优模型，经2017年各月实际值验证，模型预测准确性较高。  结论  ARIMA（1，1，1）（0，1，1）₁₂乘积季节模型能较好的预测辽宁省其他感染性腹泻月发病率，具有一定的推广及应用价值。

Application of autoregressive integrated moving average model in prediction of other infectious diarrhea in Liaoning province

Objective Through the analysis on the monthly incidence of other infectious diarrhea in Liaoning province from2007 to 2017,an autoregressive integrated moving average(ARIMA)model was established to provide reference for the prevention and control of other infectious diarrhea in Liaoning.Methods The monthly incidence data of other infectious diarrhea in Liaoning from 2007 to 2016 were collected from the Public Health Science Data Center of the National Population and Health Science Data Center for an analysis with software.SPSS 25.0,an ARIMA model based on the time series was established by using the incidence rate to predict the monthly incidence rate of other infectious diarrhea in Liaoning in 2017,and the accuracy of the model prediction was evaluated based on the actual value.Results The time series of the incidence of other infectious diarrhea in Liaoning from 2007 to 2016 was a non-stationary one.Based on graphical observation and multiple verifications,the following four alternative models were determined:ARIMA(1,1,1)(0,1,0)12,ARIMA(1,1,1)(1,1,0)12,ARIMA(1,1,1)(0,1,1)12 and ARIMA(1,1,1)(1,1,1)12.Through the Ljung-Box test and comparing Bayesian Information Criterion(BIC)value,ARIMA(1,1,1)(0,1,1)12 was finally determined to be the optimal model.Compared with the actual value of each month in 2017,the model prediction was highly accurate.Conclusion The ARIMA(1,1,1)(0,1,1)12 model can better predict the monthly incidence of other infectious diarrhea in Liaoning.It is necessary to promote the application of this model.