SARS-CoV-2 transmission chains from genetic data: a Danish case study

  1. Andreas Bluhm
  2. Matthias Christandl
  3. Fulvio Gesmundo
  4. Frederik Ravn Klausen
  5. Laura Mančinska
  6. Vincent Steffan
  7. Daniel Stilck França
  8. Albert H. Werner

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Abstract

Background

The COVID-19 pandemic caused by the SARS-CoV-2 virus started in China in December 2019 and has since spread globally. Information about the spread of the virus in a country can inform the gradual reopening of a country and help to avoid a second wave of infections. Denmark is currently opening up after a lockdown in mid-March.

Methods

We perform a phylogenetic analysis of 742 publicly available Danish SARS-CoV-2 genome sequences and put them into context using sequences from other countries.

Result

Our findings are consistent with several introductions of the virus to Denmark from independent sources. We identify several chains of mutations that occurred in Denmark and in at least one case find evidence that the virus spread from Denmark to other countries. A number of the mutations found in Denmark are non-synonymous, and in general there is a considerable variety of strains. The proportions of the most common haplotypes is stable after lockdown.

Conclusion

Our work shows how genetic data can be used to identify routes of introduction of a virus into a region and provide alternative means for verifying existing assumptions. For example, our analysis supports the hypothesis that the virus was brought to Denmark by skiers returning from Ischgl. On the other hand, we identify transmission chains suggesting that Denmark was part of a network of countries among which the virus was being transmitted; thus challenging the common narrative that Denmark only got infected from abroad. Our analysis does not indicate that the major haplotypes appearing in Denmark have a different degree of virality. Our methods can be applied to other countries, regions or even highly localised outbreaks. When used in real-time, we believe they can serve to identify transmission events and supplement traditional methods such as contact tracing.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.29.123612: (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
    Sequence alignment: We perform multiple sequence alignment (MSA) using the R-package DECIPHER [19, 20].
    R-package
    suggested: None
    DECIPHER
    suggested: (DECIPHER, RRID:SCR_006552)
    Most of our analysis uses R [23] and except for the R-packages mentioned in the previous sections, we also use stringr, dplyr, ggplot2 and ape [24].
    ggplot2
    suggested: (ggplot2, RRID:SCR_014601)

    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 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

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2020.05.29.123612 on bioRxiv