Impacts of vaccination and asymptomatic testing on SARS-CoV-2 transmission dynamics in a university setting

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Abstract

We investigate the impact of vaccination and asymptomatic testing uptake on SARS-CoV-2 transmission in a university student population using a stochastic compartmental model. We find that the magnitude and timing of outbreaks is highly variable depending on the transmissibility of the most dominant strain of SARS CoV-2 and under different vaccine uptake levels and efficacies. When delta is the dominant strain, low level interventions (no asymptomatic testing, 30% vaccinated with a vaccine that is 80% effective at reducing infection) lead to 53-71% of students become infected during the first term. Asymptomatic testing is most useful when vaccine uptake is low: when 30% of students are vaccinated, 90% uptake of asymptomatic testing leads to almost half the case numbers. With high interventions (90% using asymptomatic testing, 90% vaccinated) cumulative incidence is 7-9%, with around 80% of these cases estimated to be asymptomatic. However, under emergence of a new variant that is at least twice as transmissible as delta and with the vaccine efficacy against infection reduced to 55%, large outbreaks are likely in universities, even with very high (90%) uptake of vaccination and 100% uptake of asymptomatic testing. If vaccine efficacy against infection against this new variant is higher (70%), then outbreaks can be mitigated if there is least 50% uptake of asymptomatic testing additional to 90% uptake of vaccination. Our findings suggest that effective vaccination is critical for controlling SARS-CoV-2 transmission in university settings with asymptomatic testing ranging from additionally useful to critical, depending on effectiveness and uptake of vaccination. Other measures may be necessary to control outbreaks under the emergence of a more transmissible variant with vaccine escape.

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  1. SciScore for 10.1101/2021.11.22.21266565: (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

    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 found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).


    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.


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