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Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England
Open accessPreprint — published as 10.1126/science.abg3055 · Paper page
Abstract
A novel SARS-CoV-2 variant, VOC 202012/01 (lineage B.1.1.7), emerged in southeast England in November 2020 and is rapidly spreading towards fixation. Using a variety of statistical and dynamic modelling approaches, we estimate that this variant has a 43–90% (range of 95% credible intervals 38–130%) higher reproduction number than preexisting variants. A fitted two-strain dynamic transmission model shows that VOC 202012/01 will lead to large resurgences of COVID-19 cases. Without stringent control measures, including limited closure of educational institutions and a greatly accelerated vaccine roll-out, COVID-19 hospitalisations and deaths across England in 2021 will exceed those in 2020. Concerningly, VOC 202012/01 has spread globally and exhibits a similar transmission increase (59–74%) in Denmark, Switzerland, and the United States.
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![Fig. 2 Measuring the growth rate of VOC 202012/01. (A) Average relativized growth rate (i.e., a measure of variant fitness relative to other variants present during the 31 days after initial phylogenetic observation of a given variant) for all lineages in the COG-UK dataset, highlighting many lineages that have risen to prominence including B.1.177, the lineage with the highest relative abundance during the initial phylogenetic observation of VOC 202012/01. The shaded regions show conservative 95% rejection intervals; VOC 202012/01 is the first strain to exceed this threshold of faster relativized growth. Although many lineages exhibit above-average rates of growth, VOC 202012/01 has had the highest average relativized growth of any lineage in the history of COG-UK surveillance of SARS-COV-2. (B) Plotting all lineages’ relativized growth rates [ρ(t)] as a function of lineage age with conservative 95% rejection intervals highlights the significantly faster growth of VOC 202012/01 relative to other lineages at comparable times since their initial observation. Later declines in VOC and B.1.177 correspond to highly uncertain estimates of growth rates for data that are yet to be backfilled, so these declines in ρ(t) are sensitive to the processing of future sequences from recent dates (fig. S1). (C) Muller plots of the relative abundances of the major SARS-CoV-2 variants in the UK, based on a multinomial spline fit to COG-UK sequence data (Table 1 and table S1, separate-slopes multinomial spline model). A model extrapolation until 1 March 2021 is shown (shaded area). Minority variants are 440 circulating SARS-CoV-2 variants of low abundance. Specific colors represent the same lineages in (A) to (C). (D) Mean reproduction number over 7-day periods in 149 upper-tier local authorities of England (colored by the NHS England region they are within) plotted against the weekly proportion of Pillar 2 community SARS-CoV-2 tests with S gene target failure shows the spread of VOC 202012/01, a corresponding increase in the reproduction number in each local authority, and the eventual impact of targeted government restrictions from 20 December 2020. Testing data are shown for the week after the reproduction number estimates to account for delays from infection to test.](/paper-figures/cf53abb2f4d446fabf4a1a5bb2dee3b8/estimated-transmissibility-and-impact-of-sars-cov-2-lineage-b-1-1-7-in-england-figure-2.jpg)
![Fig. 3 Comparison of possible biological mechanisms underlying the rapid spread of VOC 202012/01. Each row shows a different assumed mechanism. (A) Relative frequency of VOC 202012/01 (black line and ribbon respectively denote observed S gene target failure frequency with 95% binomial credible interval; purple line and ribbon respectively denote mean and 95% credible interval from model fit). (B) Posterior estimates (mean and 95% credible intervals) for relative odds of hospitalization (severe illness), relative odds of ICU admission (critical illness), relative odds of death (fatal illness), growth rate as a multiplicative factor per week [i.e., exp(7·∆r)], and the parameter that defines the hypothesized mechanism; all parameters are relative to those estimated for preexisting variants. (C to E) Illustrative model fits for the South East NHS England region: (C) fitted two-strain increased transmissibility model with VOC 202012/01 included; (D) fitted two-strain increased transmissibility model with VOC 202012/01 removed; (E) fitted single-strain model without emergence of VOC 202012/01. Black lines denote observed data; error bars denote the date range and 95% credible intervals for observed PCR prevalence and seroprevalence; colored lines and ribbons denote median and 95% credible intervals from model fit.](/paper-figures/fbd04fcd77b647dbab9d4a5ad6b12716/estimated-transmissibility-and-impact-of-sars-cov-2-lineage-b-1-1-7-in-england-figure-3.jpg)

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“In addition, similar results have been reported by others using vaccinee and convalescent plasmas and a variety of different pseudotype and authentic virus assays15,31,45–49.”
