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Updated: Jun 2, 2026
10:05
High-throughput Detection Method for Influenza Virus
Published on: February 4, 2012
[Comparison on the different thresholds on the 'moving percentile method' for outbreak detection].
Qiao Sun1, Sheng-Jie Lai, Zhong-Jie Li
1Shanghai Pudong New Area Center for Disease Control and Prevention, Shanghai 200136, China.
Summary
Optimizing infectious disease surveillance in China requires disease-specific thresholds for the moving percentile method. Tailoring these thresholds enhances outbreak detection performance within the China Infectious Diseases Automated-alert and Response System (CIDARS).
Area of Science:
- Public Health
- Epidemiology
- Infectious Disease Surveillance
Background:
- The China Infectious Diseases Automated-alert and Response System (CIDARS) utilizes a moving percentile method for outbreak detection.
- Determining optimal thresholds is crucial for effective early warning systems.
Purpose of the Study:
- To compare various thresholds of the moving percentile method for infectious disease outbreak detection in CIDARS.
- To identify optimal thresholds for different infectious diseases to enhance surveillance performance.
Main Methods:
- Evaluated thresholds P(50) through P(90) of the moving percentile method.
- Analyzed reported cases of 19 notifiable infectious diseases nationwide from July 2008 to June 2010.
- Assessed outbreak detection numbers and time to detection to determine optimal thresholds.
Main Results:
- Optimal thresholds varied by disease, ranging from P(50) for dysentery to P(90) for diseases like measles and dengue fever.
- Using adjusted optimal thresholds reduced signals by 12.20% compared to the default P(50) threshold over two years.
- The adjusted thresholds did not negatively impact the number of detected outbreaks or the time to detection.
Conclusions:
- The optimal moving percentile threshold is disease-specific.
- Implementing tailored thresholds can significantly optimize the performance of infectious disease outbreak detection systems like CIDARS.
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