移动百分位数法不同阈值在传染病暴发预警中的效果比较

Sun, Qiao, Lai, Sheng Jie, Li, Zhong Jie, Lan, Ya Jia, Zhang, Hong Long, Zhao, Dan, Jin, Lian Mei and Yang, Wei Zhong (2011) 移动百分位数法不同阈值在传染病暴发预警中的效果比较. Zhonghua liu xing bing xue za zhi = Zhonghua liuxingbingxue zazhi, 32 (5), 450-453. (doi:10.3760/cma.j.issn.0254-6450.2011.05.006).

Abstract

To compare the different thresholds of 'moving percentile method' for outbreak detection in the China Infectious Diseases Automated-alert and Response System (CIDARS). The thresholds of P(50), P(60), P(70), P(80) and P(90) were respectively adopted as the candidates of early warning thresholds on the moving percentile method. Aberration was detected through the reported cases of 19 notifiable infectious diseases nationwide from July 1, 2008 to June 30, 2010. Number of outbreaks and time to detection were recorded and the amount of signals acted as the indicators for determining the optimal threshold of moving percentile method in CIDARS. The optimal threshold for bacillary and amebic dysentery was P(50). For non-cholera infectious diarrhea, dysentery, typhoid and paratyphoid, and epidemic mumps, it was P(60). As for hepatitis A, influenza and rubella, the threshold was P(70), but for epidemic encephalitis B it was P(80). For the following diseases as scarlet fever, typhoid and paratyphoid, hepatitis E, acute hemorrhagic conjunctivitis, malaria, epidemic hemorrhagic fever, meningococcal meningitis, leptospirosis, dengue fever, epidemic endemic typhus, hepatitis C and measles, it was P(90). When adopting the adjusted optimal threshold for 19 infectious diseases respectively, 64 840 (12.20%) signals had a decrease, comparing to the adoption of the former defaulted threshold (P(50)) during the 2 years. However, it did not reduce the number of outbreaks being detected as well as the time to detection, in the two year period. The optimal thresholds of moving percentile method for different kinds of diseases were different. Adoption of the right optimal threshold for a specific disease could further optimize the performance of outbreak detection for CIDARS.

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Contributors

Author: Sheng Jie Lai

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