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Updated: Aug 14, 2025

Large-Scale SARS-CoV-2 Testing Utilizing Saliva and Transposition Sample Pooling
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Leveraging network structure to improve pooled testing efficiency.

Daniel K Sewell1

  • 1Department of Biostatistics University of Iowa Iowa City Iowa USA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
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This study optimizes pooled testing for infection control by grouping individuals based on disease transmission networks. This network-informed approach significantly reduces the number of tests needed for efficient screening.

Keywords:
epidemiologygroup testinginfectious diseaseoptimisationtransmission networks

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Area of Science:

  • Epidemiology
  • Computational Biology
  • Public Health

Background:

  • Screening is crucial for infection control, identifying and isolating infectious individuals.
  • Comprehensive screening poses a significant resource burden.
  • Pooled testing offers a resource-efficient alternative by testing groups before individuals.

Purpose of the Study:

  • To enhance the efficiency of pooled testing strategies for infectious disease screening.
  • To reduce the resource burden associated with widespread screening programs.
  • To leverage underlying transmission networks for optimized pooled testing.

Main Methods:

  • Developed a simulated annealing algorithm to assign individuals to pools based on network structure.
  • Evaluated the algorithm's efficiency using the ratio of correct classifications to tests performed.
  • Utilized an agent-based model simulating SARS-CoV-2 spread in a school setting for validation.

Main Results:

  • Network-informed pooling significantly improves pooled testing efficiency.
  • The proposed approach reduces the total number of tests required for regular screening.
  • Reductions in testing are robust even with incomplete or noisy network information.

Conclusions:

  • Assigning individuals to pools based on disease transmission networks optimizes screening efficiency.
  • This strategy substantially decreases the resource burden of infectious disease surveillance.
  • The method is effective even when network data is imperfect, offering practical applicability.