SARS-CoV-2 Genomic Surveillance Reveals Little Spread Between a Large University Campus and the Surrounding Community

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Abstract

COVID-19 has had high incidence at institutions of higher education (IHE) in the United States, but the transmission dynamics in these settings are not well understood. It remains unclear to what extent IHE-associated outbreaks have contributed to transmission in nearby communities. We implemented high-density prospective genomic surveillance to investigate these dynamics at the University of Michigan-Ann Arbor and the surrounding community during the Fall 2020 semester (August 16 th –November 24 th ). We sequenced complete SARS-CoV-2 genomes from 1659 individuals, including 468 students, representing 20% of cases in students and 25% of total confirmed cases in Washtenaw County over the study interval. Phylogenetic analysis identified over 200 introductions into the student population, most of which were not related to other student cases. There were two prolonged transmission clusters among students that spanned across multiple on-campus residences. However, there were very few genetic descendants of student clusters among non-students during a subsequent November wave of infections in the community. We conclude that outbreaks at the University of Michigan did not significantly contribute to the rise in Washtenaw County COVID-19 incidence during November 2020. These results provide valuable insights into the distinct transmission dynamics of SARS-CoV-2 among IHE populations and surrounding communities.

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  1. SciScore for 10.1101/2021.07.19.21260726: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: Use of residual SARS-CoV-2 positive specimens and collection of student status and on-campus residence were approved by the Institutional Review Board at the University of Michigan (Protocol HUM185966).
    Sex as a biological variablenot detected.
    RandomizationTo determine whether the contextual genomes were biasing our results, we generated a total of ten random subsamples of the global data using the schema described above and analyzed the data in the same manner (Figure S2C).
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Analysis of Raw Sequence Reads: We mapped reads to the Wuhan/Hu-1/2019 reference genome (GenBank MN908947.3) with BWA-MEM (Li, 2013).
    BWA-MEM
    suggested: (Sniffles, RRID:SCR_017619)
    We determined consensus sequences with samtools mpileup and iVar 1.2.1.
    samtools
    suggested: (SAMTOOLS, RRID:SCR_002105)
    We aligned genomes with MAFFT as implemented in augur.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    We inferred a maximum likelihood phylogeny with IQ-TREE with a GTR model and 1000 ultrafast bootstraps (Nguyen et al., 2015).
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    We used TempEst to fit and plot a root-to-tip regression of divergence over time (Rambaut et al., 2016).
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    We also performed Bayesian ASR using BEAST 1.10.4 using a fixed tree topology derived from TreeTime (Alpert et al., 2021; Drummond and Rambaut, 2007).
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

    Results from OddPub: Thank you for sharing your code.


    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are other important limitations. First, we were not able to access specimens from commercial testing sites, and the number of detected transmission introductions is certainly an underestimate. It is likely that many singletons in our data are in fact linked to a small number of other student cases. It is also possible that there were differential contact patterns between students who obtained testing at Michigan Medicine sites and those who did not. Next, our study is not an epidemiologic investigation with contact tracing and individual behavioral information. It is difficult to reliably assess the effectiveness of any single mitigation measure that was implemented during the fall semester from these data alone. In particular, it is difficult to disentangle whether the lack of spread from students into the community is the result of university and county case identification and isolation measures for students, partitioning of social structures between IHE and the surrounding communities, or a combination of both. Thorough contact tracing investigations of IHE-associated outbreaks with dense genomic surveillance across populations may be able to resolve these questions in greater detail. It is also possible that these dynamics could have played out differently with earlier emergence of a highly transmissible variant, such as B.1.1.7 (Alpert et al., 2021; Washington et al., 2021). This work will be a valuable point of comparison for future studies examining the effects o...

    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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