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Reducing false alarms in syndromic surveillance
William Peter1, Amir H Najmi, Howard S Burkom
1Applied Physics Laboratory, Johns Hopkins University, 11100 Johns Hopkins Road, Laurel, MD 20723, USA. bill.peter@jhuapl.edu
Statistics in Medicine
|March 25, 2011
Summary
This study introduces a novel method to reduce false alerts in public health surveillance by using reference data to cancel noise. This improves the accuracy of automated disease outbreak detection systems.
Area of Science:
- Public Health Surveillance
- Biostatistics
- Epidemiology
Background:
- Automated public health surveillance algorithms are prone to false alarms.
- Sudden shifts in healthcare utilization or data participation can trigger these false alerts.
Purpose of the Study:
- To describe a method for reducing false alerts in automated public health surveillance algorithms.
- To enhance the reliability of syndromic surveillance systems.
Main Methods:
- Monitoring syndromic counts against a suitable background time series.
- Utilizing mutual information to assess the suitability of background series.
- Applying a noise cancellation filter technique.
Main Results:
- Demonstrated a mathematical relationship between mutual information and reduced false alarm rates.
- The proposed method effectively cancels background noise in monitored data.
Conclusions:
- The described technique offers a robust approach to improving the accuracy of public health surveillance.
- This method has implications for the appropriate use of rates in epidemiology and biostatistics.
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