.2021 Jul 31:39:101064.

doi: 10.1016/j.eclinm.2021.101064. eCollection 2021 Sep.

Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England

Swapnil Mishra¹, Sören Mindermann², Mrinank Sharma^{3 4 5}, Charles Whittaker¹, Thomas A Mellan¹, Thomas Wilton⁶, Dimitra Klapsa⁶, Ryan Mate⁶, Martin Fritzsche⁶, Maria Zambon⁷, Janvi Ahuja^{5 8}, Adam Howes⁹, Xenia Miscouridou⁹, Guy P Nason⁹, Oliver Ratmann⁹, Elizaveta Semenova⁹, Gavin Leech¹⁰, Julia Fabienne Sandkühler¹¹, Charlie Rogers-Smith¹², Michaela Vollmer^{1 7}, H Juliette T Unwin¹, Yarin Gal², Meera Chand⁷, Axel Gandy⁹, Javier Martin⁶, Erik Volz¹, Neil M Ferguson¹, Samir Bhatt^{1 13}, Jan M Brauner^{2 5}, Seth Flaxman⁹; COVID-19 Genomics UK (COG-UK) Consortium

Affiliations

¹ Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
² Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK.
³ Department of Statistics, University of Oxford, UK.
⁴ Department of Engineering Science, University of Oxford, UK.
⁵ Future of Humanity Institute, University of Oxford, UK.
⁶ National Institute for Biological Standards and Control (NIBSC), UK.
⁷ Public Health England, London, UK.
⁸ Medical Sciences Division, University of Oxford, UK.
⁹ Department of Mathematics, Imperial College London, UK.
¹⁰ Department of Computer Science, University of Bristol, UK.
¹¹ Department of Psychology, University of Bonn, Germany.
¹² OATML Group (work done while at OATML as an external collaborator), Department of Computer Science, University of Oxford, UK.
¹³ Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark.

PMID:34401689
PMCID: PMC8349999
DOI: 10.1016/j.eclinm.2021.101064

Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England

Swapnil Mishra et al. EClinicalMedicine.2021.

.2021 Jul 31:39:101064.

doi: 10.1016/j.eclinm.2021.101064. eCollection 2021 Sep.

Authors

Affiliations

¹ Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
² Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK.
³ Department of Statistics, University of Oxford, UK.
⁴ Department of Engineering Science, University of Oxford, UK.
⁵ Future of Humanity Institute, University of Oxford, UK.
⁶ National Institute for Biological Standards and Control (NIBSC), UK.
⁷ Public Health England, London, UK.
⁸ Medical Sciences Division, University of Oxford, UK.
⁹ Department of Mathematics, Imperial College London, UK.
¹⁰ Department of Computer Science, University of Bristol, UK.
¹¹ Department of Psychology, University of Bonn, Germany.
¹² OATML Group (work done while at OATML as an external collaborator), Department of Computer Science, University of Oxford, UK.
¹³ Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark.

PMID:34401689
PMCID: PMC8349999
DOI: 10.1016/j.eclinm.2021.101064

Abstract

Background: Since its emergence in Autumn 2020, the SARS-CoV-2 Variant of Concern (VOC) B.1.1.7 (WHO label Alpha) rapidly became the dominant lineage across much of Europe. Simultaneously, several other VOCs were identified globally. Unlike B.1.1.7, some of these VOCs possess mutations thought to confer partial immune escape. Understanding when and how these additional VOCs pose a threat in settings where B.1.1.7 is currently dominant is vital.

Methods: We examine trends in the prevalence of non-B.1.1.7 lineages in London and other English regions using passive-case detection PCR data, cross-sectional community infection surveys, genomic surveillance, and wastewater monitoring. The study period spans from 31st January 2021 to 15th May 2021.

Findings: Across data sources, the percentage of non-B.1.1.7 variants has been increasing since late March 2021. This increase was initially driven by a variety of lineages with immune escape. From mid-April, B.1.617.2 (WHO label Delta) spread rapidly, becoming the dominant variant in England by late May.

Interpretation: The outcome of competition between variants depends on a wide range of factors such as intrinsic transmissibility, evasion of prior immunity, demographic specificities and interactions with non-pharmaceutical interventions. The presence and rise of non-B.1.1.7 variants in March likely was driven by importations and some community transmission. There was competition between non-B.1.17 variants which resulted in B.1.617.2 becoming dominant in April and May with considerable community transmission. Our results underscore that early detection of new variants requires a diverse array of data sources in community surveillance. Continued real-time information on the highly dynamic composition and trajectory of different SARS-CoV-2 lineages is essential to future control efforts.

