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Informed sequential pooling approach to detect SARS-CoV-2 infection.

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A two-step pooling strategy can efficiently diagnose COVID-19 (coronavirus disease 2019) by testing pooled samples. This method significantly reduces costs and time for large-scale SARS-CoV-2 testing.

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Area of Science:

  • Virology
  • Epidemiology
  • Clinical Diagnostics

Background:

  • The COVID-19 pandemic caused by SARS-CoV-2 necessitates rapid and large-scale diagnostic testing.
  • Timely diagnosis, including testing asymptomatic individuals, is crucial for controlling SARS-CoV-2 spread.
  • Clinical laboratories require increased throughput for effective pandemic response.

Purpose of the Study:

  • To evaluate a two-step sequential pooling strategy for SARS-CoV-2 detection.
  • To assess the efficiency of pooled sample testing in reducing the number of diagnostic tests required.
  • To determine if pooling strategies can be customized based on cohort characteristics and virus frequency.

Main Methods:

  • Simulated diagnostic testing protocols using pooled samples.
  • Assessed efficiency based on the number of tests needed for random and "informed" pools.
  • Varied pool sizes and virus frequencies in different simulation scenarios.

Main Results:

  • The proposed two-step pooling procedure effectively identifies positive subjects.
  • Significant cost and time savings were demonstrated compared to individual testing.
  • Pooling strategies can be tailored based on epidemiological data and cohort specifics.

Conclusions:

  • A sequential pooling approach offers an efficient method for large-scale SARS-CoV-2 diagnostics.
  • This strategy enhances laboratory throughput while reducing resource utilization.
  • Customizable pooling enhances diagnostic efficiency according to specific population and disease prevalence characteristics.