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Mishra S, Mindermann S, Sharma M, Whittaker C, Mellan TA, Wilton T, Klapsa D, Mate R, Fritzsche M, Zambon M, Ahuja J, Howes A, Miscouridou X, Nason GP, Ratmann O, Semenova E, Leech G, Sandkühler JF, Rogers-Smith C, Vollmer M, Unwin HJT, Gal Y, Chand M, Gandy A, Martin J, Volz E, Ferguson NM, Bhatt S, Brauner JM, Flaxman S. Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England. EClinicalMedicine 2021; 39:101064. [PMID: 34401689 PMCID: PMC8349999 DOI: 10.1016/j.eclinm.2021.101064] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 07/13/2021] [Accepted: 07/15/2021] [Indexed: 02/06/2023] Open
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.
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Affiliation(s)
- Swapnil Mishra
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Sören Mindermann
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | - Mrinank Sharma
- Department of Statistics, University of Oxford, UK
- Department of Engineering Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Charles Whittaker
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas A Mellan
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas Wilton
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Dimitra Klapsa
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Ryan Mate
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Martin Fritzsche
- National Institute for Biological Standards and Control (NIBSC), UK
| | | | - Janvi Ahuja
- Future of Humanity Institute, University of Oxford, UK
- Medical Sciences Division, University of Oxford, UK
| | - Adam Howes
- Department of Mathematics, Imperial College London, UK
| | | | - Guy P Nason
- Department of Mathematics, Imperial College London, UK
| | | | | | - Gavin Leech
- Department of Computer Science, University of Bristol, UK
| | | | - Charlie Rogers-Smith
- OATML Group (work done while at OATML as an external collaborator), Department of Computer Science, University of Oxford, UK
| | - Michaela Vollmer
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Public Health England, London, UK
| | - H Juliette T Unwin
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | | | - Axel Gandy
- Department of Mathematics, Imperial College London, UK
| | - Javier Martin
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Erik Volz
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Neil M Ferguson
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Samir Bhatt
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark
| | - Jan M Brauner
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Seth Flaxman
- Department of Mathematics, Imperial College London, UK
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Mishra S, Mindermann S, Sharma M, Whittaker C, Mellan TA, Wilton T, Klapsa D, Mate R, Fritzsche M, Zambon M, Ahuja J, Howes A, Miscouridou X, Nason GP, Ratmann O, Semenova E, Leech G, Sandkühler JF, Rogers-Smith C, Vollmer M, Unwin HJT, Gal Y, Chand M, Gandy A, Martin J, Volz E, Ferguson NM, Bhatt S, Brauner JM, Flaxman S. Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England. EClinicalMedicine 2021. [PMID: 34401689 DOI: 10.25561/88876] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/21/2023] Open
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.
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Affiliation(s)
- Swapnil Mishra
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Sören Mindermann
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | - Mrinank Sharma
- Department of Statistics, University of Oxford, UK
- Department of Engineering Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Charles Whittaker
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas A Mellan
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Thomas Wilton
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Dimitra Klapsa
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Ryan Mate
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Martin Fritzsche
- National Institute for Biological Standards and Control (NIBSC), UK
| | | | - Janvi Ahuja
- Future of Humanity Institute, University of Oxford, UK
- Medical Sciences Division, University of Oxford, UK
| | - Adam Howes
- Department of Mathematics, Imperial College London, UK
| | | | - Guy P Nason
- Department of Mathematics, Imperial College London, UK
| | | | | | - Gavin Leech
- Department of Computer Science, University of Bristol, UK
| | | | - Charlie Rogers-Smith
- OATML Group (work done while at OATML as an external collaborator), Department of Computer Science, University of Oxford, UK
| | - Michaela Vollmer
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Public Health England, London, UK
| | - H Juliette T Unwin
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
| | | | - Axel Gandy
- Department of Mathematics, Imperial College London, UK
| | - Javier Martin
- National Institute for Biological Standards and Control (NIBSC), UK
| | - Erik Volz
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Neil M Ferguson
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
| | - Samir Bhatt
- Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK
- Section of Epidemiology, Department of Public Health, University of Copenhagen, Denmark
| | - Jan M Brauner
- Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK
- Future of Humanity Institute, University of Oxford, UK
| | - Seth Flaxman
- Department of Mathematics, Imperial College London, UK
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