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Modelling the medium-term dynamics of SARS-CoV-2 transmission in England in the Omicron era

Author

Listed:
  • Rosanna C. Barnard

    (London School of Hygiene & Tropical Medicine
    London School of Hygiene & Tropical Medicine)

  • Nicholas G. Davies

    (London School of Hygiene & Tropical Medicine
    London School of Hygiene & Tropical Medicine)

  • Mark Jit

    (London School of Hygiene & Tropical Medicine
    London School of Hygiene & Tropical Medicine)

  • W. John Edmunds

    (London School of Hygiene & Tropical Medicine
    London School of Hygiene & Tropical Medicine)

Abstract

England has experienced a heavy burden of COVID-19, with multiple waves of SARS-CoV-2 transmission since early 2020 and high infection levels following the emergence and spread of Omicron variants since late 2021. In response to rising Omicron cases, booster vaccinations were accelerated and offered to all adults in England. Using a model fitted to more than 2 years of epidemiological data, we project potential dynamics of SARS-CoV-2 infections, hospital admissions and deaths in England to December 2022. We consider key uncertainties including future behavioural change and waning immunity and assess the effectiveness of booster vaccinations in mitigating SARS-CoV-2 disease burden between October 2021 and December 2022. If no new variants emerge, SARS-CoV-2 transmission is expected to decline, with low levels remaining in the coming months. The extent to which projected SARS-CoV-2 transmission resurges later in 2022 depends largely on assumptions around waning immunity and to some extent, behaviour, and seasonality.

Suggested Citation

  • Rosanna C. Barnard & Nicholas G. Davies & Mark Jit & W. John Edmunds, 2022. "Modelling the medium-term dynamics of SARS-CoV-2 transmission in England in the Omicron era," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-32404-y
    DOI: 10.1038/s41467-022-32404-y
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    References listed on IDEAS

    as
    1. Hyndman, Rob J. & Khandakar, Yeasmin, 2008. "Automatic Time Series Forecasting: The forecast Package for R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i03).
    2. Nicholas G. Davies & Christopher I. Jarvis & W. John Edmunds & Nicholas P. Jewell & Karla Diaz-Ordaz & Ruth H. Keogh, 2021. "Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7," Nature, Nature, vol. 593(7858), pages 270-274, May.
    3. Joël Mossong & Niel Hens & Mark Jit & Philippe Beutels & Kari Auranen & Rafael Mikolajczyk & Marco Massari & Stefania Salmaso & Gianpaolo Scalia Tomba & Jacco Wallinga & Janneke Heijne & Malgorzata Sa, 2008. "Social Contacts and Mixing Patterns Relevant to the Spread of Infectious Diseases," PLOS Medicine, Public Library of Science, vol. 5(3), pages 1-1, March.
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    Cited by:

    1. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    2. Chen, Jiaqi & Gu, Changgui & Ruan, Zhongyuan & Tang, Ming, 2023. "Competition of SARS-CoV-2 variants on the pandemic transmission dynamics," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
    3. Lloyd A. C. Chapman & Maite Aubry & Noémie Maset & Timothy W. Russell & Edward S. Knock & John A. Lees & Henri-Pierre Mallet & Van-Mai Cao-Lormeau & Adam J. Kucharski, 2023. "Impact of vaccinations, boosters and lockdowns on COVID-19 waves in French Polynesia," Nature Communications, Nature, vol. 14(1), pages 1-16, December.

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