Analysis and Applications of Adaptive Group Testing Methods for COVID-19

  1. Cassidy Mentus
  2. Martin Romeo
  3. Christian DiPaola

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Abstract

Testing strategies for Covid-19 to maximize number of people tested are urgently needed. Recently, it has been demonstrated that RT-PCR has the sensitivity to detect one positive case in a mixed sample of 32 cases [12], In this paper we propose adaptive group testing strategies based on generalized binary splitting (CBS) [5], where we restrict the group test to the largest group that can be used. The method starts by choosing a group from the population to be tested, performing a test on the combined sample from the entire group, and progressively splitting the group further into subgroups. Compared to individual testing at 4% prevalence, we save 74%; at 1% we save 91%; and at .1% we save 98% of tests. We analyze the number of times each sample is used and show that the method is still efficient if we resort to testing a case individually if the sample is running low.

In addition we recommend clinical screening to filter out individuals with symptoms and show this leaves us with a population with lower prevalence. Our approach is particularly applicable to vulnerable confined populations such as nursing homes, prisons, military ships and cruise ships.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.05.20050245: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2020.04.05.20050245v2 on medRxiv
  3. Version published to 10.1101/2020.04.05.20050245v1 on medRxiv