Dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 in England

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Abstract

Rapid transmission of the SARS-CoV-2 Omicron variant has led to record-breaking case incidence rates around the world. Since May 2020, the REal-time Assessment of Community Transmission-1 (REACT-1) study tracked the spread of SARS-CoV-2 infection in England through RT-PCR of self-administered throat and nose swabs from randomly-selected participants aged 5 years and over. In January 2022, we found an overall weighted prevalence of 4.41% (n = 102,174), three-fold higher than in November to December 2021; we sequenced 2,374 (99.2%) Omicron infections (19 BA.2), and only 19 (0.79%) Delta, with a growth rate advantage for BA.2 compared to BA.1 or BA.1.1. Prevalence was decreasing overall (reproduction number R = 0.95, 95% credible interval [CrI], 0.93, 0.97), but increasing in children aged 5 to 17 years (R = 1.13, 95% CrI, 1.09, 1.18). In England during January 2022, we observed unprecedented levels of SARS-CoV-2 infection, especially among children, driven by almost complete replacement of Delta by Omicron.

Abstract

The REACT-1 study measures the community prevalence of SARS-CoV-2 in England through repeated cross-sectional surveys. Here, the authors present data from REACT-1 that document the increase in infection prevalence, particularly among children, associated with the Omicron variant in January 2022.

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Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study

Tommy Nyberg, Neil M Ferguson, Sophie Nash … (2022)

Background The omicron variant (B.1.1.529) of SARS-CoV-2 has demonstrated partial vaccine escape and high transmissibility, with early studies indicating lower severity of infection than that of the delta variant (B.1.617.2). We aimed to better characterise omicron severity relative to delta by assessing the relative risk of hospital attendance, hospital admission, or death in a large national cohort. Methods Individual-level data on laboratory-confirmed COVID-19 cases resident in England between Nov 29, 2021, and Jan 9, 2022, were linked to routine datasets on vaccination status, hospital attendance and admission, and mortality. The relative risk of hospital attendance or admission within 14 days, or death within 28 days after confirmed infection, was estimated using proportional hazards regression. Analyses were stratified by test date, 10-year age band, ethnicity, residential region, and vaccination status, and were further adjusted for sex, index of multiple deprivation decile, evidence of a previous infection, and year of age within each age band. A secondary analysis estimated variant-specific and vaccine-specific vaccine effectiveness and the intrinsic relative severity of omicron infection compared with delta (ie, the relative risk in unvaccinated cases). Findings The adjusted hazard ratio (HR) of hospital attendance (not necessarily resulting in admission) with omicron compared with delta was 0·56 (95% CI 0·54–0·58); for hospital admission and death, HR estimates were 0·41 (0·39–0·43) and 0·31 (0·26–0·37), respectively. Omicron versus delta HR estimates varied with age for all endpoints examined. The adjusted HR for hospital admission was 1·10 (0·85–1·42) in those younger than 10 years, decreasing to 0·25 (0·21–0·30) in 60–69-year-olds, and then increasing to 0·47 (0·40–0·56) in those aged at least 80 years. For both variants, past infection gave some protection against death both in vaccinated (HR 0·47 [0·32–0·68]) and unvaccinated (0·18 [0·06–0·57]) cases. In vaccinated cases, past infection offered no additional protection against hospital admission beyond that provided by vaccination (HR 0·96 [0·88–1·04]); however, for unvaccinated cases, past infection gave moderate protection (HR 0·55 [0·48–0·63]). Omicron versus delta HR estimates were lower for hospital admission (0·30 [0·28–0·32]) in unvaccinated cases than the corresponding HR estimated for all cases in the primary analysis. Booster vaccination with an mRNA vaccine was highly protective against hospitalisation and death in omicron cases (HR for hospital admission 8–11 weeks post-booster vs unvaccinated: 0·22 [0·20–0·24]), with the protection afforded after a booster not being affected by the vaccine used for doses 1 and 2. Interpretation The risk of severe outcomes following SARS-CoV-2 infection is substantially lower for omicron than for delta, with higher reductions for more severe endpoints and significant variation with age. Underlying the observed risks is a larger reduction in intrinsic severity (in unvaccinated individuals) counterbalanced by a reduction in vaccine effectiveness. Documented previous SARS-CoV-2 infection offered some protection against hospitalisation and high protection against death in unvaccinated individuals, but only offered additional protection in vaccinated individuals for the death endpoint. Booster vaccination with mRNA vaccines maintains over 70% protection against hospitalisation and death in breakthrough confirmed omicron infections. Funding Medical Research Council, UK Research and Innovation, Department of Health and Social Care, National Institute for Health Research, Community Jameel, and Engineering and Physical Sciences Research Council.

