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Related Experiment Video

Updated: May 12, 2026

High-throughput Detection of Respiratory Pathogens in Animal Specimens by Nanoscale PCR
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Published on: November 28, 2016

Syndromic surveillance using veterinary laboratory data: data pre-processing and algorithm performance evaluation.

Fernanda C Dórea1, Beverly J McEwen, W Bruce McNab

  • 1Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Prince Edward Island, Canada. fdorea@upei.ca

Journal of the Royal Society, Interface
|April 12, 2013
PubMed
Summary
This summary is machine-generated.

This study evaluated methods for detecting cattle disease outbreaks using laboratory data. Combining multiple detection algorithms offers the most effective approach for early identification of temporal aberrations in animal health surveillance.

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Last Updated: May 12, 2026

High-throughput Detection of Respiratory Pathogens in Animal Specimens by Nanoscale PCR
11:00

High-throughput Detection of Respiratory Pathogens in Animal Specimens by Nanoscale PCR

Published on: November 28, 2016

Area of Science:

  • Veterinary epidemiology
  • Animal health surveillance
  • Data science in animal health

Background:

  • Diagnostic laboratory test orders can serve as a valuable data source for monitoring disease trends in cattle.
  • Effective early detection of clinical syndromes in cattle requires robust methods to identify temporal aberrations in surveillance data.

Purpose of the Study:

  • To compare pre-processing methods for removing temporal effects from diagnostic test order data.
  • To evaluate the sensitivity and specificity of various temporal aberration detection algorithms for simulated cattle disease outbreaks.

Main Methods:

  • Utilized four years of real diagnostic test order data and over 200 simulated outbreak signals.
  • Assessed pre-processing techniques like weekly differencing to mitigate day-of-week effects.
  • Compared performance of Exponentially Weighted Moving Average (EWMA) charts, Holt-Winters exponential smoothing, Shewhart charts, and Cumulative Sum (CUSUM) charts.

Main Results:

  • Weekly differencing effectively removed day-of-week effects, even with low daily counts.
  • No single aberration detection algorithm outperformed all others across diverse outbreak scenarios.
  • EWMA charts and Holt-Winters smoothing showed complementary strengths; Holt-Winters provided automated adjustments.
  • Shewhart charts offered earlier detection in some cases but lower sensitivity; CUSUM charts provided limited value.

Conclusions:

  • Automated monitoring for early detection of temporal aberrations in cattle health is most effective when multiple algorithms are used concurrently.
  • The choice of algorithms should consider their complementary performance characteristics for different outbreak scenarios.
  • Further research may refine the application of specific algorithms based on data characteristics.