IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-28291-y.html
   My bibliography  Save this article

Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves

Author

Listed:
  • Benjamin B. Lindsey

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Teaching Hospitals NHS Foundation Trust)

  • Ch. Julián Villabona-Arenas

    (Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
    London School of Hygiene and Tropical Medicine)

  • Finlay Campbell

    (Health Emergencies Programme, World Health Organization)

  • Alexander J. Keeley

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Teaching Hospitals NHS Foundation Trust)

  • Matthew D. Parker

    (Sheffield Biomedical Research Centre, The University of Sheffield
    Sheffield Bioinformatics Core, The University of Sheffield
    The University of Sheffield)

  • Dhruv R. Shah

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield)

  • Helena Parsons

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Teaching Hospitals NHS Foundation Trust)

  • Peijun Zhang

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield)

  • Nishchay Kakkar

    (Sheffield Teaching Hospitals NHS Foundation Trust)

  • Marta Gallis

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield)

  • Benjamin H. Foulkes

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield)

  • Paige Wolverson

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield)

  • Stavroula F. Louka

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield)

  • Stella Christou

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield)

  • Amy State

    (Sheffield Teaching Hospitals NHS Foundation Trust)

  • Katie Johnson

    (Sheffield Teaching Hospitals NHS Foundation Trust)

  • Mohammad Raza

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Teaching Hospitals NHS Foundation Trust)

  • Sharon Hsu

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Bioinformatics Core, The University of Sheffield)

  • Thibaut Jombart

    (Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
    London School of Hygiene and Tropical Medicine
    School of Public Health, Imperial College London)

  • Anne Cori

    (School of Public Health, Imperial College London)

  • Cariad M. Evans

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Teaching Hospitals NHS Foundation Trust)

  • David G. Partridge

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Teaching Hospitals NHS Foundation Trust)

  • Katherine E. Atkins

    (Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
    London School of Hygiene and Tropical Medicine
    Usher Institute, The University of Edinburgh)

  • Stéphane Hué

    (Centre for Mathematical Modelling of Infectious Diseases, London School of Hygiene and Tropical Medicine
    London School of Hygiene and Tropical Medicine)

  • Thushan I. Silva

    (Immunity and Cardiovascular Disease, Medical School, University of Sheffield
    Sheffield Teaching Hospitals NHS Foundation Trust
    MRC Unit The Gambia at the London School of Hygiene and Tropical Medicine)

Abstract

Hospital outbreaks of COVID19 result in considerable mortality and disruption to healthcare services and yet little is known about transmission within this setting. We characterise within hospital transmission by combining viral genomic and epidemiological data using Bayesian modelling amongst 2181 patients and healthcare workers from a large UK NHS Trust. Transmission events were compared between Wave 1 (1st March to 25th July 2020) and Wave 2 (30th November 2020 to 24th January 2021). We show that staff-to-staff transmissions reduced from 31.6% to 12.9% of all infections. Patient-to-patient transmissions increased from 27.1% to 52.1%. 40%-50% of hospital-onset patient cases resulted in onward transmission compared to 4% of community-acquired cases. Control measures introduced during the pandemic likely reduced transmissions between healthcare workers but were insufficient to prevent increasing numbers of patient-to-patient transmissions. As hospital-acquired cases drive most onward transmission, earlier identification of nosocomial cases will be required to break hospital transmission chains.

Suggested Citation

  • Benjamin B. Lindsey & Ch. Julián Villabona-Arenas & Finlay Campbell & Alexander J. Keeley & Matthew D. Parker & Dhruv R. Shah & Helena Parsons & Peijun Zhang & Nishchay Kakkar & Marta Gallis & Benjami, 2022. "Characterising within-hospital SARS-CoV-2 transmission events using epidemiological and viral genomic data across two pandemic waves," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28291-y
    DOI: 10.1038/s41467-022-28291-y
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-28291-y
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-28291-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Finlay Campbell & Anne Cori & Neil Ferguson & Thibaut Jombart, 2019. "Bayesian inference of transmission chains using timing of symptoms, pathogen genomes and contact data," PLOS Computational Biology, Public Library of Science, vol. 15(3), pages 1-20, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joshua L. Warren & Melanie H. Chitwood & Benjamin Sobkowiak & Caroline Colijn & Ted Cohen, 2023. "Spatial modeling of Mycobacterium tuberculosis transmission with dyadic genetic relatedness data," Biometrics, The International Biometric Society, vol. 79(4), pages 3650-3663, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-28291-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.