Modeling and Global Sensitivity Analysis of Strategies to Mitigate Covid-19 Transmission on a Structured College Campus

  1. Lihong Zhao
  2. Fabian Santiago
  3. Erica M. Rutter
  4. Shilpa Khatri
  5. Suzanne S. Sindi

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Abstract

In response to the COVID-19 pandemic, many higher educational institutions moved their courses on-line in hopes of slowing disease spread. The advent of multiple highly-effective vaccines offers the promise of a return to “normal” in-person operations, but it is not clear if—or for how long—campuses should employ non-pharmaceutical interventions such as requiring masks or capping the size of in-person courses. In this study, we develop and fine-tune a model of COVID-19 spread to UC Merced’s student and faculty population. We perform a global sensitivity analysis to consider how both pharmaceutical and non-pharmaceutical interventions impact disease spread. Our work reveals that vaccines alone may not be sufficient to eradicate disease dynamics and that significant contact with an infectious surrounding community will maintain infections on-campus. Our work provides a foundation for higher-education planning allowing campuses to balance the benefits of in-person instruction with the ability to quarantine/isolate infectious individuals.

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  1. Version published to 10.1007/s11538-022-01107-2
  2. Version published to 10.1101/2022.04.01.22273316v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2022.04.01.22273316: (What is this?)

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

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    Experimental Models: Organisms/Strains
    SentencesResources
    In this early stage of approximately exponential growth in the number of cumulative case, C(t), where C′(t) ≈ rC(t), we can estimate r by using cumulative cases at two points in time C(t1) and C(t2), where r = log (C(t1)/C(t2)) /(t1 − t2) and t1 > t2.
    t1 − t2
    suggested: None

    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: We detected the following sentences addressing limitations in the study:
    We note there are several limitations with the current study. In our model it is assumed that vaccination makes the recipient immune (e.g., there are no breakthrough cases). A logical future direction would include waning immunity and booster shots. Another option would be to include vaccinated sub-populations with decreased transmission rates and shorter convalescence times. As differing variants have become dominant (e.g., delta, omicron), the parameters would need to be altered to reflect increased transmission rates and, perhaps, shortened infectious duration. We also assumed 100% compliance with interventions, meaning that every member of the community wears well-fitting masks at all times when they are indoors. Overall, our model exhibits results consistent with current public health messaging. Our model predicts that, despite increasing vaccination rates, it remains important to continue to socially distance and wear masks to reduce transmission. Whether these results will hold for more recent variants, such as delta and omicron, remains to be seen.

    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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  4. Version published to 10.1101/2022.04.01.22273316v1 on medRxiv