Early Outbreak Detection Using an Automated Data Feed of Test Orders from a Veterinary Diagnostic Laboratory

  • Loren Shaffer
  • Julie Funk
  • Päivi Rajala-Schultz
  • Garrick Wallstrom
  • Thomas Wittum
  • Michael Wagner
  • William Saville
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4506)


Disease surveillance in animals remains inadequate to detect outbreaks resulting from novel pathogens and potential bioweapons. Mostly relying on confirmed diagnoses, another shortcoming of these systems is their ability to detect outbreaks in a timely manner. We investigated the feasibility of using veterinary laboratory test orders in a prospective system to detect outbreaks of disease earlier compared to traditional reporting methods. IDEXX Laboratories, Inc. automatically transferred daily records of laboratory test orders submitted from veterinary providers in Ohio via a secure file transfer protocol. Test products were classified to appropriate syndromic category using their unique identifying number. Counts of each category by county were analyzed to identify unexpected increases using a cumulative sums method. The results indicated that disease events can be detected through the prospective analysis of laboratory test orders and may provide indications of similar disease events in humans before traditional disease reporting.


Disease Surveillance Test Order Daily Record Syndromic Surveillance Emerg Infect 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Loren Shaffer
    • 1
  • Julie Funk
    • 2
  • Päivi Rajala-Schultz
    • 1
  • Garrick Wallstrom
    • 3
  • Thomas Wittum
    • 1
  • Michael Wagner
    • 3
  • William Saville
    • 1
  1. 1.Department of Veterinary Preventive Medicine, The Ohio State University, Columbus, Ohio 43210 
  2. 2.National Food Safety and Toxicology Center, Michigan State University, East Lansing, Michigan 44824 
  3. 3.Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania 15219 

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