Genomic epidemiology reveals transmission patterns and dynamics of SARS-CoV-2 in Aotearoa New Zealand

  1. Jemma L. Geoghegan
  2. Xiaoyun Ren
  3. Matthew Storey
  4. James Hadfield
  5. Lauren Jelley
  6. Sarah Jefferies
  7. Jill Sherwood
  8. Shevaun Paine
  9. Sue Huang
  10. Jordan Douglas
  11. Fábio K. Mendes
  12. Andrew Sporle
  13. Michael G. Baker
  14. David R. Murdoch
  15. Nigel French
  16. Colin R. Simpson
  17. David Welch
  18. Alexei J. Drummond
  19. Edward C. Holmes
  20. Sebastián Duchêne
  21. Joep de Ligt

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

New Zealand, a geographically remote Pacific island with easily sealable borders, implemented a nationwide ‘lockdown’ of all non-essential services to curb the spread of COVID-19. Here, we generate 649 SARS-CoV-2 genome sequences from infected patients in New Zealand with samples collected during the ‘first wave’, representing 56% of all confirmed cases in this time period. Despite its remoteness, the viruses imported into New Zealand represented nearly all of the genomic diversity sequenced from the global virus population. These data helped to quantify the effectiveness of public health interventions. For example, the effective reproductive number, R e of New Zealand’s largest cluster decreased from 7 to 0.2 within the first week of lockdown. Similarly, only 19% of virus introductions into New Zealand resulted in ongoing transmission of more than one additional case. Overall, these results demonstrate the utility of genomic pathogen surveillance to inform public health and disease mitigation.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.08.05.20168930: (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
    For the NSW primer set, raw reads were quality and adapter trimmed using trimmomatic (v 0.36)20.
    trimmomatic
    suggested: (Trimmomatic, RRID:SCR_011848)
    Duplicated reads were marked using Picard (v 2.10.10)23 and not used for SNP calling or depth calculation.
    Picard
    suggested: (Picard, RRID:SCR_006525)
    In total, 649 sequences passed our quality control (BioProject: PRJNA648792; a list of genomes and their sequencing methods are provided in Supplementary Table 1).
    BioProject
    suggested: (NCBI BioProject, RRID:SCR_004801)
    A maximum likelihood phylogenetic tree was estimated using IQ-TREE (v 1.6.8)28, utilising the Hasegawa-Kishino-Yano (HKY+r)29 nucleotide substitution model with a gamma distributed rate variation among sites (the best fit model was determined by ModelFinder30), and branch support assessment using the ultrafast bootstrap method31.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    We regressed root-to-tip genetic divergence against sampling dates to investigate the evolutionary tempo of our SARS-CoV-2 samples using TempEst (v 1.5.3)32.
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    With the full set of New Zealand sequences, we used a time-aware coalescent Bayesian exponential growth model available in BEAST (v 1.10.4)34.
    BEAST
    suggested: (BEAST, RRID:SCR_010228)

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

    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: Please consider improving the rainbow (“jet”) colormap(s) used on page 6. At least one figure is not accessible to readers with colorblindness and/or is not true to the data, i.e. not perceptually uniform.

    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 10.1038/s41467-020-20235-8
  3. Version published to 10.1101/2020.08.05.20168930v3 on medRxiv
  4. Version published to 10.1101/2020.08.05.20168930v2 on medRxiv
  5. Version published to 10.1101/2020.08.05.20168930v1 on medRxiv