High prevalence group testing in epidemiology with geometrically inspired algorithms.

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.