Cited by 68COVID-19 epidemiological studiesSARS-CoV-2 and COVID-19 ResearchMathematical and Theoretical Epidemiology and Ecology Models

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

Open access
Barnard, Rosanna C., Davies, Nicholas G., Centre for Mathematical Modelling of Infectious Diseases COVID-19 working group, Munday, James D., Lowe, Rachel, Knight, Gwenan M., Leclerc, Quentin J., Tully, Damien C. +53 more·2022·Nature Communications

Published version of a preprint

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41467-022-32404-y

Cites

9 of 9 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Under a sensitivity analysis assuming naturally acquired immunity decayed to 85% in 365 days, population immunity by 31 July 2022 was 48% (42-54%).

    “Sensitivity analyses assuming exponentially decaying immunity from natural infection yielded population immunity of 48% (42–54%) or 36% (31–41%) if such naturally acquired immunity decayed to 85% in 365 days15 or to 75% in 100 days16,17, respectively”

    From Real-world COVID-19 vaccine effectiveness against the Omicron BA.2 variant in a SARS-CoV-2 infection-naive population (2023) · cites this paper as [15]

    The Waning immunity and Model assumptions sections describe scenarios in which immunity from prior infection wanes, but do not give an assumption of 85% remaining after 365 days. Searches for that assumption and for the 31 July 2022 population-immunity estimate found no passage reporting 48% (42–54%). Page numbers were not available in our copy. (LLM, conf=0.74)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 13

  1. Barnard, R. rosannaclairebarnard/covidm-mtd-Omi: First release associated with preprint. https://doi.org/10.5281/zenodo.6806637. https://zenodo.org/record/6806637 (2022). · DOI
  2. Teslya, A. et al. Impact of self-imposed prevention measures and short-term government-imposed social distancing on mitigating and delaying a COVID-19 epidemic: A modelling study. PLoS Med 17, e1003166 (2020).
  3. Weitz, J. S., Park, S. W., Eksin, C. & Dushoff, J. Awareness-driven behavior changes can shift the shape of epidemics away from peaks and toward plateaus, shoulders, and oscillations. Proc. Natl Acad. Sci. U. S. A. 117, 32764-32771 (2020).
  4. Lyngse, F. P. et al. Transmission of SARS-CoV-2 Omicron VOC subvariants BA.1 and BA.2: Evidence from Danish Households. medRxiv 2022.01.28.22270044 (2022).
  5. Stegger, M. et al. Occurrence and significance of Omicron BA.1 infection followed by BA.2 reinfection. medRxiv 2022.02.19.22271112 (2022).
  6. Barnard, R. C., Davies, N. G., Pearson, C. A. B., Jit, M. & John Edmunds, W. Projected epidemiological consequences of the Omicron SARS-CoV-2 variant in England, December 2021 to April 2022. medRxiv 2021.12.15.21267858 (2021).
  7. Ito, K., Piantham, C. & Nishiura, H. Estimating relative generation times and relative reproduction numbers of Omicron BA.1 and BA.2 with respect to Delta in Denmark. medRxiv 2022.03.02.22271767 (2022).
  8. Yamasoba, D. et al. Virological characteristics of SARS-CoV-2 BA.2 variant. bioRxiv 2022.02.14.480335 https://doi.org/10.1101/2022. 02.14.480335 (2022). · DOI
  9. Lentini, A., Pereira, A., Winqvist, O. & Reinius, B. Monitoring of the SARS-CoV-2 Omicron BA.1/BA.2 variant transition in the Swedish population reveals higher viral quantity in BA.2 cases. medRxiv 2022.03.26.22272984 (2022).
  10. Kirsebom, F. C. M. et al. COVID-19 Vaccine Effectiveness against the Omicron BA.2 variant in England. medRxiv 2022.03.22.22272691 (2022).
  11. Levin, A. T. et al. Assessing the Age Specificity of Infection Fatality Rates for COVID-19: Systematic Review, Meta-Analysis, and Public Policy Implications. medRxiv 2020.07.23.20160895 (2020).
  12. Endo, A., van Leeuwen, E. & Baguelin, M. Introduction to particle Markov-chain Monte Carlo for disease dynamics modellers. Epi- demics 29, 100363 (2019).
  13. James D. Munday 1 , Rachel Lowe 1 , Gwenan M. Knight 1 , Quentin J. Leclerc 1 , Damien C. Tully 1 , David Hodgson 1 , Rachael Pung 1 , Joel Hellewell 1 , Mihaly Koltai 1 , David Simons 1 , Kaja Abbas 1 , Adam J. Kucharski 1 , Simon R. Procter 1 , Frank G. Sandmann 1 , Carl A. B. Pearson 1 , Kiesha Prem 1 , Alicia Showering 1 , Sophie R. Meakin 1 , Kathleen O'Reilly 1 , Ciara V. McCarthy 1 , Matthew Quaife 1 , Kerry L. M. Wong 1 , Yalda Jafari 1 , Arminder K. Deol 1 , Rein M. G. J. Houben 1 , Charlie Diamond 1 , Thibaut Jombart 1 , C. Julian Villabona-Arenas 1 , William Waites 1 , Rosalind M. Eggo 1 , Akira Endo 1 , Hamish P. Gibbs 1 , Petra Klepac 1 , Jack Williams 1 , Billy J. Quilty 1 , Oliver Brady 1 , Jon C. Emery 1 , Katherine E. Atkins 1 , Lloyd A. C. Chapman 1 , Katharine Sherratt 1 , Sam Abbott 1 , Nikos I. Bosse 1 , Paul Mee 1 , Sebastian Funk 1 , Jiayao Lei 1 , Yang Liu 1 , Stefan Flasche 1 , James W. Rudge 1 , Fiona Yueqian Sun 1 , Graham Medley 1 , Timothy W. Russell 1 , Amy Gimma 1 , Stéphane Hué 1 , Christopher I. Jarvis 1 , Emilie Finch 1 & Samuel Clifford 1

