SCALE19: A scalable and cost-efficient method for testing Covid-19 based on hierarchical group testing

  1. Jeremie S. Kim
  2. Justine S. Kim
  3. Hyong S. Kim

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Abstract

Containment of Covid-19 requires an extensive testing of the affected population. Some propose global testing to effectively contain Covid-19. Current tests for Covid-19 are administered individually. These tests for Covid-19 are expensive and are limited due to the lack of resources and time. We propose a simple and efficient group testing method for Covid-19. We propose a group testing method where test subjects are grouped and tested. Depending on the result of the group test, subsequent sub groups are formed and tested recursively based on a quartery search algorithm. We designed and built an evaluation model that simulates test subject population, infected test subjects according to available Covid-19 statistics, and the group testing processes in SCALE19. We considered several population models including USA and the world. Our results show that we can significantly reduce the required number of tests up to 89% without sacrificing the accuracy of the individual test of the entire population. For USA, up to 280 million tests can be reduced from the total US population of 331 million and it would be equivalent saving of $28 billion assuming a cost of $100 per test. For the world, 6.96 billion tests can be reduced from the total population of 7.8 billion and it would be equivalent to saving $696 billion. We propose SCALE19 can significantly reduce the total required number of tests compared to individual tests of the entire population. We believe SCALE19 is efficient and simple to be deployed in containment of Covid-19.

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    SciScore for 10.1101/2020.06.01.20119172: (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).

    Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

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  2. Version published to 10.1101/2020.06.01.20119172v1 on medRxiv