Estimating data-driven coronavirus disease 2019 mitigation strategies for safe university reopening

  1. Qihui Yang
  2. Don M. Gruenbacher
  3. Caterina M. Scoglio

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Abstract

After one pandemic year of remote or hybrid instructional modes, universities struggled with plans for an in-person autumn (fall) semester in 2021. To help inform university reopening policies, we collected survey data on social contact patterns and developed an agent-based model to simulate the spread of severe acute respiratory syndrome coronavirus 2 in university settings. Considering a reproduction number of R 0 = 3 and 70% immunization effectiveness, we estimated that at least 80% of the university population immunized through natural infection or vaccination is needed for safe university reopening with relaxed non-pharmaceutical interventions (NPIs). By contrast, at least 60% of the university population immunized through natural infection or vaccination is needed for safe university reopening when NPIs are adopted. Nevertheless, attention needs to be paid to large-gathering events that could lead to infection size spikes. At an immunization coverage of 70%, continuing NPIs, such as wearing masks, could lead to a 78.39% reduction in the maximum cumulative infections and a 67.59% reduction in the median cumulative infections. However, even though this reduction is very beneficial, there is still a possibility of non-negligible size outbreaks because the maximum cumulative infection size is equal to 1.61% of the population, which is substantial.

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  1. Version published to 10.1098/rsif.2021.0920
  3. Rapid Reviews Infectious Diseases

    Hanisha Tatapudi

    Review 1: "Estimating Data-Driven COVID-19 Mitigation Strategies for Safe University Reopening"

    Reviewers find that this preprint offers a straightforward model to explore university re-opening--with important "micro-scale" policy implications--but offer a number of suggestions to further refine and clarify the model's construction and parameters.

  4. Rapid Reviews Infectious Diseases

    Xiang Chen, Ph.D.;

    Review 2: "Estimating Data-Driven COVID-19 Mitigation Strategies for Safe University Reopening"

    Reviewers find that this preprint offers a straightforward model to explore university re-opening--with important "micro-scale" policy implications--but offer a number of suggestions to further refine and clarify the model's construction and parameters.

  5. ScreenIT

    SciScore for 10.1101/2021.08.13.21261983: (What is this?)

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

    Table 1: Rigor

    Ethicsnot detected.
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot 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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

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  6. Version published to 10.1101/2021.08.13.21261983v1 on medRxiv