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High prevalence group testing in epidemiology with geometrically inspired algorithms.

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Group testing using the hypercube algorithm significantly reduces clinical laboratory workload and costs for SARS-CoV-2 testing. This method achieved 50-72.5% test reduction in experiments, proving efficient for mass surveillance.

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Area of Science:

  • Infectious Disease Epidemiology
  • Clinical Laboratory Science
  • Bioinformatics and Computational Biology

Background:

  • The SARS-CoV-2 pandemic highlighted the strain on clinical laboratories due to mass surveillance demands.
  • Group testing strategies offer a resource-efficient solution for large-scale testing during public health emergencies.

Purpose of the Study:

  • To adapt and expand the hypercube algorithm for group testing in scenarios with high prevalence.
  • To optimize pooling designs for specific sample sizes and prevalence rates to maximize test reduction.
  • To validate the adapted hypercube methodology through empirical laboratory experiments.

Main Methods:

  • Exploration and expansion of the novel hypercube algorithm for high group prevalence settings.
  • Numerical studies to investigate the limits and optimize pooling designs.
  • Hyperparameter optimization for maximizing test reduction and examining standard deviation for resilience and precision.
  • Empirical validation using pooled SARS-CoV-2 samples in laboratory experiments.

Main Results:

  • The adapted hypercube algorithm was successfully applied to SARS-CoV-2 sample groups (50-200 samples) with up to 10% group prevalence.
  • Achieved test reductions ranging from 50% to 72.5% compared to individual testing in experimental setups.
  • Simulations indicated potential for higher test reductions based on sample size and group prevalence.

Conclusions:

  • The hypercube algorithm, adapted for high prevalence, offers a validated and efficient approach for resource conservation in mass surveillance testing.
  • This methodology provides a scalable solution for future epidemiological testing needs, significantly reducing laboratory workload and costs.