Cited by

Sorted by citations

Authors
Barnard, R. C., Davies, N. G., Centre for Mathematical Modelling of Infectious Diseases COVID-19 working group, Munday, J. D., Lowe, R., Knight, G. M., Leclerc, Q. J., Tully, D. C., Hodgson, D., Pung, R., Hellewell, J., Koltai, M., Simons, D., Abbas, K., Kucharski, A. J., Procter, S. R., Sandmann, F. G., Pearson, C. A. B., Prem, K., Showering, A., Meakin, S. R., O’Reilly, K., McCarthy, C. V., Quaife, M., Wong, K. L. M., Jafari, Y., Deol, A. K., Houben, R. M. G. J., Diamond, C., Jombart, T., Villabona-Arenas, C. J., Waites, W., Eggo, R. M., Endo, A., Gibbs, H. P., Klepac, P., Williams, J., Quilty, B. J., Brady, O., Emery, J. C., Atkins, K. E., Chapman, L. A. C., Sherratt, K., Abbott, S., Bosse, N. I., Mee, P., Funk, S., Lei, J., Liu, Y., Flasche, S., Rudge, J. W., Sun, F. Y., Medley, G., Russell, T. W., Gimma, A., Hué, S., Jarvis, C. I., Finch, E., Clifford, S., Jit, M., & Edmunds, W. J.
Affiliations
London School of Hygiene & Tropical Medicine (GB)
Corresponding author
Barnard, Rosanna C.
Venue
Nature Communications
Published
2022-08-19
Volume
13
Issue
1
Pages
4879-4879
ISSN
2041-1723 (ISSN-L)
OpenAlex
W4292315763
Cited by
68
References
9
Citations by year
2026: 8 · 2025: 7 · 2024: 20 · 2023: 24 · 2022: 8 · 2021: 1
FWCI
8.74
Type
journal article
Language
en
Open access
Locations
  • Nature Communications (publishedVersion, cc-by)
  • medRxiv (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • ePrints Soton (University of Southampton) (acceptedVersion)
  • Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (acceptedVersion)
  • LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
  • PubMed Central (submittedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Lancaster EPrints (Lancaster University) (submittedVersion)
Funders
  • European Commission (101003688)
  • RCUK | Medical Research Council (MC_PC_19065)
  • DH | National Institute for Health Research (NIHR200929, NIHR200908, PR-OD-1017-20002)
Topics
  • Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
  • Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
  • Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Mathematical and Theoretical Epidemiology and Ecology Models
Keywords
SARS-CoV-2 transmission, Omicron variant, COVID-19 pandemic, booster vaccination, waning immunity, hospitalization, COVID-19 mortality, behavior change, seasonal variation, England

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review