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目的探讨应用时间序列基于季节性差分的自回归移动平均模型(autoregressive integrated moving average,ARIMA)预测全国丙型肝炎的发病情况。方法利用“中国疾病预防控制信息系统”中的“疾病监测信息报告管理系统”(又称“传染病疫情信息网络直报系统”)的资料,应用SPSS 19.0统计软件、采用ARIMA模型,对全国2005年1月~2012年12月丙型肝炎逐月发病数进行建模和拟合,利用所得到的模型对2013年1~6月的发病情况进行预测,并按照预测值与实际观察值之间的差异评价其预测效果。结果分析结果显示,丙型肝炎发病以年为周期,一年中3~5月为高发月。非季节自回归参数为5.84,t=-2.567,P=0.012。非季节移动平均参数为0.481,t=3.392,P=0.001,季节移动平均参数为0.625,t=3.547,P=0.001,差异有统计学意义。BIC=14.162,Ljung-Box统计量检验残差序列为白噪声序列,预测的平均相对误差为3.4%,丙型肝炎拟合的最佳模型为ARIMA(1,1,1),(0,1,1)12。结论ARIMA对全国丙型肝炎拟合的预测效果较为满意,预测结果将为今后丙型肝炎等多种传染病的预防和控制提供理论支持。
OBJECTIVE: To predict the incidence of hepatitis C in China using time-series autoregressive moving average (ARIMA) model. Methods Using the information of “Disease Surveillance Information Reporting Management System” (also known as “Infectious Disease Epworth Information Network Direct Reporting System”) in “China Disease Prevention and Control Information System”, using SPSS 19.0 statistical software, ARIMA model was used to model and fit the monthly incidence of hepatitis C in China from January 2005 to December 2012. The model was used to predict the incidence of hepatitis C from January to June 2013. According to the predictive value And the actual observation of the difference between the evaluation of the predictive effect. Results Analysis showed that the incidence of hepatitis C in the year cycle, 3 to 5 months of the year as the high incidence of months. Non-seasonal autoregressive parameters were 5.84, t = -2.567, P = 0.012. The non-seasonal moving average parameters were 0.481, t = 3.392, P = 0.001, the seasonal moving average parameters were 0.625, t = 3.547, P = 0.001, the difference was statistically significant. BIC = 14.162, the Ljung-Box statistic test residual sequence is white noise sequence, the average relative error of prediction is 3.4%, the best model of Hepatitis C fitting is ARIMA (1,1,1), (0,1 , 1) 12. Conclusions ARIMA is satisfactory for the prediction of hepatitis C in the whole country. The prediction results will provide theoretical support for the prevention and control of many infectious diseases such as hepatitis C in the future.