Estimating COVID-19 Cases on University Campuses Prior To Semester

  1. Alex Fout
  2. Jude Bayham
  3. Margaret J Gutilla
  4. Bailey K Fosdick
  5. Heather Pidcoke
  6. Michael Kirby
  7. Peter Jan van Leeuwen
  8. Chuck Anderson

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Abstract

For many institutions of higher learning, the beginning of each semester is marked by a significant migration of young adults into the area. In the midst of the COVID19 pandemic, this presents an opportunity for active cases to be introduced into a community. Prior to the Fall 2020 semester, Colorado State University researchers combined student home locations with recent case counts compiled by the New York Times to assign a probability to each individual of arriving with COVID19. These probabilities were combined to estimate that there would be 7.8 new cases among the on-campus population. Comprehensive testing of arriving students revealed 7 new cases, which validated the approach. The procedure was repeated to explore what could happen if students had returned to campus after Fall break. The estimate of 48 cases corroborated the University’s early decision to transition to fully remote learning after break.

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    SciScore for 10.1101/2021.01.22.21250120: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: This project was reviewed and approved by CSU’s Institutional Review Board under protocol 20-10414H.
    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 and data.

    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

    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.

  2. Version published to 10.1101/2021.01.22.21250120v1 on medRxiv