浙江省传染病自动预警系统暴发预警效果评价

徐旭卿; 鲁琴宝; 王臻; 赖圣杰; 李中杰

Abstract

徐旭卿,鲁琴宝,王臻,赖圣杰,李中杰.浙江省传染病自动预警系统暴发预警效果评价[J].Chinese journal of Epidemiology,2011,32(5):442-445

浙江省传染病自动预警系统暴发预警效果评价

Evaluation on the performance of China Infectious Disease Automated-alert and Response System(CIDARS)in Zhejiang province

Received:January 29, 2011

DOI：

KeyWord: 传染病预警系统

English Key Word: Infectious disease Early-warning system

FundProject:"十一五"国家科技支撑计划(2006BAK01A13,2008BAI56B02);中国-世界卫生组织合作项目(WPCHN0801617,WPCHN1002405);国家科技重大专项(2009ZX10004-201)

Author Name	Affiliation	E-mail
XU Xu-qing	Zhejiang Center for Disease Control and Prevention, Hangzhou 310051, China	xqxu@cdc.zj.cn
LU Qin-bao	Zhejiang Center for Disease Control and Prevention, Hangzhou 310051, China
WANG Zhen	Zhejiang Center for Disease Control and Prevention, Hangzhou 310051, China
LAI Sheng-jie	Chinese Center for Disease Control and Prevention
LI Zhong-jie	Chinese Center for Disease Control and Prevention

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Abstract:

目的分析国家传染病自动预警系统(预警系统)在浙江省传染病暴发早期探测中的效果.方法以浙江省2008年7月1日至2010年6月30日移动百分位数法预警相关病种的预警信号数及初步核实、现场调查结果和突发公共卫生事件报告系统报告结果作为研究对象,以灵敏度和错误预警率评价预警系统的有效性.结果预警系统共发出预警信号26 446条,涉及17种传染病,平均每县每周预警信号2.83条.99.95%的预警信号得到响应,预警信号经过初步核实后,254条(0.90%)预警信号被判断为疑似事件,经过现场调查共确认暴发30起,预警系统的灵敏度为69.77%,预警错误率为1.39%.结论预警系统可初步实现传染病暴发的早期自动预警,但仍需进一步研究以减少假阳性预警信号,合理设置阈值,提高暴发探测来源数据的质量.

English Abstract:

Objective To evaluate the performance of China Infectious Disease Automatedalert and Response System(CIDARS). Methods A retrospective analysis was conducted on data related to the warning signals, the outcome of signal verification, the field investigation of CIDARS,and the emergent events reported through Public Health Emergency Events Surveillance System from July 1,2008 to June 30, 2010 in Zhejiang province. The performance of CIDARS was qualitatively evaluated by indicators on its sensitivity and rote of false alarm. Results In total, 26 446 signals were generated by the system which involving 17 diseases, with an average of 2.83 signals per country per week. Among all the signals, 99.95% of them were responded. 0.90% of the signals were judged as suspected events via the preliminary verification, and 30 outbreaks were finally confirmed by field investigation. The sensitivity of the system was 69.77% with the false alarm rate as 1.39%. Conclusion The system seemed to have worked on the outbreak early warning of infectious diseases and could directly reflect the anomaly event emerged from the infectious disease reporting system.However, more efforts should be paid to the following areas as how to decrease the false positive signals, select suitable thresholds and increase the quality of data in order to enhance the accuracy of the system.

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