中国全科医学2017，Vol.20Issue(2)：182-186,5.DOI:10.3969/j.issn.1007-9572.2017.02.012
差分自回归移动平均与广义回归神经网络组合模型在丙型肝炎月发病率中的预测应用
Application of ARIMA-GRNN Combination Model in Predicting Monthly Incidence of Hepatitis C
摘要
Abstract
Objective To explore the predictive modeling effects and application prospects of ARIMA-GRNN combination model in the monthly incidence of hepatitis C,and to provide basis for the epidemic prediction. Methods From May 2015 to May 2016,the 2004—2014 monthly data on the incidence of hepatitis C were selected from direct reporting system of legal infectious diseases in Shandong Provincial Center for Disease Control and Prevention,and the population at the same period released by Shandong provincial Bureau of Statistics were also chosen in the study. ARIMA fitted model of the monthly incidence data of hepatitis C in Shandong province from 2004 to 2014 was constructed,and the fitting precision was verified and extrapolated;the fitted value of ARIMA model was taken as the input of GRNN model,and the actual value of monthly incidence of hepatitis C as the output,and the samples were trained and predicted. The effects of ARIMA model and ARIMA-GRNN combination model on predicting the monthly incidence of hepatitis C were compared. Results The annual average incidence of hepatitis C in Shandong province from 2004 to 2014 was 17. 28 / 100 000,and showed an increasing trend as time went on(Z= 29. 05,P < 0. 01). By the use of ARIMA(1,2,1)model,the predictive incidence of hepatitis C in Shandong province in 2014 was basically the same as the actual incidence,which falls within the 95% confidence interval with good fitting effects. The fitted value of ARIMA(1,2,1)model was taken as the input of GRNN model,and the actual value of monthly incidence of hepatitis C as the output,the training model with an optimal smoothing factor of 0. 12 was selected,and the fitted value of ARIMA-GRNN combination model basically agreed with the actual value. The mean error rate( MER)of ARIMA model and ARIMA-GRNN combination model were 16. 87% and 15. 30% respectively;their determination coefficients(R2 )were 0. 53 and 0. 60 respectively;their mean absolute errors(MAE)were 0. 17 and 0. 09 respectively;and the mean absolute percent errors (MAPE)were 1. 18 and 0. 35 respectively. Conclusion The fitting and predictive effects of ARIMA-GRNN combination model on the monthly incidence of hepatitis C in Shandong province is better than those of simple ARIMA model,and has a high fitting precision and a promising application prospects. It is of certain practical significance in the epidemic prediction.关键词
丙型肝炎/发病率/预测/差分自回归移动平均模型/广义回归神经网络Key words
Hepatitis C/Incidence/Forecasting/ARIMA model/GRNN分类
医药卫生引用本文复制引用
刘红杨,刘洪庆,李望晨,赵晶..差分自回归移动平均与广义回归神经网络组合模型在丙型肝炎月发病率中的预测应用[J].中国全科医学,2017,20(2):182-186,5.基金项目
“健康山东”重大社会风险预测与治理协同创新中心资助课题 （）
