Assessing the Effectiveness of the Early Aberration Reporting System (EARS) for Early Event Detection of the H1N1 ("Swine Flu") Virus

Abstract

The Monterey County Health Department (MCHD) in California uses the Early Aberration Reporting System (EARS) to monitor emergency room and clinic data for biosurveillance, particularly as an alert system for various types of disease outbreaks. The flexibility of the system has proven to be a very useful feature of EARS; however, little research has been conducted to assess its performance. In this thesis, a quantitative analysis based on modifications to EARS' internal logic and algorithms is assessed. Logic is used as a counting tool for potential cases of outbreak, and the Early Event Detection (EED) algorithms are used to determine whether or not an outbreak is about to occur. The EED methods are compared by assessing their ability to detect the presence of a known H1N1 outbreak in Monterey County. This research found the cumulative sum (CUSUM) detection method to be the most reliable in signaling the H1N1 outbreak, across all combinations of logic explored.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2010
Accession Number
ADA531613

Entities

People

  • Katie S. Hagen

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Algorithms
  • California
  • Data Sets
  • Detection
  • Disease Outbreaks
  • Diseases And Disorders
  • Event Detection
  • Health Services
  • Homeland Security
  • Medical Personnel
  • Operations Research
  • Patient Care
  • Public Health
  • Situational Awareness
  • Statistical Algorithms
  • United States
  • Warning Systems

Readers

  • Auditory Neuroscience/Auditory Physiology.
  • Computational Modeling and Simulation
  • Infectious Disease/Epidemiology

Technology Areas

  • Biotechnology