A Social Network Model of the COVID-19 Pandemic

  1. Pei Jun Zhao

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Abstract

In the COVID-19 coronavirus pandemic, currently vaccines and specific anti-viral treatment are not yet available. Thus, preventing viral transmission by case isolation, quarantine, and social distancing is essential to slowing its spread. Here we model social networks using weighted graphs, where vertices represent individuals and edges represent contact. As public health measures are implemented, connectivity in the graph decreases, resulting in lower effective reproductive numbers, and reduced viral transmission. For COVID-19, model parameters were derived from the coronavirus epidemic in China, validated by epidemic data in Italy, then applied to the United States. We calculate that, in the U.S., the public is able to contain viral transmission by limiting the average number of contacts per person to less than 7 unique individuals over each 5 day period. This increases the average social distance between individuals to 10 degrees of separation.

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  1. ScreenIT

    SciScore for 10.1101/2020.03.23.20041798: (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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  2. Version published to 10.1101/2020.03.23.20041798 on medRxiv