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Plasma Neutralization of the SARS-CoV-2 Omicron Variant

Fabian Schmidt, Frauke Muecksch, Yiska Weisblum … (2021)

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The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

Matthew D. Hoffman, Andrew Gelman (2011)

Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) algorithm that avoids the random walk behavior and sensitivity to correlated parameters that plague many MCMC methods by taking a series of steps informed by first-order gradient information. These features allow it to converge to high-dimensional target distributions much more quickly than simpler methods such as random walk Metropolis or Gibbs sampling. However, HMC's performance is highly sensitive to two user-specified parameters: a step size ε and a desired number of steps L. In particular, if L is too small then the algorithm exhibits undesirable random walk behavior, while if L is too large the algorithm wastes computation. We introduce the No-U-Turn Sampler (NUTS), an extension to HMC that eliminates the need to set a number of steps L. NUTS uses a recursive algorithm to build a set of likely candidate points that spans a wide swath of the target distribution, stopping automatically when it starts to double back and retrace its steps. Empirically, NUTS perform at least as efficiently as and sometimes more efficiently than a well tuned standard HMC method, without requiring user intervention or costly tuning runs. We also derive a method for adapting the step size parameter ε on the fly based on primal-dual averaging. NUTS can thus be used with no hand-tuning at all. NUTS is also suitable for applications such as BUGS-style automatic inference engines that require efficient "turnkey" sampling algorithms. 30 pages, 7 figures

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Author and article information

Contributors

Paul Elliott:

ORCID: http://orcid.org/0000-0002-7511-5684

p.elliott@imperial.ac.uk

Marc Chadeau-Hyam:

ORCID: http://orcid.org/0000-0001-8341-5436

m.chadeau@imperial.ac.uk

Christl A. Donnelly:

ORCID: http://orcid.org/0000-0002-0195-2463

c.donnelly@imperial.ac.uk

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 3 August 2022

Publication date PMC-release: 3 August 2022

Publication date Collection: 2022

Volume: 13

Electronic Location Identifier: 4500

Affiliations

[1 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, School of Public Health, Imperial College London, ; London, UK

[2 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, MRC Centre for Environment and Health, School of Public Health, Imperial College London, ; London, UK

[3 ]GRID grid.417895.6, ISNI 0000 0001 0693 2181, Imperial College Healthcare NHS Trust, ; London, UK

[4 ]GRID grid.451056.3, ISNI 0000 0001 2116 3923, National Institute for Health Research Imperial Biomedical Research Centre, ; London, UK

[5 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Health Data Research (HDR) UK, Imperial College London, ; London, UK

[6 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, UK Dementia Research Institute, Imperial College London, ; London, UK

[7 ]GRID grid.14105.31, ISNI 0000000122478951, MRC Centre for Global infectious Disease Analysis, ; London, UK

[8 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Jameel Institute, , Imperial College London, ; London, UK

[9 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, National Heart and Lung Institute, Imperial College Healthcare NHS Trust, ; London, UK

[10 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Department of Infectious Disease, , Imperial College London, ; London, UK

[11 ]GRID grid.9835.7, ISNI 0000 0000 8190 6402, CHICAS, Lancaster Medical School, , Lancaster University, UK and Health Data Research, ; Lancaster, UK

[12 ]GRID grid.40368.39, ISNI 0000 0000 9347 0159, Quadram Institute, ; Norwich, UK

[13 ]GRID grid.7445.2, ISNI 0000 0001 2113 8111, Institute of Global Health Innovation, Imperial College London, ; London, UK

[14 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Statistics, , University of Oxford, ; Oxford, UK

Author information

Paul Elliott http://orcid.org/0000-0002-7511-5684

Barbara Bodinier http://orcid.org/0000-0002-0781-3624

David Tang http://orcid.org/0000-0002-6974-5663

Matthew Whitaker http://orcid.org/0000-0003-1363-6537

Christina Atchison http://orcid.org/0000-0001-8304-7389

Andrew J. Page http://orcid.org/0000-0001-6919-6062

Deborah Ashby http://orcid.org/0000-0003-3146-7466

Wendy Barclay http://orcid.org/0000-0002-3948-0895

Graham Taylor http://orcid.org/0000-0002-3773-2390

Helen Ward http://orcid.org/0000-0001-8238-5036

Ara Darzi http://orcid.org/0000-0001-7815-7989

Graham S. Cooke http://orcid.org/0000-0001-6475-5056

Marc Chadeau-Hyam http://orcid.org/0000-0001-8341-5436

Christl A. Donnelly http://orcid.org/0000-0002-0195-2463

Article

Publisher ID: 32121

DOI: 10.1038/s41467-022-32121-6

PMC ID: 9349208

PubMed ID: 35922409

SO-VID: 42ff170a-6323-440c-b270-d87244768112

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 19 February 2022

Date accepted : 18 July 2022

Funding

Funded by: Department of Health and Social Care in England

Funded by: FundRef https://doi.org/10.13039/501100000289, Cancer Research UK (CRUK);

Award ID: 22184

Award Recipient : Marc Chadeau-Hyam

Funded by: FundRef https://doi.org/10.13039/100010661, EC | Horizon 2020 Framework Programme (EU Framework Programme for Research and Innovation H2020);

Award ID: 874627

Award ID: 874739

Award Recipient : Marc Chadeau-Hyam

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ScienceOpen disciplines: Uncategorized

Keywords: viral epidemiology,epidemiology,sars-cov-2

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ScienceOpen disciplines: Uncategorized

Keywords: viral epidemiology, epidemiology, sars-cov-2

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Dynamics of a national Omicron SARS-CoV-2 epidemic during January 2022 in England

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Most cited references 7

Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study

Plasma Neutralization of the SARS-CoV-2 Omicron Variant

The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo

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