首页 | 本学科首页   官方微博 | 高级检索  
检索        

中国内地法定报告传染病预测和监测的ARIMA模型
引用本文:冯丹,韩晓娜,赵文娟,生甡,YANG Hong,杨红,方立群,曹务春.中国内地法定报告传染病预测和监测的ARIMA模型[J].疾病控制杂志,2007,11(2):140-143.
作者姓名:冯丹  韩晓娜  赵文娟  生甡  YANG Hong  杨红  方立群  曹务春
作者单位:军事医学科学院,微生物与流行病研究所流行病学研究室,北京,100071
基金项目:欧盟第六框架项目 , 国家自然科学基金 , 北京市自然科学基金
摘    要:目的通过对1995年1月~2004年4月中国大陆法定报告传染病逐月发病率数据的分析,研究其变化规律,建立预测与监测的ARIMA时间序列模型。方法利用时间序列模型中的自回归滑动平均混合模型ARIMA,考虑非季节效应和季节效应,分析中国法定报告传染病发病率的变化趋势和周期性,模型参数估计采用非线性最小二乘法,应用残差和赤池信息量准则(AIC)评价模型的优劣。1995~2004年我国内地法定报告传染病逐月发病率的数据用于建立模型,2005年1月~2006年4相应数据用于模型检验。结果分析结果显示,法定报告传染病发病以年为周期,一年中6~9月为高发月,尤其是8月和7月最为严重。ARIMA(0,1,0)(0,1,0)12模型是法定报告传染病拟合的最佳模型,其拟合残差的方差为2.28,外推预测的平均绝对误差为0.34。利用预测值的95%置信区间建立了我国内地法定报告传染病发病率变化的监测控制线,用于其发病情况的预测与预报。结论对传染病发病率历史数据进行时间序列分析是用于传染病监测的一个重要的工具。所建立的ARIMA模型适用于对中国大陆法定报告传染病发病率预测与监测。该模型具有一定的实用价值,并可以应用于其他传染病的监测和异常变化的检测。

关 键 词:疾病报告  模型  统计学  人群监测
文章编号:1008-6013(2007)02-0140-04
收稿时间:2006-08-01
修稿时间:2006-11-15

Using ARIMA model to surveillance and forecast the incidence rate of notifiable infectious diseases in Mainland China
YANG Hong.Using ARIMA model to surveillance and forecast the incidence rate of notifiable infectious diseases in Mainland China[J].Chinese Journal of Disease Control and Prevention,2007,11(2):140-143.
Authors:YANG Hong
Institution:FENG Dan, HAN Xiao-na, ZHAO Wen-juan, SHENG Shen, YANG Hong, FANG Li-qun, CAO Wu-chun. (Department of Epidemiology, Academy of Military Medical Sciences, Beijing 100071, China)
Abstract:Objective To develop the model for forecasting and surveilling the spreading of notifiable infectious diseases in Mainland China.Methods Using time-series methods,ARIMA(0,1,0)(0,1,0)12 model was developed for purpose of forecasting and surveillance of notifiable infectious disease in Mainland China.The model was based on the reporting data of these diseases in Mainland China from 1995 to 2004,and it was tested by the data from January,2005 to April,2006.Results The changes of incidence rate of the notifiable infectious diseases in Mainland China presented a yearly periodicity,and showed that the incidence rate from April to September exceeded the monthly average of it.The residuals sum of square of the ARIMA model was 2.28 for incidence rate of the notifiable infectious disease from 1995 to 2004,and the mean error of the model was 0.34.The model could further be used to draw a control chart for surveillance of the disease.Conclusions Time series methods applied to historical reporting data of infectious disease are an important tool for infectious disease surveillance.The ARIMA model is suitable to forecast report incidence rate of notifiable infectious diseases in Mainland China.Our approach potentially has a high practical value for the notifiable infectious diseases in forecasting and surveillance,and it can be generalized to other diseases to develop automated surveillance system and capable of detecting anomalies in disease pattern.
Keywords:Notifiable infectious diseases  Model  Statistical  Population surveillance
本文献已被 CNKI 维普 万方数据 等数据库收录!
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号