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      Phylogenetic analysis of SARS-CoV-2 in Boston highlights the impact of superspreading events

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      Science (New York, N.y.)
      American Association for the Advancement of Science

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          Abstract

          Analysis of 772 complete SARS-CoV-2 genomes from early in the Boston area epidemic revealed numerous introductions of the virus, a small number of which led to most cases. The data revealed two superspreading events. One, in a skilled nursing facility, led to rapid transmission and significant mortality in this vulnerable population but little broader spread, while other introductions into the facility had little effect. The second, at an international business conference, produced sustained community transmission and was exported, resulting in extensive regional, national, and international spread. The two events also differed significantly in the genetic variation they generated, suggesting varying transmission dynamics in superspreading events. Our results show how genomic epidemiology can help understand the link between individual clusters and wider community spread.

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          MAFFT Multiple Sequence Alignment Software Version 7: Improvements in Performance and Usability

          We report a major update of the MAFFT multiple sequence alignment program. This version has several new features, including options for adding unaligned sequences into an existing alignment, adjustment of direction in nucleotide alignment, constrained alignment and parallel processing, which were implemented after the previous major update. This report shows actual examples to explain how these features work, alone and in combination. Some examples incorrectly aligned by MAFFT are also shown to clarify its limitations. We discuss how to avoid misalignments, and our ongoing efforts to overcome such limitations.
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            IQ-TREE: A Fast and Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies

            Large phylogenomics data sets require fast tree inference methods, especially for maximum-likelihood (ML) phylogenies. Fast programs exist, but due to inherent heuristics to find optimal trees, it is not clear whether the best tree is found. Thus, there is need for additional approaches that employ different search strategies to find ML trees and that are at the same time as fast as currently available ML programs. We show that a combination of hill-climbing approaches and a stochastic perturbation method can be time-efficiently implemented. If we allow the same CPU time as RAxML and PhyML, then our software IQ-TREE found higher likelihoods between 62.2% and 87.1% of the studied alignments, thus efficiently exploring the tree-space. If we use the IQ-TREE stopping rule, RAxML and PhyML are faster in 75.7% and 47.1% of the DNA alignments and 42.2% and 100% of the protein alignments, respectively. However, the range of obtaining higher likelihoods with IQ-TREE improves to 73.3-97.1%.
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              An interactive web-based dashboard to track COVID-19 in real time

              In December, 2019, a local outbreak of pneumonia of initially unknown cause was detected in Wuhan (Hubei, China), and was quickly determined to be caused by a novel coronavirus, 1 namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The outbreak has since spread to every province of mainland China as well as 27 other countries and regions, with more than 70 000 confirmed cases as of Feb 17, 2020. 2 In response to this ongoing public health emergency, we developed an online interactive dashboard, hosted by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University, Baltimore, MD, USA, to visualise and track reported cases of coronavirus disease 2019 (COVID-19) in real time. The dashboard, first shared publicly on Jan 22, illustrates the location and number of confirmed COVID-19 cases, deaths, and recoveries for all affected countries. It was developed to provide researchers, public health authorities, and the general public with a user-friendly tool to track the outbreak as it unfolds. All data collected and displayed are made freely available, initially through Google Sheets and now through a GitHub repository, along with the feature layers of the dashboard, which are now included in the Esri Living Atlas. The dashboard reports cases at the province level in China; at the city level in the USA, Australia, and Canada; and at the country level otherwise. During Jan 22–31, all data collection and processing were done manually, and updates were typically done twice a day, morning and night (US Eastern Time). As the outbreak evolved, the manual reporting process became unsustainable; therefore, on Feb 1, we adopted a semi-automated living data stream strategy. Our primary data source is DXY, an online platform run by members of the Chinese medical community, which aggregates local media and government reports to provide cumulative totals of COVID-19 cases in near real time at the province level in China and at the country level otherwise. Every 15 min, the cumulative case counts are updated from DXY for all provinces in China and for other affected countries and regions. For countries and regions outside mainland China (including Hong Kong, Macau, and Taiwan), we found DXY cumulative case counts to frequently lag behind other sources; we therefore manually update these case numbers throughout the day when new cases are identified. To identify new cases, we monitor various Twitter feeds, online news services, and direct communication sent through the dashboard. Before manually updating the dashboard, we confirm the case numbers with regional and local health departments, including the respective centres for disease control and prevention (CDC) of China, Taiwan, and Europe, the Hong Kong Department of Health, the Macau Government, and WHO, as well as city-level and state-level health authorities. For city-level case reports in the USA, Australia, and Canada, which we began reporting on Feb 1, we rely on the US CDC, the government of Canada, the Australian Government Department of Health, and various state or territory health authorities. All manual updates (for countries and regions outside mainland China) are coordinated by a team at Johns Hopkins University. The case data reported on the dashboard aligns with the daily Chinese CDC 3 and WHO situation reports 2 for within and outside of mainland China, respectively (figure ). Furthermore, the dashboard is particularly effective at capturing the timing of the first reported case of COVID-19 in new countries or regions (appendix). With the exception of Australia, Hong Kong, and Italy, the CSSE at Johns Hopkins University has reported newly infected countries ahead of WHO, with Hong Kong and Italy reported within hours of the corresponding WHO situation report. Figure Comparison of COVID-19 case reporting from different sources Daily cumulative case numbers (starting Jan 22, 2020) reported by the Johns Hopkins University Center for Systems Science and Engineering (CSSE), WHO situation reports, and the Chinese Center for Disease Control and Prevention (Chinese CDC) for within (A) and outside (B) mainland China. Given the popularity and impact of the dashboard to date, we plan to continue hosting and managing the tool throughout the entirety of the COVID-19 outbreak and to build out its capabilities to establish a standing tool to monitor and report on future outbreaks. We believe our efforts are crucial to help inform modelling efforts and control measures during the earliest stages of the outbreak.
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                Author and article information