From mRNA vaccine-elicited antibodies to SARS-CoV-2 and circulating variants (2021) · cites this paper as [15]
Experimental — accuracy not yet measuredIn the citing paper
“They are frequently sensitive to mutation in these residues and select for K417N, E484K and N501Y mutations in SARS-CoV-2 S protein expression libraries in yeast and VSV 12,15,33.”
From mRNA vaccine-elicited antibodies to SARS-CoV-2 and circulating variants (2021) · cites this paper as [15]
In the citing paper
“Additionally, K417N, Y453F, S477R, N501Y, and D614G represent circulating variants some of which have been associated with rapidly increasing case numbers14,15,19,33–35.”
From mRNA vaccine-elicited antibodies to SARS-CoV-2 and circulating variants (2021) · cites this paper as [15]
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Chen, Y., Zhu, L., Huang, W., Tong, X., Wu, H., Tao, Y., Tong, B., Huang, H., Chen, J., Zhao, X., et al. (2021) · Emerging Microbes & Infections · doi:10.1080/22221751.2021.1942227 · Cited by 20
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Application of omics technology to combat the COVID‐19 pandemic
Yang, J., Yan, Y., & Zhong, W. (2021) · MedComm · doi:10.1002/mco2.90 · Cited by 20
Sideward contact tracing and the control of epidemics in large gatherings
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A metagenomics workflow for SARS-CoV-2 identification, co-pathogen detection, and overall diversity
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Drug repurposing strategies and key challenges for COVID-19 management
Mule, S., Singh, A., Greish, K. F., Sahebkar, A., Kesharwani, P., & Shukla, R. (2021) · Journal of drug targeting · doi:10.1080/1061186x.2021.2013852 · Cited by 22
Joffe, A. R., & Redman, D. (2021) · Frontiers in Public Health · doi:10.3389/fpubh.2021.715904 · Cited by 20
Wang, Y., Zhang, F., Byrd, J. B., Yu, H. J., Ye, X., & He, Y. O. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.770031 · Cited by 21
Organoid Models of SARS-CoV-2 Infection: What Have We Learned about COVID-19?
Tran, B. M., Deliyannis, G., Hachani, A., Earnest, L., Torresi, J., & Vincan, E. (2022) · Organoids · doi:10.3390/organoids1010002 · Cited by 21
Molecular variants of SARS-CoV-2: antigenic properties and current vaccine efficacy
Damavandi, A. R., Dowran, R., Sharif, S. A., Kashanchi, F., & Jafari, R. (2022) · Medical Microbiology and Immunology · doi:10.1007/s00430-022-00729-6 · Cited by 21
Cavani, M., Riofrío, W., & Arciniega, M. (2022) · Molecules · doi:10.3390/molecules27072370 · Cited by 23
Detection of SARS-CoV-2 in exhaled breath from non-hospitalized COVID-19-infected individuals
Leding, C., Skov, J., Uhrbrand, K., Lisby, J. G., Hansen, K. P., Benfield, T., & Duncan, L. K. (2022) · Scientific Reports · doi:10.1038/s41598-022-15243-1 · Cited by 21
Collin, A., Hejblum, B. P., Vignals, C., Lehot, L., Thiébaut, R., Moireau, P., & Prague, M. (2023) · The International Journal of Biostatistics · doi:10.1515/ijb-2022-0087 · Cited by 30
Ali, M. A., Zhang, F., Hu, C., Yuan, B., Gao, S., & Panat, R. P. (2024) · Advanced Materials Interfaces · doi:10.1002/admi.202400005 · Cited by 21
Novel human neutralizing mAbs specific for Spike-RBD of SARS-CoV-2
Passariello, M., Gentile, C., Ferrucci, V., Sasso, E., Vetrei, C., Fusco, G., Viscardi, M., Brandi, S., Cerino, P., Zambrano, N., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-90348-7 · Cited by 21
