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[中国传染病自动预警与响应系统(CIDARS)在浙江省的性能评估]

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

作者信息

Xu Xu-Qing, Lu Qin-Bao, Wang Zhen, Lai Sheng-Jie, Li Zhong-Jie

机构信息

Zhejiang Center for Disease Control and Prevention, Hangzhou 310051, China.

出版信息

Zhonghua Liu Xing Bing Xue Za Zhi. 2011 May;32(5):442-5.

PMID:21569722
Abstract

OBJECTIVE

To evaluate the performance of China Infectious Disease Automated-alert 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 rate 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.

摘要

目的

评估中国传染病自动预警与响应系统(CIDARS)的性能。

方法

对2008年7月1日至2010年6月30日浙江省与CIDARS的预警信号、信号核实结果、现场调查以及通过突发公共卫生事件监测系统报告的突发事件相关数据进行回顾性分析。通过敏感性和误报率指标对CIDARS的性能进行定性评估。

结果

该系统共产生26446条信号,涉及17种疾病,平均每个县每周2.83条信号。所有信号中,99.95%得到了响应。经初步核实,0.90%的信号被判定为疑似事件,最终通过现场调查确认了30起疫情。该系统的敏感性为69.77%,误报率为1.39%。

结论

该系统似乎在传染病疫情预警方面发挥了作用,能够直接反映传染病报告系统中出现的异常事件。然而,在如何减少假阳性信号、选择合适的阈值以及提高数据质量等方面仍需进一步努力,以提高系统的准确性。

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引用本文的文献

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J Huazhong Univ Sci Technolog Med Sci. 2017 Dec;37(6):833-841. doi: 10.1007/s11596-017-1814-9. Epub 2017 Dec 21.