Revealing fine-scale spatiotemporal differences in SARS-CoV-2 introduction and spread

  1. Gage K. Moreno
  2. Katarina M. Braun
  3. Kasen K. Riemersma
  4. Michael A. Martin
  5. Peter J. Halfmann
  6. Chelsea M. Crooks
  7. Trent Prall
  8. David Baker
  9. John J. Baczenas
  10. Anna S. Heffron
  11. Mitchell Ramuta
  12. Manjeet Khubbar
  13. Andrea M. Weiler
  14. Molly A. Accola
  15. William M. Rehrauer
  16. Shelby L. O’Connor
  17. Nasia Safdar
  18. Caitlin S. Pepperell
  19. Trivikram Dasu
  20. Sanjib Bhattacharyya
  21. Yoshihiro Kawaoka
  22. Katia Koelle
  23. David H. O’Connor
  24. Thomas C. Friedrich

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Abstract

Evidence-based public health approaches that minimize the introduction and spread of new SARS-CoV-2 transmission clusters are urgently needed in the United States and other countries struggling with expanding epidemics. Here we analyze 247 full-genome SARS-CoV-2 sequences from two nearby communities in Wisconsin, USA, and find surprisingly distinct patterns of viral spread. Dane County had the 12 th known introduction of SARS-CoV-2 in the United States, but this did not lead to descendant community spread. Instead, the Dane County outbreak was seeded by multiple later introductions, followed by limited community spread. In contrast, relatively few introductions in Milwaukee County led to extensive community spread. We present evidence for reduced viral spread in both counties following the statewide “Safer at Home” order, which went into effect 25 March 2020. Our results suggest patterns of SARS-CoV-2 transmission may vary substantially even in nearby communities. Understanding these local patterns will enable better targeting of public health interventions.

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  1. Version published to 10.1038/s41467-020-19346-z
  2. ScreenIT

    SciScore for 10.1101/2020.07.09.20149104: (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

    Software and Algorithms
    SentencesResources
    We used custom python scripts to filter and clean metadata.
    python
    suggested: (IPython, RRID:SCR_001658)
    FastTree 54 and are available at https://github.com/roblanf/sarscov2phylo/.
    FastTree
    suggested: (FastTree, RRID:SCR_015501)
    Any sequences which were not already in the down-sampled alignment described above were added using MAFFT.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Results were visualized using Matplotlib 58, Seaborn (https://github.com/mwaskom/seaborn), and Baltic (https://github.com/evogytis/baltic).
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)
    Phylodynamic analysis: Bayesian phylogenetic inference and dynamic modelling were performed with BEAST2 software (v2.6.2) 59 and the PhyDyn package (v1.3.6) 14.
    BEAST2
    suggested: (BEAST2, RRID:SCR_017307)
    PhyDyn
    suggested: (PhyDyn, RRID:SCR_018544)
    Parameter traces were visually inspected for adequate mixing and convergence in Tracer (v1.7.1).
    Tracer
    suggested: (Tracer, RRID:SCR_019121)

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    There are some important caveats to this study. Of the total reported positives in each county during the study period, high-quality sequences were available for 27% of test-positive cases in Dane County, but only 5% of test-positive cases in Milwaukee County 24,25. Despite the deep sampling of SARS-CoV-2 sequences in Wisconsin relative to other regions in the US, even greater targeted sequencing efforts may be required to fully capture the sequence heterogeneity conferred by multiple introduction events and variable superspreading dynamics. It is possible additional sequencing in Milwaukee County would uncover additional viral lineages, or that the 5% of cases we sequenced do not fully represent the diversity of viruses found throughout the county, skewing our observations. However, in analyzing sample metadata we find no evidence that particular locations within Milwaukee County were over- or under-sampled relative to their known SARS-CoV-2 prevalence. Another potential explanation is that Milwaukee County was under-testing their epidemic. Throughout the period analyzed here, the percentage of SARS-CoV-2 tests returning positive in Milwaukee County was ~20%, compared to only ~5% in Dane County 24,25. As we are only able to sequence test-positive samples, under-testing in Milwaukee County may have limited our ability to capture a complete representation of their epidemic. Through increased testing and continued sequencing efforts, it is likely that we will be able to more fu...

    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

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  3. Version published to 10.1101/2020.07.09.20149104v1 on medRxiv