Funding: National Institute for Health Research, Medicines and Healthcare products Regulatory Agency, DeepMind, EPSRC, EA Funds programme, Open Philanthropy, Academy of Medical Sciences Bill,Melinda Gates Foundation, Imperial College Healthcare NHS Trust, The Novo Nordisk Foundation, MRC Centre for Global Infectious Disease Analysis, Community Jameel, Cancer Research UK, Imperial College COVID-19 Research Fund, Medical Research Council, Wellcome Sanger Institute.

Keywords: Epidemiology; Genomic surveillance; Public health; SARS-CoV-2; Variants of concern; Waste water monitoring.

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Conflict of interest statement

Dr. Semenova reports other from AstraZeneca, outside the submitted work; M. Sharma reports grants from EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (EP/S024050/1) and a grant from the EA Funds programme, during the conduct of the study; Dr. Martin reports grants from National Institute for Health Research (NIHR), during the conduct of the study; . Dr. Nason reports and I am a member of the Royal Statistical Society's COVID-19 Taskforce. Dr. Wilton reports grants from National Institute for Health Research (NIHR), during the conduct of the study; . Dr. Mate reports grants from National Institute for Health Research (NIHR), during the conduct of the study; . Dr. Klapsa reports grants from National Institute for Health Research (NIHR), during the conduct of the study; . C. Rogers-Smith reports grants from Open Philanthropy, during the conduct of the study; . Dr. Gal reports grants from research grant (studentship) from GlaxoSmithKline, outside the submitted work; . Dr. Brauner reports grants from Cancer Research UK, during the conduct of the study; Dr. Ferguson reports grants from UK Medical Research Council, grants from UK National Institute of Health Research, grants from Community Jameel, during the conduct of the study; grants from NIH NIGMS, grants from Janssen Pharmaceuticals, grants from Bill and Melinda Gates Foundation, grants from Gavi, the Vaccine Alliance, outside the submitted work; All other authors declare no conflicts of interests or competing interests.

Figures

**Fig. 1**
Trends in S+ infections in London, February-May 2021. (A) Estimated aggregated weekly incidence (log scale) of symptomatic S+ cases diagnosed via community testing (Pillar 2) calculated by multiplying the fraction of S+ cases by the total number of positives and S+ infections estimated from the ONS infection survey .B) Temporal trends in the proportion of cases and infections that are S+, estimated from symptomatic community testing (Pillar 2), the ONS infection survey, and from SARS-CoV-2 sequence data (COG-UK public data, which may include travelers and surge testing; non-B.1.17 fraction is shown). Shaded ribbons represent 95% uncertainty intervals for the mean. Details on uncertainty intervals can be found in Supplementary Text. Results for other regions of England can be found in Supplementary Figures 1 and 2.

**Fig. 2**
Mean Cycle threshold (Ct) values by week for Pillar 2 symptomatic community testing in London. Shaded ribbons show 95% confidence intervals around the mean calculated as 1.96 * standard error (assuming asymptotic normality). Ct values for ORF1ab gene and N gene are shown, with S+ in blue and S- in red. MS2 control indicates the mean Ct value of Bacteriophage MS2, which is added to samples for calibration purposes. In each plot, samples with Ct values above 30 for the specific gene shown are excluded. Results for other regions of England can be found in Supplementary Fig. 3.

**Fig. 3**
Fraction of viral RNA showing mutations at key spike protein amino acid positions, identified in sewage samples from North London. Mean values from replicate sequences (n = 8–12) for each sampling date are shown. Error bars indicate standard error of the mean.A) HV69–70del, Y144del, and A570D are relatively uniquely found in B.1.1.7 (Supplementary Table 1).B) E484K is absent in B.1.1.7. but present in several other variants of interest/concern; and linked to evasion of previous immunity.C) G142D and T478K are associated with B.1.617.2 (G142D is also found in B.1.617.1, Supplementary Table 1).

**Fig. 4**
The sample frequency of non-B.1.1.7 lineages in Greater London in community testing (n = 2957 sequenced samples). (A) Bar charts show the sample proportion of lineages with at least 20 samples after 31 March 2021. Error bars show 95% confidence intervals based on binomial sampling. (B) Stacked area charts show estimates over time of the frequency of lineages in the period 1 March to 29 May. Colour-code is identical to panel A). While a variety of non-B.1.1.7 variants (all S+) are in circulation in March and the beginning of April, by May B.1.617.2 predominates. A of this figure, displaying data that was available until mid-April, can be found in the Supplement (Supplementary Fig. 6).

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Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England

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Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England

Authors

Affiliations

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Conflict of interest statement

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