Designing Efficient Contact Tracing Through Risk-Based Quarantining

  1. Andrew Perrault
  2. Marie Charpignon
  3. Jonathan Gruber
  4. Milind Tambe
  5. Maimuna S. Majumder

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Abstract

Contact tracing for COVID-19 is especially challenging because transmission often occurs in the absence of symptoms and because a purported 20% of cases cause 80% of infections, resulting in a small risk of infection for some contacts and a high risk for others. Here, we introduce risk-based quarantine, a system for contact tracing where each cluster (a group of individuals with a common source of exposure) is observed for symptoms when tracing begins, and clusters that do not display them are released from quarantine. We show that, under our assumptions, risk-based quarantine reduces the amount of quarantine time served by more than 30%, while achieving a reduction in transmission similar to standard contact tracing policies where all contacts are quarantined for two weeks. We compare our proposed risk-based quarantine approach against test-driven release policies, which fail to achieve a comparable level of transmission reduction due to the inability of tests to detect exposed people who are not yet infectious but will eventually become so. Additionally, test-based release policies are expensive, limiting their effectiveness in low-resource environments, whereas the costs imposed by risk-based quarantine are primarily in terms of labor and organization.

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  1. ScreenIT

    SciScore for 10.1101/2020.11.16.20227389: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our modeling approach has significant limitations. We highlight four. First, we aggregate data from heterogeneous and possibly incompatible sources with respect to the onset and presentation of symptoms; further, there are some areas where very little relevant data are available. Our estimates of the effectiveness associated with RBQ depend critically on the fraction of individuals who develop COVID-19 symptoms after exposure and the timing of when they do so. To evaluate the effectiveness of RBQ, we need to know the probability of COVID-19 given observed symptoms and known exposure to an individual who tested positive for SARS-CoV-2 infection. The less predictive COVID-19-like symptoms are of infection, the less effective RBQ will be at reducing the amount of quarantine served. It may be desirable to observe contacts for a specific set of highly predictive symptoms, such as anosmia (Petersen and Phillips 2020). Doing so would yield higher confidence that a symptomatic individual is infected at the cost that more infected individuals would be asymptomatic under this definition. Second, we are missing important data on the subject of adherence to quarantine and isolation. Ideally, our model would be parameterized using a distribution of the number of contacts of quarantined and isolated individuals as a function of quarantine length and motivation, i.e., information provided to the individual about the likelihood that they are sick or infectious. Unfortunately, most existing s...

    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

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  2. Version published to 10.1101/2020.11.16.20227389v2 on medRxiv
  3. Version published to 10.1101/2020.11.16.20227389v1 on medRxiv