TESTING, TRACING AND SOCIAL DISTANCING: ASSESSING OPTIONS FOR THE CONTROL OF COVID_19

  1. Lia Humphrey
  2. Edward W. Thommes
  3. Roie Fields
  4. Laurent Coudeville
  5. Naseem Hakim
  6. Ayman Chit
  7. Monica G. Cojocaru

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Abstract

In this work we present an analysis of non-pharmaceutical interventions implemented around the world in the fight against COVID-19: Social distancing, shelter-in-place, mask wearing, etc measures to protect the susceptible, together with, in various degrees, testing & contact-tracing to identify, isolate and treat the infected. The majority of countries have relied on the former, while ramping up their testing and tracing capabilities. We consider the examples of South Korea, Italy, Canada and the United States. By fitting a disease transmission model to daily case report data, we show that in each of the four countries their combination of social-distancing and testing/tracing to date have had a significant impact on the evolution of their pandemic curves. In this work we estimate the average isolation rates of infected individuals needing to occur in each country as a result of large-scale testing and contact tracing as a mean of lifting social distancing measures, without a resurgence of COVID-19. We find that an average isolation rate of an infected individual every 4.5 days (South Korea), 5.7 days (Canada) and to 6 days (Italy) would be sufficient. We also find that a rate of under 3.5 days will help in the United States, although it would not completely mitigate the second wave the country is currently under.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.23.20077503: (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: We detected the following sentences addressing limitations in the study:
    Our work is subject to a number of limitations. We are simulating large, heterogeneous, geographically widely-distributed populations with an unstratified disease transmission model. Although we have taken the proportion of asymptomatic COVID-19 cases to be 50%, informed by a recent CDC report, assuming asymptomatic infection confers immunity, this would mean a smaller remaining pool of susceptibles and thus a lower current effective reproduction number. Estimates of R0 from time series data of cases depend, as always, on the assumed latent and infectious periods. As we have demonstrated through (Fig 7), if these periods are longer than the isolation time, then it is the latter which principally drives the disease dynamics. Thus, our findings about threshold isolation times are relatively robust against the possibility of a substantially longer COVID-19 serial interval.

    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.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.04.23.20077503v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.04.23.20077503: (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

    Antibodies
    SentencesResources
    E Bendavid , et al. , Covid-19 antibody seroprevalence in santa clara county , california . medRxiv ( 2020) . 28 .
    Covid-19
    suggested: None

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

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore is not a substitute for expert review. SciScore checks for the presence and correctness of RRIDs (research resource identifiers) in the manuscript, and detects sentences that appear to be missing RRIDs. SciScore also checks to make sure that rigor criteria are addressed by authors. It does this by detecting sentences that discuss criteria such as blinding or power analysis. SciScore does not guarantee that the rigor criteria that it detects are appropriate for the particular study. Instead it assists authors, editors, and reviewers by drawing attention to sections of the manuscript that contain or should contain various rigor criteria and key resources. For details on the results shown here, please follow this link.

  4. Version published to 10.1101/2020.04.23.20077503v1 on medRxiv