Off-season RSV epidemics in Australia after easing of COVID-19 restrictions

  1. John-Sebastian Eden
  2. Chisha Sikazwe
  3. Ruopeng Xie
  4. Yi-Mo Deng
  5. Sheena G. Sullivan
  6. Alice Michie
  7. Avram Levy
  8. Elena Cutmore
  9. Christopher C. Blyth
  10. Philip N. Britton
  11. Nigel Crawford
  12. Xiaomin Dong
  13. Dominic E. Dwyer
  14. Kimberly M. Edwards
  15. Bethany A. Horsburgh
  16. David Foley
  17. Karina Kennedy
  18. Cara Minney-Smith
  19. David Speers
  20. Rachel L. Tulloch
  21. Edward C. Holmes
  22. Vijaykrishna Dhanasekaran
  23. David W. Smith
  24. Jen Kok
  25. Ian G. Barr
  26. the Australian RSV study group
  27. Annette Alafaci
  28. Ian Carter
  29. Andrew Daley
  30. Michelle Francis
  31. Alison Kesson
  32. Hannah Moore
  33. Christine Ngo
  34. Tyna Tran

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Abstract

Human respiratory syncytial virus (RSV) is an important cause of acute respiratory infection with the most severe disease in the young and elderly. Non-pharmaceutical interventions and travel restrictions for controlling COVID-19 have impacted the circulation of most respiratory viruses including RSV globally, particularly in Australia, where during 2020 the normal winter epidemics were notably absent. However, in late 2020, unprecedented widespread RSV outbreaks occurred, beginning in spring, and extending into summer across two widely separated regions of the Australian continent, New South Wales (NSW) and Australian Capital Territory (ACT) in the east, and Western Australia. Through genomic sequencing we reveal a major reduction in RSV genetic diversity following COVID-19 emergence with two genetically distinct RSV-A clades circulating cryptically, likely localised for several months prior to an epidemic surge in cases upon relaxation of COVID-19 control measures. The NSW/ACT clade subsequently spread to the neighbouring state of Victoria and to cause extensive outbreaks and hospitalisations in early 2021. These findings highlight the need for continued surveillance and sequencing of RSV and other respiratory viruses during and after the COVID-19 pandemic, as mitigation measures may disrupt seasonal patterns, causing larger or more severe outbreaks.

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  1. Version published to 10.1038/s41467-022-30485-3
  2. ScreenIT

    SciScore for 10.1101/2021.07.21.21260810: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: RSV subtyping and whole genome sequencing: Samples were sequenced from cases collected for routine diagnostic purposes as part of public health responses, and from on-going research studies approved by the local Human Research Ethics Committees of the Royal Children’s Hospital and Western Sydney Local Health District with approval numbers 37185 and LNR/17/WMEAD/128,
    Sex as a biological variablenot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    RSV whole genome sequencing (WGS) was conducted using established protocols24 for a subset of samples selected to provide temporal and geographical representation of i) the pre-COVID-19 period, inclusive of July 2017 to March 2020, and ii) the post-COVID-19 period, inclusive of April 2020 to March 2021.
    WGS
    suggested: None
    Multiplexed libraries were then sequenced either on an Illumina iSeq 100 or MiSeq producing at least 200,000 paired end reads (2×150nt) per library.
    MiSeq
    suggested: (A5-miseq, RRID:SCR_012148)
    For genome assembly, the sequence reads were QC trimmed using BBDuk v37.9831 before de novo assembly with MEGAHIT v1.1.332 or reference based assembly with IRMA33.
    BBDuk
    suggested: (Bestus Bioinformaticus Duk, RRID:SCR_016969)
    MEGAHIT
    suggested: (MEGAHIT, RRID:SCR_018551)
    To confirm assembly, the trimmed sequence reads were re-mapped onto the draft genome with BBMap v37.98 and visually assessed using the Geneious Prime v.
    BBMap
    suggested: (BBmap, RRID:SCR_016965)
    Multiple sequence alignments were performed independently with MAFFT v.
    MAFFT
    suggested: (MAFFT, RRID:SCR_011811)
    735 and examined using TempEst v.
    TempEst
    suggested: (TempEst, RRID:SCR_017304)
    1.536 to identify and exclude excessively divergent sequences in a preliminary maximum likelihood (ML) tree generated in FastTree v.2.137.
    FastTree
    suggested: (FastTree, RRID:SCR_015501)
    Phylogenetic relationships of the full-length alignments were inferred using the ML method in IQ-TREE v.
    IQ-TREE
    suggested: (IQ-TREE, RRID:SCR_017254)
    G gene phylogenies were estimated using RAxML v.
    RAxML
    suggested: (RAxML, RRID:SCR_006086)

    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.

    Results from scite Reference Check: We found no unreliable references.

    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 10.1101/2021.07.21.21260810v1 on medRxiv