Revealing COVID-19 Transmission by SARS-CoV-2 Genome Sequencing and Agent Based Modelling

  1. Rebecca J Rockett
  2. Alicia Arnott
  3. Connie Lam
  4. Rosemarie Sadsad
  5. Verlaine Timms
  6. Karen-Ann Gray
  7. John-Sebastian Eden
  8. Sheryl Chang
  9. Mailie Gall
  10. Jenny Draper
  11. Eby Sim
  12. Nathan L Bachmann
  13. Ian Carter
  14. Kerri Basile
  15. Roy Byun
  16. Matthew V O’Sullivan
  17. Sharon C-A Chen
  18. Susan Maddocks
  19. Tania C. Sorrell
  20. Dominic E Dwyer
  21. Edward C Holmes
  22. Jen Kok
  23. Mikhail Prokopenko
  24. Vitali Sintchenko

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Abstract

Community transmission of the new coronavirus SARS-CoV-2 is a major public health concern that remains difficult to assess. We present a genomic survey of SARS-CoV-2 from a during the first 10 weeks of COVID-19 activity in New South Wales, Australia. Transmission events were monitored prospectively during the critical period of implementation of national control measures. SARS-CoV-2 genomes were sequenced from 209 patients diagnosed with COVID-19 infection between January and March 2020. Only a quarter of cases appeared to be locally acquired and genomic-based estimates of local transmission rates were concordant with predictions from a computational agent-based model. This convergent assessment indicates that genome sequencing provides key information to inform public health action and has improved our understanding of the COVID-19 evolution from outbreak to epidemic.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.19.048751: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    SARS-CoV-2 genome sequencing: We undertook SARS-CoV-2 WGS using an existing amplicon-based Illumina sequencing approach29,30.
    WGS
    suggested: None
    Amplicons were then pooled equally, purified and quantified before Nextera XT library preparation and multiplex sequencing on an Illumina iSeq or MiniSeq (150 cycle flow cell)31.
    MiniSeq
    suggested: None
    Demultiplexed reads were quality trimmed using Trimmomatic (sliding window of 4, minimum read quality score of 20, leading/trailing quality of 5)32.
    Trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Briefly, reads were mapped to the reference SARS-CoV-2 genome (NCBI GenBank accession MN908947) using BWA-mem version 0.7.17, with unmapped reads discarded.
    BWA-mem
    suggested: (Sniffles, RRID:SCR_017619)
    The quality of GISAID genomes was evaluated using QUAST, with sequences retained only if they were >28,000-bp in length and contained <0.05% missing bases (n=1,985 reference genomes).
    QUAST
    suggested: (QUAST, RRID:SCR_001228)
    The GISAID and NSW genomes were aligned with MAFFT v7.402 (FFT-NS-2, progressive method)35.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)

    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.
    • No funding statement was detected.
    • 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 10.1101/2020.04.19.048751v2 on bioRxiv
  3. Version published to 10.1101/2020.04.19.048751v1 on bioRxiv