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Related Experiment Video

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Remote Laboratory Management: Respiratory Virus Diagnostics
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Syndromic surveillance using laboratory test requests: a practical guide informed by experience with two systems.

F C Dórea1, A Lindberg2, B J McEwen3

  • 1Swedish Zoonosis Centre, Department of Disease Control and Epidemiology, National Veterinary Institute (SVA), Uppsala, Sweden; Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Canada.

Preventive Veterinary Medicine
|April 29, 2014
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Summary
This summary is machine-generated.

Two automated systems were developed to extract animal health surveillance data from laboratory databases. These systems enhance early disease detection and epidemiological information management using open-source software.

Keywords:
Animal healthEarly disease detectionLaboratorySyndromic surveillance

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Area of Science:

  • Veterinary epidemiology
  • Public health surveillance
  • Data science

Background:

  • Syndromic surveillance systems are crucial for early disease detection and monitoring.
  • Automated, continuous, and electronic data acquisition is essential for timely feedback.
  • Diagnostic laboratory data often meets these requirements for surveillance.

Purpose of the Study:

  • To develop and describe two automated systems for extracting surveillance information from animal laboratory databases.
  • To enhance early disease detection and epidemiological information management in Canada and Sweden.
  • To demonstrate the adaptability and potential for widespread adoption of these systems.

Main Methods:

  • Development of two automated systems to extract data from animal diagnostic laboratory databases.
  • Classification of incoming requests into syndromes.
  • Retrospective data evaluation for baseline profiles, noise removal, and aberration identification.
  • Identification of temporal effects and prospective evaluation of detection algorithms.
  • Real-time monitoring and implementation using open-source software.

Main Results:

  • Two systems were successfully developed and implemented in Canada and Sweden.
  • Syndrome classification was the most time-consuming and least portable step.
  • Remaining data processing steps were adaptable between systems.
  • The approach utilizes existing data management software and open-source tools.

Conclusions:

  • The developed systems can significantly contribute to early disease detection and epidemiological management.
  • The use of open-source software facilitates easy adoption by other institutions.
  • These systems are expected to become indispensable tools for diagnosticians and epidemiologists.
  • The approach stimulates further technical development in syndromic surveillance systems.