Chandrashekar, A., Liu, J., Yu, J., McMahan, K., Tostanoski, L. H., Jacob-Dolan, C., Mercado, N. B., Anioke, T., Chang, A., Gardner, S., et al. (2021) · Science Translational Medicine · doi:10.1126/scitranslmed.abj2641 · Cited by 22
Assessing the risk of vaccine-driven virulence evolution in SARS-CoV-2
Miller, I. F., & Metcalf, C. J. E. (2022) · Royal Society Open Science · doi:10.1098/rsos.211021 · Cited by 22
A tale of three SARS-CoV-2 variants with independently acquired P681H mutations in New York StatePreprint
Lasek‐Nesselquist, E., Pata, J. D., Schneider, E., & George, K. S. (2021) · medRxiv · doi:10.1101/2021.03.10.21253285 · Cited by 22
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Tortorici, M. A., Czudnochowski, N., Starr, T. N., Marzi, R., Walls, A. C., Zatta, F., Bowen, J. E., Jaconi, S., di Iulio, J., Wang, Z., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.07.438818 · Cited by 22
Starr, T. N., Czudnochowski, N., Zatta, F., Park, Y., Liu, Z., Addetia, A., Pinto, D., Beltramello, M., Hernandez, P., Greaney, A. J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.06.438709 · Cited by 24
Effect of SARS-CoV-2 spike mutations on animal ACE2 usage and in vitro neutralization sensitivityPreprint
Yao, W., Ma, D., Wang, H., Tang, X., Du, C., Pan, H., Li, C., Lin, H., Farzan, M., Zhao, J., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.01.27.428353 · Cited by 21
Njenga, L. W., Mbugua, S. N., Odhiambo, R. A., & Onani, M. O. (2023) · Dalton Transactions · doi:10.1039/d3dt00366c · Cited by 20
Català, M., Li, X., Prats, C., & Prieto‐Alhambra, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-98216-0 · Cited by 19
Appropriate names for COVID-19 variants
Karim, S. S. A., de Oliveira, T., & Loots, G. (2021) · Science · doi:10.1126/science.abh0836 · Cited by 20
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González‐Parra, G., & Arenas, A. J. (2021) · Computational and Applied Mathematics · doi:10.1007/s40314-021-01592-6 · Cited by 20
The Evolving Faces of the SARS-CoV-2 Genome
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EpiBeds: Data informed modelling of the COVID-19 hospital burden in England
Overton, C. E., Pellis, L., Stage, H. B., Scarabel, F., Burton, J., Fraser, C., Hall, I., House, T., Jewell, C., Nurtay, A., et al. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010406 · Cited by 18
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The danger of the single storyline obfuscating the complexities of managing SARS‐CoV‐2/COVID‐19
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Antibody cocktail effective against variants of SARS-CoV-2
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Metadata
- Authors
- Davies, N. G., Abbott, S., Barnard, R. C., Jarvis, C. I., Kucharski, A. J., Munday, J. D., Pearson, C. A. B., Russell, T. W., Tully, D. C., Washburne, A. D., Wenseleers, T., Gimma, A., Waites, W., Wong, K. L. M., van Zandvoort, K., Silverman, J. D., CMMID COVID-19 Working Group, The COVID-19 Genomics UK (COG-UK) Consortium, Diaz-Ordaz, K., Keogh, R., Eggo, R. M., Funk, S., Jit, M., Atkins, K. E., & Edmunds, W. J.
- Affiliations
- London School of Hygiene & Tropical Medicine (GB)
- KU Leuven (BE)
- Pennsylvania State University (US)
- Corresponding author
- Davies, Nicholas G.
