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Optimizing Pooled Testing for Estimating the Prevalence of Multiple Diseases.

Md S Warasi1, Laura L Hungerford2, Kevin Lahmers2

  • 1Department of Mathematics and Statistics, Radford University, Whitt Hall 224, Radford, VA 24142 USA.

Journal of Agricultural, Biological, and Environmental Statistics
|August 17, 2022
PubMed
Summary
This summary is machine-generated.

Optimizing pooled testing strategies using mathematical models enhances disease diagnosis efficiency for low-prevalence diseases. This research identifies ideal pool sizes and methods for precise, cost-effective multi-disease surveillance.

Keywords:
Animal testingExperimental designGroup testingScreeningSurveillance

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Veterinary Medicine

Background:

  • Pooled testing offers efficiency gains for diagnosing diseases with low prevalence.
  • Standard pool sizes (e.g., 2, 5, 10) are commonly used but may not be optimal.
  • Optimization theory can guide the selection of ideal pool sizes and strategies.

Purpose of the Study:

  • To optimize the precision of disease prevalence estimators from multiplex pooled testing data.
  • To evaluate the estimation and cost efficiency of pooled testing strategies for animal disease surveillance.
  • To determine optimal pooling strategies for jointly estimating the prevalence of multiple diseases.

Main Methods:

  • Utilized optimization theory to determine ideal pool sizes and pooling strategies.
  • Analyzed estimation efficiency (precision) and cost efficiency of estimators.
  • Adjusted for the number of expended tests in the analysis.
  • Developed a software application using the R shiny package for implementation.

Main Results:

  • Identified specific pooling strategies that maximize benefits for jointly estimating multiple disease prevalences.
  • Demonstrated that optimized pooling can significantly enhance the precision of prevalence estimators.
  • Showcased the utility of the developed R package for practical application.

Conclusions:

  • Optimized pooled testing protocols improve diagnostic efficiency and precision, especially for low-prevalence and multiple diseases.
  • The findings are applicable to both simple and complex pooled testing scenarios, including those with individual retesting.
  • The provided software facilitates the implementation of advanced pooled testing strategies in disease surveillance.