Estimating data-driven COVID-19 mitigation strategies for safe university reopening

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

Reviewed by

Read the full article

Listed in

Log in to save this article

Abstract

After one pandemic year of remote or hybrid instructional modes, universities in the United States are now planning for an in-person fall semester in 2021. However, it is uncertain what the vaccination rate will look like after students, faculty, and staff return to campus. 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 COVID-19 in university settings. In this paper, we aim to identify the immunity threshold that, if exceeded, would lead to a relatively safe on-campus experience for the university population. With relaxed non-pharmaceutical interventions, we estimated that immunity in at least 60% of the university population is needed for safe university reopening. Still, attention needs to be paid to extreme events that could lead to huge infection size spikes. At an immune level of 60%, continuing non-pharmaceutical interventions, such as wearing masks, could lead to an 89% reduction in the maximum cumulative infection, which reflects the possible non-negligible infection size from extreme events.

Article activity feed

  2. 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.

  3. 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.

  4. 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

    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.

  5. Version published to 10.1101/2021.08.13.21261983v1 on medRxiv