SARIMA模型在长治市肺结核预测中的应用  

Application of the SARIMA Model in the Prediction of Pulmonary Tuberculosis in Changzhi City

作　　者：张喜红[1] 李慧[2] 曹文君 崔永梅 ZHANG Xihong1,LI Hui2,CAO Wenjun3,CUI Yongmei4(1. Teaching and Research Section of Mathematics, Changzhi Medical College, Changzhi 046000, China; 2. Department of Mathematical Statistics, School of Statistics, Beijing Normal University, Beijing 100875, China; 3. Teaching and Research Section of Epidemiology and Health Statistics, Department of Public Health and Preventive Medicine, Changzhi Medical College, Changzhi 046000, China; 4. Department of Infectious Diseases Prevention and Con-trol, Changzhi Center for Disease Control and Prevention, Changzhi 046011, Chin)

机构地区：[1]长治医学院数学教研室,山西长治046000 [2]北京师范大学统计学院数理统计系,北京100875 [3]长治医学院公共卫生与预防医学系流行病与卫生统计学教研室,山西长治046000 [4]长治市疾病预防控制中心传染病防控科,山西长治046011

出　　处：《中国医科大学学报》2018年第7期585-588,共4页Journal of China Medical University

基　　金：国家自然科学基金(81302518)

摘　　要：目的应用时间序列季节自回归求和滑动平均(SARIMA)模型探讨长治市肺结核的发病规律,为防控肺结核发生提供依据。方法收集长治市2010年1月至2017年12月肺结核逐月发病数,应用Eviews3.1对2010年1月至2017年6月肺结核发病数建立SARIMA模型;利用所建SARIMA模型对2017年7月至12月肺结核发病数进行预测,并与实际值对比来评价模型预测效果。利用模型预测长治市2018年1到12月肺结核发病数。结果建立模型SARIMA(2,1,0)×(1,0,1)12,表达式为(1-B)(1+0.657B+0.279B^2)(1-0.906B^12)y_1=(1-0.885B^12)ε_1,y_1=ln(x_1),其中ε_1~WN(0,0,1272),该模型是预测长治市肺结核发病人数的合适模型,2017年7月至12月预测值平均相对误差为5.96%。结论建立了时间序列模型SARIMA(2,1,0)×(1,0,1)12来总结长治市肺结核的发病规律,并有效预测肺结核发病人数。Objective To investigate the pattern of pulmonary tuberculosis in Changzhi city by using the time-series seasonal autoregressive integrated moving average（SARIMA） model to provide a reliable basis for the prevention and control of tuberculosis. Methods The monthly incidence of pulmonary tuberculosis in Changzhi city from January 2010 to December 2017 was collected. On the basis of this number,the SARIMA model was established using Eviews3.1. The established SARIMA model was used to predict the number of patients with pulmonary tuberculosis from July to December 2017 and compared with the actual numbers to evaluate the prediction effect of the model. In addition,the model was used to predict the incidence of tuberculosis in Changzhi city from January to December in 2018. Results The SARIMA model was finally established as SARIMA（2,1,0）×（1,0,1）_12,with the expression of（1-B）（1＋0.657 B＋0.279 B^2）（1-0.906 B^12） y_1=（1-0.885 B^12） ε_1,y_1=ln（xt）,of which ε_1-WN（0,0,127~2）. The model was suitable for predicting the incidence of pulmonary tuberculosis in Changzhi city,with an average relative error of 5.96% for the predicted values from July to December 2017. Conclusion The time series model SARIMA（2,1,0）×（1,0,1）_12 can better investigate the incidence of pulmonary tuberculosis in Changzhi and effectively predict the incidence of tuberculosis.

关 键 词：季节自回归求和滑动平均模型 时间序列 肺结核 预测 

分 类 号：R183.3[医药卫生—流行病学]