Population-scale testing can suppress the spread of COVID-19

  1. Jussi Taipale
  2. Paul Romer
  3. Sten Linnarsson

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Abstract

We propose an additional intervention that would contribute to the control of the COVID-19 pandemic, offer more protection for people working in essential jobs, and help guide an eventual reopening of society. The intervention is based on: (1) testing every individual (2) repeatedly, and (3) self-quarantine of infected individuals. Using a standard epidemiological model (SIR), we show here that by identification and isolation of the majority of infectious individuals, including those who may be asymptomatic, the reproduction number R 0 of SARS-CoV-2 would be reduced well below 1.0, and the epidemic would collapse. We replicate these observations in a more complex stochastic dynamic model on a social network graph. We also find that the testing regime would be additive to other interventions, and be effective at any level of prevalence. If adopted as a policy, any industrial society could sustain the regime for as long as it takes to find a safe and effective cure or vaccine. Our model also indicates that unlike sampling-based tests, population-scale testing does not need to be very accurate: false negative rates up to 15% could be tolerated if 80% comply with testing every ten days, and false positives can be almost arbitrarily high when a high fraction of the population is already effectively quarantined. Testing at the required scale would be feasible if existing qPCR-based methods are scaled up and multiplexed. A mass produced, low throughput field test kit could also be carried out at home. Economic analysis also supports the feasibility of the approach: current reagent costs for tests are in the range of a dollar or less, and the estimated benefits for population-scale testing are so large that the policy would be cost-effective even if the costs were larger by more than two orders of magnitude. To identify both active and previous infections, both viral RNA and antibodies could be tested. All technologies to build such test kits, and to produce them in the scale required to test the entire worlds’ population exist already. Integrating them, scaling up production, and implementing the testing regime will require resources and planning, but at a scale that is very small compared to the effort that every nation would devote to defending itself against a more traditional foe.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.27.20078329: (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

    Software and Algorithms
    SentencesResources
    We used the seirsplus Python package (https://github.com/ryansmcgee/seirsplus), which models a population where each individual transitions between six states: susceptible, exposed, detected-exposed, infectious, detected-infectious, and recovered.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    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 10.1101/2020.04.27.20078329v2 on medRxiv
  3. Version published to 10.1101/2020.04.27.20078329v1 on medRxiv