- ORCID
- Davies, Nicholas G.
- Abbott, Sam
- Barnard, Rosanna C.
- Jarvis, Christopher I.
- Kucharski, Adam J.
- Munday, James D.
- Pearson, Carl A. B.
- Russell, Timothy W.
- Tully, Damien C.
- Washburne, Alex D.
- Wenseleers, Tom
- Gimma, Amy
- Waites, William
- Wong, Kerry L. M.
- van Zandvoort, Kevin
- Silverman, Justin D.
- Keogh, Ruth
- Eggo, Rosalind M.
- Funk, Sebastian
- Jit, Mark
- Atkins, Katherine E.
- Edmunds, W. John
- Venue
- Science
- Publisher
- openRxiv
- Published
- 2020-12-26
- Volume
- 372
- Issue
- 6538
- ISSN
- 0036-8075 (ISSN-L)
- PMC
- PMC8128288
- OpenAlex
- W3134208712
- Cited by
- 2,557
- References
- 60
- Citations by year
- 2026: 54 · 2025: 90 · 2024: 184 · 2023: 323 · 2022: 839 · 2021: 1060 · 2020: 6
- FWCI
- 233.82
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Science (publishedVersion, cc-by)
- medRxiv (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (publishedVersion, cc-by)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion, cc-by)
- Northumbria Research Link (Northumbria University) (acceptedVersion)
- LSHTM Research Online (London School of Hygiene and Tropical Medicine) (acceptedVersion)
- Strathprints: The University of Strathclyde institutional repository (University of Strathclyde) (acceptedVersion)
- Informationszentrum Lebenswissenschaften (ZB MED) (submittedVersion, cc-by)
- Lirias (KU Leuven) (submittedVersion, other-oa)
- Edinburgh Research Explorer (University of Edinburgh) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Bill and Melinda Gates Foundation (INV-016832, OPP1184344, INV-003174)
- University of Kentucky
- University of Bern
- Wellcome Trust (206250, 210758/Z/18/Z, 206250/Z/17/Z, Z/17/Z, 221303/Z/20/Z, 221303, 210758, 218554/Z/19/Z)
- UK Research and Innovation (ES/P010873/1, ES/P010873, MC_PC_19027)
- Universitätsspital Basel
- National Institute for Health Research Health Protection Research Unit (NIHR200908, NIHR200929)
- National Institute for Health and Care Research ((NIHR200929), MR/V027956/1, (NIHR200908), 16/137/109, 16/137/10)
- Research Councils UK (MR/S003975/1, ES/P010873/1)
- Academy of Medical Sciences (AMS-CSF4-Torok)
- University of Warwick
- European Commission (101003688, COVID-19, 19065)
- Public Health England
- CANDU Owners Group
- Eidgenössische Technische Hochschule Zürich
- Hôpitaux Universitaires de Genève
- Université de Sfax
- Foreign, Commonwealth and Development Office (221303/Z/20/Z)
- Medical Research Council (MR/S003975/1, 206250/Z/17/Z, MR/V027956/1, NIHR200908, COVID-19, MC_PC_19065)
- Economic and Social Research Council (ES/P010873/1)
- Statens Serum Institut
- Research England
- National Institute of General Medical Sciences (T32GM145449)
- H2020 European Research Council (757688)
- Health Data Research UK
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Health Sciences › Medicine › Infectious Diseases › SARS-CoV-2 and COVID-19 Research
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Keywords
- SARS-CoV-2, B.1.1.7 variant, reproduction number, SARS-CoV-2 transmission, epidemic forecasting, COVID-19 containment measures, vaccine rollout, school closures, England
- MeSH terms
- COVID-19, SARS-CoV-2, COVID-19 Vaccines, Adolescent, Adult, Aged, Aged, 80 and over, Child, Child, Preschool, Communicable Disease Control, England, Europe, Female, Humans, Infant, Male, Middle Aged, Models, Theoretical, Mutation, Severity of Illness Index, Socioeconomic Factors, United States, Viral Load, Basic Reproduction Number, Young Adult
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