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Related Experiment Video

Updated: Jan 13, 2026

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
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Simulation of pooling optimization methods for differing infection dynamics, sampling practices, and desired outcomes

Catharine Burgess1,2,3, Duan Sriyotee Loy4, John Dustin Loy4

  • 1Department of Biomedical Sciences and Pathobiology, Virginia-Maryland College of Veterinary Medicine, Virginia Tech, Blacksburg, VA, USA.

Journal of Veterinary Diagnostic Investigation : Official Publication of the American Association of Veterinary Laboratory Diagnosticians, Inc
|January 8, 2026
PubMed
Summary

Choosing the right pooling strategy for disease surveillance is crucial for cost-effectiveness. Our study shows that the best method depends on the specific pathogen, like bovine viral diarrhea virus (BVDV), and how samples are collected.

Keywords:
Theileriabovine viral diarrhea virusoptimizationpopulation surveillancesample pooling

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Area of Science:

  • Veterinary epidemiology
  • Diagnostic assay optimization
  • Infectious disease surveillance

Background:

  • Pooling strategies in disease surveillance aim to optimize testing economy and reduce costs.
  • The reliability of prevalence assumptions for pooling optimization varies significantly across pathogens and surveillance designs.
  • Suboptimal pooling due to unknown or unpredictable prevalence can lead to increased surveillance program costs.

Purpose of the Study:

  • To evaluate the performance of different pooling optimization methods in maximizing testing economy and cost reduction.
  • To compare the effectiveness of various methods for approximating optimal pool size (OPS) across different surveillance programs.
  • To assess the compatibility of pooling optimization methods with surveillance priorities, sampling practices, and pathogen infection dynamics.

Main Methods:

  • Simulated different pooling optimization methods using Monte Carlo simulations.
  • Utilized data from bovine viral diarrhea virus (BVDV) and *Theileria orientalis* surveillance submissions.
  • Determined true prevalence, OPS, and historical prevalence for each submission.
  • Trialed fixed pool sizes, historical prevalence, and prevalence estimation testing methods.

Main Results:

  • Contrasting results were observed between BVDV and *T. orientalis* surveillance.
  • Historical prevalence was the most reliable optimization method for BVDV.
  • *T. orientalis* surveillance required significantly more tests with historical prevalence compared to truly optimized pooling (p <0.05).

Conclusions:

  • The selection of a pooling optimization approach must consider the interplay of pathogen infection dynamics, sampling practices, and surveillance program priorities.
  • No single pooling optimization method is universally superior; context-specific evaluation is necessary.
  • Accurate prevalence estimation is critical for effective and economical disease surveillance.