Reconstructing SARS-CoV-2 infection dynamics through the phylogenetic inference of unsampled sources of infection

  1. Deshan Perera
  2. Ben Perks
  3. Michael Potemkin
  4. Andy Liu
  5. Paul M. K. Gordon
  6. M. John Gill
  7. Quan Long
  8. Guido van Marle

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Abstract

The COVID-19 pandemic has illustrated the importance of infection tracking. The role of asymptomatic, undiagnosed individuals in driving infections within this pandemic has become increasingly evident. Modern phylogenetic tools that take into account asymptomatic or undiagnosed individuals can help guide public health responses. We finetuned established phylogenetic pipelines using published SARS-CoV-2 genomic data to examine reasonable estimate transmission networks with the inference of unsampled infection sources. The system utilised Bayesian phylogenetics and TransPhylo to capture the evolutionary and infection dynamics of SARS-CoV-2. Our analyses gave insight into the transmissions within a population including unsampled sources of infection and the results aligned with epidemiological observations. We were able to observe the effects of preventive measures in Canada’s “Atlantic bubble” and in populations such as New York State. The tools also inferred the cross-species disease transmission of SARS-CoV-2 transmission from humans to lions and tigers in New York City’s Bronx Zoo. These phylogenetic tools offer a powerful approach in response to both the COVID-19 and other emerging infectious disease outbreaks.

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  1. Version published to 10.1371/journal.pone.0261422
  2. ScreenIT

    SciScore for 10.1101/2021.01.04.21249233: (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
    Multiple Sequence Alignment (MSA): MSA was performed using Multiple Alignment using Fast Fourier Transform (MAFFT) [21].
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    Phylogenetic parameter selection: Phylogenetic reconstruction was performed through Bayesian time-trees employing BEAST 2.6.2 [25].
    BEAST
    suggested: (BEAST, RRID:SCR_010228)
    Visualization of transmission networks: Data visualization was conducted using Gephi 0.9.2 a specialized network analysis software [26].
    Gephi
    suggested: (Gephi, RRID:SCR_004293)

    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.

  3. Version published to 10.1101/2021.01.04.21249233v1 on medRxiv