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Optimal group testing with heterogeneous risks.

Nina Bobkova1, Ying Chen2, Hülya Eraslan3,4

  • 1Department of Economics, Rice University and CEPR, Houston, USA.

Economic Theory
|June 26, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an optimized group testing algorithm for infectious diseases, reducing the number of tests required. For certain infection probabilities, grouping one high-risk individual with low-risk individuals is most efficient.

Keywords:
Group testingHeterogeneous risksNegative assortative matchingPooled testingPositive assortative matching

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Infectious disease surveillance requires efficient testing strategies.
  • Traditional group testing methods may not be optimal for populations with varying risk levels.

Purpose of the Study:

  • To develop and evaluate an optimal group testing algorithm for individuals with heterogeneous infectious disease risks.
  • To compare the efficiency of the proposed algorithm against existing methods like Dorfman's.

Main Methods:

  • Mathematical modeling of group testing strategies.
  • Analysis of optimal group composition based on infection probabilities.
  • Simulation and comparison with established group testing protocols.

Main Results:

  • The proposed algorithm significantly reduces the number of tests needed compared to Dorfman's method.
  • Optimal strategy involves heterogeneous groups with one high-risk individual when infection probabilities are low.
  • Optimal group test size was found to be four for parameters including U.S. COVID-19 positivity rates.

Conclusions:

  • Heterogeneous group testing can be highly efficient under specific epidemiological conditions.
  • The findings have practical implications for designing testing strategies in public health and team management.
  • Optimized group testing can improve resource allocation during disease outbreaks.