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Changing composition of SARS-CoV-2 lineages and rise of Delta variant in England.
Mishra, Swapnil; Mindermann, Sören; Sharma, Mrinank; Whittaker, Charles; Mellan, Thomas A; Wilton, Thomas; Klapsa, Dimitra; Mate, Ryan; Fritzsche, Martin; Zambon, Maria; Ahuja, Janvi; Howes, Adam; Miscouridou, Xenia; Nason, Guy P; Ratmann, Oliver; Semenova, Elizaveta; Leech, Gavin; Sandkühler, Julia Fabienne; Rogers-Smith, Charlie; Vollmer, Michaela; Unwin, H Juliette T; Gal, Yarin; Chand, Meera; Gandy, Axel; Martin, Javier; Volz, Erik; Ferguson, Neil M; Bhatt, Samir; Brauner, Jan M; Flaxman, Seth.
Afiliação
  • Mishra S; Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
  • Mindermann S; Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK.
  • Sharma M; Department of Statistics, University of Oxford, UK.
  • Whittaker C; Department of Engineering Science, University of Oxford, UK.
  • Mellan TA; Future of Humanity Institute, University of Oxford, UK.
  • Wilton T; Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
  • Klapsa D; Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
  • Mate R; National Institute for Biological Standards and Control (NIBSC), UK.
  • Fritzsche M; National Institute for Biological Standards and Control (NIBSC), UK.
  • Zambon M; National Institute for Biological Standards and Control (NIBSC), UK.
  • Ahuja J; National Institute for Biological Standards and Control (NIBSC), UK.
  • Howes A; Public Health England, London, UK.
  • Miscouridou X; Future of Humanity Institute, University of Oxford, UK.
  • Nason GP; Medical Sciences Division, University of Oxford, UK.
  • Ratmann O; Department of Mathematics, Imperial College London, UK.
  • Semenova E; Department of Mathematics, Imperial College London, UK.
  • Leech G; Department of Mathematics, Imperial College London, UK.
  • Sandkühler JF; Department of Mathematics, Imperial College London, UK.
  • Rogers-Smith C; Department of Mathematics, Imperial College London, UK.
  • Vollmer M; Department of Computer Science, University of Bristol, UK.
  • Unwin HJT; Department of Psychology, University of Bonn, Germany.
  • Gal Y; OATML Group (work done while at OATML as an external collaborator), Department of Computer Science, University of Oxford, UK.
  • Chand M; Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
  • Gandy A; Public Health England, London, UK.
  • Martin J; Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
  • Volz E; Oxford Applied and Theoretical Machine Learning (OATML) Group, Department of Computer Science, University of Oxford, UK.
  • Ferguson NM; Public Health England, London, UK.
  • Bhatt S; Department of Mathematics, Imperial College London, UK.
  • Brauner JM; National Institute for Biological Standards and Control (NIBSC), UK.
  • Flaxman S; Medical Research Council (MRC) Centre for Global Infectious Disease Analysis, Jameel Institute, School of Public Health, Imperial College London, UK.
EClinicalMedicine ; 39: 101064, 2021 Sep.
Article em En | MEDLINE | ID: mdl-34401689
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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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article