Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence

Kraemer, Moritz U.G., Hill, Verity, Ruis, Christopher, Dellicour, Simon, Bajaj, Sumali, McCrone, John T., Baele, Guy, Parag, Kris V., Battle, Anya Lindström, Gutierrez, Bernardo, Jackson, Ben, Colquhoun, Rachel, O’Toole, Áine, Klein, Brennan, Vespignani, Alessandro, Volz, Erik, Faria, Nuno R., Aanensen, David, Loman, Nicholas J., du Plessis, Louis, Cauchemez, Simon, Rambaut, Andrew, Scarpino, Samuel V., Pybus, Oliver G., The COVID-19 Genomics UK (CoG-UK) consortium, , Bashton, Matthew, Nelson, Andrew, Young, Greg, Smith, Darren and McCann, Clare (2021) Spatiotemporal invasion dynamics of SARS-CoV-2 lineage B.1.1.7 emergence. Science, 373 (6557). pp. 889-895. ISSN 0036-8075

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Official URL: https://doi.org/10.1126/science.abj0113

Abstract

Understanding the causes and consequences of the emergence of SARS-CoV-2 variants of concern is crucial to pandemic control yet difficult to achieve, as they arise in the context of variable human behavior and immunity. We investigate the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based PCR data. We identify a multi-stage spatial invasion process in which early B.1.1.7 growth rates were associated with mobility and asymmetric lineage export from a dominant source location, enhancing the effects of B.1.1.7’s increased intrinsic transmissibility. We further explore how B.1.1.7 spread was shaped by non-pharmaceutical interventions and spatial variation in previous attack rates. Our findings show that careful accounting of the behavioral and epidemiological context within which variants of concern emerge is necessary to interpret correctly their observed relative growth rates.

Item Type: Article
Additional Information: Funding information: We thank all involved in the collection and processing of SARS-CoV-2 testing and genomic data. We also thank Public Health England (PHE) for making anonymized epidemiological data available for this analysis. We thank the Office of National Statistics (ONS) for their effort to publish the Coronavirus (COVID-19) infection surveys in real-time. Funding: VH was supported by the Biotechnology and Biological Sciences Research Council (BBSRC) [grant number BB/M010996/1]. AR acknowledges the support of the Wellcome Trust (Collaborators Award 206298/Z/17/Z – ARTIC network) and the European Research Council (grant agreement no. 725422 – ReservoirDOCS). M.U.G.K. acknowledges support from the Branco Weiss Fellowship. M.U.G.K. and S.D. acknowledge support from the European Union's Horizon 2020 project MOOD (grant agreement no. 874850). O.G.P. and M.U.G.K. acknowledge support from the Oxford Martin School. A.L.B., S.V.S. and M.U.G.K. acknowledge support from the Rockefeller Foundation and Google.org. C.R. was supported by a Fondation Botnar Research Award (Programme grant 6063) and UK Cystic Fibrosis Trust (Innovation Hub Award 001). A.L.B acknowledges support from the Biotechnologyand Biological Sciences Research Council (BBSRC) [grant number BB/M011224/1]. SD acknowledges support from the Fonds National de la Recherche Scientifique (FNRS, Belgium). GB acknowledges support from the Research Foundation - Flanders (Fonds voor Wetenschappelijk Onderzoek - Vlaanderen, G0E1420N and G098321N) and from the Interne Fondsen KU Leuven/Internal Funds KU Leuven under grant agreement C14/18/094. COG-UK is supported by funding from the Medical Research Council (MRC) part of UK Research & Innovation (UKRI), the National Institute of Health Research (NIHR) and Genome Research Limited, operating as the Wellcome Sanger Institute. A.OT is supported by the Wellcome Trust Hosts, Pathogens & Global Health Programme [grant number: grant.203783/Z/16/Z] and Fast Grants [award number: 2236]. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the European Commission or any of the other funders. Author contributions: M.U.G.K., A.R., V.H., C.R., S.D., S.V.S, O.G.P. conceived and planned the research. M.U.G.K., V.H., A.R., C.R., S.D., G.B., B.K., A.L.B., S.D., S.G., S.V.S. analyzed the data. M.U.G.K. and O.G.P. wrote the first draft. All authors contributed to writing and interpreting the results. M.U.G.K., A.R., S.V.S., and O.G.P. jointly supervised this work. Competing interests: O.G.P., A.R., A.O’T. have undertaken consulting for AstraZeneca relating to the genetic diversity and classification of SARS-CoV-2 lineages. SVS is a paid consultant with Pandefense Advisory and Booz Allen Hamilton; is on the advisory board for BioFire Diagnostics Trend Surveillance, which includes paid consulting; and holds unexercised options in Iliad Biotechnologies. These entities provided no financial support associated with this research, did not have a role in the design of this study, and did not have any role during its execution, analyses, interpretation of the data and/or decision to submit. Data and materials availability: Aggregated epidemiological data used in this study are available from https://coronavirus.data.gov.uk/details/download. SARS-CoV-2 infection survey data are available via the Office of National Statistics (ONS) and available from https://www.ons.gov.uk/peoplepopulationandcommunity/healthandsocialcare/conditionsanddiseases/datasets/coronaviruscovid19infectionsurveydata. Raw epidemiological SARS-CoV-2 line list data are available via Public Health England (PHE) and aggregated statistics are available via Github. All genomes, phylogenetic trees, basic metadata are available from the COG-UK consortium website (https://www.cogconsortium.uk/data). The O2 aggregated, anonymised mobile data insights dataset is not publicly available owing to stringent licensing agreements. Information on the process of requesting access to the O2 aggregated mobile data insights dataset is available o2@businesso2.co.uk. The Google COVID-19 Aggregated Mobility Research Dataset is not publicly available owing to stringent licensing agreements. Information on the process of requesting access to the Google mobility data are available from sadilekadam@google.com. Code and data are available on the following GitHub repository https://github.com/COG-UK/B.1.1.7_spatial_analysis_UK and permanently on Zeonodo (57). Matthew Bashton, Andrew Nelson, Darren Smith, Greg Young and Clare McCann are members of the COVID-19 Genomics UK (COG-UK) consortium. Full author details and roles are in the final pages of the supplementary Supplementary Materials.
Subjects: A300 Clinical Medicine
B100 Anatomy, Physiology and Pathology
B200 Pharmacology, Toxicology and Pharmacy
C500 Microbiology
C700 Molecular Biology, Biophysics and Biochemistry
Department: Faculties > Health and Life Sciences > Applied Sciences
Depositing User: Elena Carlaw
Date Deposited: 27 Jul 2021 15:03
Last Modified: 24 Sep 2021 14:30
URI: http://nrl.northumbria.ac.uk/id/eprint/46773

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