Contact tracing evaluation for COVID-19 transmission in the different movement levels of a rural college town in the USA

  1. Sifat A. Moon
  2. Caterina M. Scoglio

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Abstract

Contact tracing can play a key role in controlling human-to-human transmission of a highly contagious disease such as COVID-19. We investigate the benefits and costs of contact tracing in the COVID-19 transmission. We estimate two unknown epidemic model parameters (basic reproductive number $$R_0$$ R 0 and confirmed rate $$\delta _2$$ δ 2 ) by using confirmed case data. We model contact tracing in a two-layer network model. The two-layer network is composed by the contact network in the first layer and the tracing network in the second layer. In terms of benefits, simulation results show that increasing the fraction of traced contacts decreases the size of the epidemic. For example, tracing $$25\%$$ 25 % of the contacts is enough for any reopening scenario to reduce the number of confirmed cases by half. Considering the act of quarantining susceptible households as the contact tracing cost, we have observed an interesting phenomenon. The number of quarantined susceptible people increases with the increase of tracing because each individual confirmed case is mentioning more contacts. However, after reaching a maximum point, the number of quarantined susceptible people starts to decrease with the increase of tracing because the increment of the mentioned contacts is balanced by a reduced number of confirmed cases. The goal of this research is to assess the effectiveness of contact tracing for the containment of COVID-19 spreading in the different movement levels of a rural college town in the USA. Our research model is designed to be flexible and therefore, can be used to other geographic locations.

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  1. Version published to 10.1038/s41598-021-83722-y
  2. Version published to 10.1101/2020.06.24.20139204v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.06.24.20139204: (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: 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.

    About SciScore

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  4. Version published to 10.1101/2020.06.24.20139204v1 on medRxiv