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

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          Abstract

          Fueling outbreaks

          The B.1.1.7 lineage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused fast-spreading outbreaks globally. Intrinsically, this variant has greater transmissibility than its predecessors, but this capacity has been amplified in some circumstances to tragic effect by a combination of human behavior and local immunity. What are the extrinsic factors that help or hinder the rapid dissemination of variants? Kraemer et al. explored the invasion dynamics of B.1.1.7. in fine detail, from its location of origin in Kent, UK, to its heterogenous spread around the country. A combination of mobile phone and virus data including more than 17,000 genomes shows how distinct phases of dispersal were related to intensity of mobility and the timing of lockdowns. As the local outbreaks grew, importation from the London source area became less important. Had B.1.1.7. emerged at a slightly different time of year, its impact might have been different. —CA

          Abstract

          Disentangling the factors that contribute to the rapid spread of virus variants is essential for understanding their epidemiological consequences.

          Abstract

          Understanding the causes and consequences of the emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern is crucial to pandemic control yet difficult to achieve because they arise in the context of variable human behavior and immunity. We investigated the spatial invasion dynamics of lineage B.1.1.7 by jointly analyzing UK human mobility, virus genomes, and community-based polymerase chain reaction data. We identified a multistage 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 explored how B.1.1.7 spread was shaped by nonpharmaceutical 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.

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          FastTree 2 – Approximately Maximum-Likelihood Trees for Large Alignments

          Background We recently described FastTree, a tool for inferring phylogenies for alignments with up to hundreds of thousands of sequences. Here, we describe improvements to FastTree that improve its accuracy without sacrificing scalability. Methodology/Principal Findings Where FastTree 1 used nearest-neighbor interchanges (NNIs) and the minimum-evolution criterion to improve the tree, FastTree 2 adds minimum-evolution subtree-pruning-regrafting (SPRs) and maximum-likelihood NNIs. FastTree 2 uses heuristics to restrict the search for better trees and estimates a rate of evolution for each site (the “CAT” approximation). Nevertheless, for both simulated and genuine alignments, FastTree 2 is slightly more accurate than a standard implementation of maximum-likelihood NNIs (PhyML 3 with default settings). Although FastTree 2 is not quite as accurate as methods that use maximum-likelihood SPRs, most of the splits that disagree are poorly supported, and for large alignments, FastTree 2 is 100–1,000 times faster. FastTree 2 inferred a topology and likelihood-based local support values for 237,882 distinct 16S ribosomal RNAs on a desktop computer in 22 hours and 5.8 gigabytes of memory. Conclusions/Significance FastTree 2 allows the inference of maximum-likelihood phylogenies for huge alignments. FastTree 2 is freely available at http://www.microbesonline.org/fasttree.
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            IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

            Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.
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              Temporal dynamics in viral shedding and transmissibility of COVID-19