                Journal
                Science
                Science
                SCIENCE
                Science (New York, N.y.)
                American Association for the Advancement of Science
                0036-8075
                1095-9203
                10 December 2020
                : eabe3261
                Affiliations
                [1 ]Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, MA 02142, USA.
                [2 ]Division of Infectious Diseases, Massachusetts General Hospital, Boston, MA, USA.
                [3 ]Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, MA 02138, USA.
                [4 ]Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, USA.
                [5 ]Massachusetts Department of Public Health, Boston, MA, USA.
                [6 ]Harvard Program in Biological and Biomedical Sciences, Harvard Medical School, Boston, MA 02115, USA.
                [7 ]Department of Systems Biology, Harvard Medical School, Boston, MA, USA.
                [8 ]Department of Pathology, Massachusetts General Hospital, Boston, MA, USA.
                [9 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
                [10 ]Division of Pulmonary and Critical Care, Massachusetts General Hospital, Boston, MA, USA.
                [11 ]Department of Pediatrics, Harvard Medical School, Boston, MA, USA.
                [12 ]Institute for Research, Quality, and Policy in Homeless Health Care, Boston Health Care for the Homeless Program, Boston, MA, USA.
                [13 ]Section of General Internal Medicine, Boston University Medical Center, Boston.
                [14 ]Division of General Internal Medicine, Massachusetts General Hospital, Boston.
                [15 ]Department of Medicine, Harvard Medical School, Boston, MA, USA.
                [16 ]Institute for Medical Engineering and Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
                [17 ]Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01605, USA.
                [18 ]Massachusetts Consortium on Pathogen Readiness, Boston, MA, 02115, USA.
                [19 ]Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA.
                [20 ]University of Massachusetts Medical School, Infectious Diseases and Immunology, Worcester, MA 01655.
                [21 ]Pediatric Infectious Disease Unit, Massachusetts General Hospital for Children, Boston, MA, USA.
                [22 ]Department of Pathology, Harvard Medical School, Boston, MA, USA.
                [23 ]Howard Hughes Medical Institute, 4000 Jones Bridge Rd, Chevy Chase, MD 20815.
                Author notes
                [*]

                These authors contributed equally to this work.

                [‡]

                These authors contributed equally to this work.

                Author information
                https://orcid.org/0000-0002-2758-4005
                https://orcid.org/0000-0002-1799-7295
                https://orcid.org/0000-0002-0180-6802
                https://orcid.org/0000-0002-9626-7820
                https://orcid.org/0000-0001-6699-3568
                https://orcid.org/0000-0002-9114-6421
                https://orcid.org/0000-0003-4163-706X
                https://orcid.org/0000-0001-6011-4323
                https://orcid.org/0000-0003-0165-5669
                https://orcid.org/0000-0003-2490-2191
                https://orcid.org/0000-0002-4996-6442
                https://orcid.org/0000-0002-9454-2737
                https://orcid.org/0000-0003-3140-1483
                https://orcid.org/0000-0003-0309-368X
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                https://orcid.org/0000-0001-7115-8305
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                https://orcid.org/0000-0002-3871-5760
                https://orcid.org/0000-0002-1115-0894
                https://orcid.org/0000-0003-3642-5126
                https://orcid.org/0000-0002-2570-4253
                https://orcid.org/0000-0001-5430-3937
                https://orcid.org/0000-0001-7802-4721
                https://orcid.org/0000-0001-5650-4054
                https://orcid.org/0000-0002-3603-8110
                https://orcid.org/0000-0003-4558-0339
                https://orcid.org/0000-0002-6319-7336
                https://orcid.org/0000-0003-2589-7777
                https://orcid.org/0000-0001-9414-8521
                https://orcid.org/0000-0002-3723-1326
                https://orcid.org/0000-0002-8217-5458
                https://orcid.org/0000-0002-9843-1890
                https://orcid.org/0000-0001-7226-7781
                https://orcid.org/0000-0003-0082-968X
                Article
                abe3261
                10.1126/science.abe3261
                7857412
                33303686
                5326dd6a-e456-4be7-bdf3-ac57c9b9005b
                Copyright © 2020 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
                : 19 August 2020
                : 07 December 2020
                Funding
                Funded by: doi http://dx.doi.org/10.13039/100000052, NIH Office of the Director;
                Award ID: U19AI110818
                Categories
                Research Article
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                Epidemiology
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