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Related Concept Videos

Principles of Disease Surveillance01:26

Principles of Disease Surveillance

Disease surveillance is the systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice. This process integrates data dissemination to entities responsible for preventing and controlling disease, injury, and disability. Surveillance systems provide crucial information for action, helping public health authorities make informed decisions to manage and prevent outbreaks, ensure public safety, optimize...
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Related Experiment Video

Updated: Jun 3, 2026

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1
06:18

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

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
PubMed
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.

Related Experiment Videos

Last Updated: Jun 3, 2026

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1
06:18

Multiplexed Isothermal Amplification Based Diagnostic Platform to Detect Zika, Chikungunya, and Dengue 1

Published on: March 13, 2018

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.