              We report temporal patterns of viral shedding in 94 patients with laboratory-confirmed COVID-19 and modeled COVID-19 infectiousness profiles from a separate sample of 77 infector-infectee transmission pairs. We observed the highest viral load in throat swabs at the time of symptom onset, and inferred that infectiousness peaked on or before symptom onset. We estimated that 44% (95% confidence interval, 25-69%) of secondary cases were infected during the index cases' presymptomatic stage, in settings with substantial household clustering, active case finding and quarantine outside the home. Disease control measures should be adjusted to account for probable substantial presymptomatic transmission.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - review & editing
                Role: ConceptualizationRole: Formal analysisRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - review & editing
                Role: MethodologyRole: Software
                Role: Formal analysisRole: Writing - review & editing
                Role: ValidationRole: Writing - review & editing
                Role: InvestigationRole: SoftwareRole: Writing - review & editing
                Role: VisualizationRole: Writing - review & editing
                Role: ResourcesRole: Software
                Role: Data curationRole: SoftwareRole: Writing - review & editing
                Role: ConceptualizationRole: Formal analysisRole: ResourcesRole: Software
                Role: Formal analysisRole: SoftwareRole: ValidationRole: VisualizationRole: Writing - original draftRole: Writing - review & editing
                Role: ConceptualizationRole: Writing - review & editing
                Role: Conceptualization
                Role: ConceptualizationRole: InvestigationRole: VisualizationRole: Writing - review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: SoftwareRole: Writing - review & editing
                Role: Data curationRole: Funding acquisitionRole: Project administrationRole: ResourcesRole: Supervision
                Role: Writing - review & editing
                Role: Methodology
                Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: Writing - review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing - review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: Writing - original draftRole: Writing - review & editing
                Journal
                Science
                Science
                science
                science
                Science (New York, N.y.)
                American Association for the Advancement of Science
                0036-8075
                1095-9203
                20 August 2021
                22 July 2021
                22 July 2021
                : 373
                : 6557
                : 889-895
                Affiliations
                [1 ]Department of Zoology, University of Oxford, Oxford, UK.
                [2 ]Institute of Evolutionary Biology, University of Edinburgh, Edinburgh, UK.
                [3 ]Molecular Immunity Unit, Department of Medicine, Cambridge University, Cambridge, UK.
                [4 ]Spatial Epidemiology Lab (SpELL), Université Libre de Bruxelles, Bruxelles, Belgium.
                [5 ]Department of Microbiology, Immunology and Transplantation, Rega Institute, KU Leuven, 3000 Leuven, Belgium.
                [6 ]MRC Centre for Global Infectious Disease Analysis, Jameel Institute for Disease and Emergency Analytics, Imperial College London, London, UK.
                [7 ]Department of Plant Sciences, University of Oxford, Oxford, UK.
                [8 ]Network Science Institute, Northeastern University, Boston, USA.
                [9 ]Instituto de Medicina Tropical, Faculdade de Medicina da Universidade de Sao Paulo, Sao Paulo, Brazil.
                [10 ]Centre for Genomic Pathogen Surveillance, Wellcome Genome Campus, Hinxton, UK.
                [11 ]Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
                [12 ]Institute of Microbiology and Infection, University of Birmingham, Birmingham, UK.
                [13 ]Mathematical Modelling of Infectious Diseases Unit, Institut Pasteur, UMR2000, CNRS, Paris, France.
                [14 ]Vermont Complex Systems Center, University of Vermont, Burlington, USA.
                [15 ]Santa Fe Institute, Santa Fe, USA.
                [16 ]Department of Pathobiology and Population Sciences, Royal Veterinary College London, London, UK.
                Author notes
                [†]

                These authors contributed equally to this work.

                [‡]

                Consortium members and affiliations are listed in the supplementary materials.

                Author information
                https://orcid.org/0000-0001-8838-7147
                https://orcid.org/0000-0002-3509-8146
                https://orcid.org/0000-0003-0977-5534
                https://orcid.org/0000-0001-9558-1052
                https://orcid.org/0000-0002-8313-819X
                https://orcid.org/0000-0002-9846-8917
                https://orcid.org/0000-0002-1915-7732
                https://orcid.org/0000-0002-7806-3605
                https://orcid.org/0000-0001-6356-4688
                https://orcid.org/0000-0002-9220-2739
                https://orcid.org/0000-0002-9981-0649
                https://orcid.org/0000-0002-5577-9897
                https://orcid.org/0000-0001-8083-474X
                https://orcid.org/0000-0001-8326-5044
                https://orcid.org/0000-0003-3419-4205
                https://orcid.org/0000-0001-6268-8937
                https://orcid.org/0000-0002-9747-8822
                https://orcid.org/0000-0001-6688-0854
                https://orcid.org/0000-0002-9843-8988
                https://orcid.org/0000-0003-0352-6289
                https://orcid.org/0000-0001-9186-4549
                https://orcid.org/0000-0003-4337-3707
                https://orcid.org/0000-0001-5716-2770
                https://orcid.org/0000-0002-8797-2667
                Article
                abj0113
                10.1126/science.abj0113
                9269003
                34301854
                251c6328-50b3-4c50-bddd-2d282dabf4a8
                Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution License 4.0 (CC BY).

                This is an open-access article distributed under the terms of the Creative Commons Attribution license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 16 April 2021
                : 12 July 2021
                Product
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100004211, Oxford Martin School, University of Oxford;
                Categories
                Research Article
                Research Articles
                R-Articles
                Ecology
                Epidemiology
                Coronavirus
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