Full genome viral sequences inform patterns of SARS-CoV-2 spread into and within Israel

  1. Danielle Miller
  2. Michael A. Martin
  3. Noam Harel
  4. Omer Tirosh
  5. Talia Kustin
  6. Moran Meir
  7. Nadav Sorek
  8. Shiraz Gefen-Halevi
  9. Sharon Amit
  10. Olesya Vorontsov
  11. Avraham Shaag
  12. Dana Wolf
  13. Avi Peretz
  14. Yonat Shemer-Avni
  15. Diana Roif-Kaminsky
  16. Naama M. Kopelman
  17. Amit Huppert
  18. Katia Koelle
  19. Adi Stern

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Abstract

Full genome sequences are increasingly used to track the geographic spread and transmission dynamics of viral pathogens. Here, with a focus on Israel, we sequence 212 SARS-CoV-2 sequences and use them to perform a comprehensive analysis to trace the origins and spread of the virus. We find that travelers returning from the United States of America significantly contributed to viral spread in Israel, more than their proportion in incoming infected travelers. Using phylodynamic analysis, we estimate that the basic reproduction number of the virus was initially around 2.5, dropping by more than two-thirds following the implementation of social distancing measures. We further report high levels of transmission heterogeneity in SARS-CoV-2 spread, with between 2-10% of infected individuals resulting in 80% of secondary infections. Overall, our findings demonstrate the effectiveness of social distancing measures for reducing viral spread.

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  1. Version published to 10.1038/s41467-020-19248-0
  2. ScreenIT

    SciScore for 10.1101/2020.05.21.20104521: (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
    Determining genome consensus sequences: Sequencing reads were trimmed using pTrimmer, a multiplexing primer trimming tool (Zhang, et al. 2019), and then aligned to the reference genome of the SARS-CoV-2 (GenBank ID MN908947) using our AccuNGS pipeline (Gelbart, et al. 2019), which is based on BLAST (Altschul, et al. 1997), using an e-value of 10-9.
    AccuNGS
    suggested: None
    BLAST
    suggested: (BLASTX, RRID:SCR_001653)
    Both IQ Tree and TreeTime were executed using the Augur python package.
    Augur
    suggested: None
    python
    suggested: (IPython, RRID:SCR_001658)
    Phylodynamic Analysis: Phylodynamic analyses were conducted using BEAST2 v2.6.2 (Bouckaert, et al. 2019) and PhyDyn v1.3.6 (Volz and Siveroni 2018).
    PhyDyn
    suggested: (PhyDyn, RRID:SCR_018544)
    XML files to run both BEAST2 and PhyDyn were generated using a custom Python 3 script which was designed to edit a template XML file originally generated with BEAUti and manually edited.
    BEAST2
    suggested: (BEAST2, RRID:SCR_017307)
    BEAST2 and PhyDyn outputs were visualized using Python 3, Matplotlib (Hunter 2007), Seaborn, and Baltic (https://github.com/evogytis/baltic).
    Matplotlib
    suggested: (MatPlotLib, RRID:SCR_008624)

    Results from OddPub: Thank you for sharing your code.

    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.

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  3. Version published to 10.1101/2020.05.21.20104521v1 on medRxiv