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      Coast-to-coast spread of SARS-CoV-2 in the United States revealed by genomic epidemiology

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      1 , 22 , 1 , 22 , 2 , 3 , 22 , 1 , 1 , 4 , 1 , 1 , 5 , 6 , 6 , 6 , 7 , 1 , 1 , 8 , 9 , 9 , 5 , 10 , 11 , 10 , 11 , 10 , 11 , 10 , 10 , 10 , 11 , 1 , 12 , 13 , 1 , 14 , 15 , 20 , 4 , 16 , 17 , 17 , 18 , 19 , 21 , 7 , 12 , 7 , 19 , 2 , 3 , 1 , 1 , 23 , 24
      medRxiv
      Cold Spring Harbor Laboratory
      Genomic epidemiology, SARS-CoV-2, MinION sequencing, Phylogenetics, Travel Risk, COVID-19, Coronavirus

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          Summary

          Since its emergence and detection in Wuhan, China in late 2019, the novel coronavirus SARS-CoV-2 has spread to nearly every country around the world, resulting in hundreds of thousands of infections to date. The virus was first detected in the Pacific Northwest region of the United States in January, 2020, with subsequent COVID-19 outbreaks detected in all 50 states by early March. To uncover the sources of SARS-CoV-2 introductions and patterns of spread within the U.S., we sequenced nine viral genomes from early reported COVID-19 patients in Connecticut. Our phylogenetic analysis places the majority of these genomes with viruses sequenced from Washington state. By coupling our genomic data with domestic and international travel patterns, we show that early SARS-CoV-2 transmission in Connecticut was likely driven by domestic introductions. Moreover, the risk of domestic importation to Connecticut exceeded that of international importation by mid-March regardless of our estimated impacts of federal travel restrictions. This study provides evidence for widespread, sustained transmission of SARS-CoV-2 within the U.S. and highlights the critical need for local surveillance.

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          A pneumonia outbreak associated with a new coronavirus of probable bat origin

          Since the outbreak of severe acute respiratory syndrome (SARS) 18 years ago, a large number of SARS-related coronaviruses (SARSr-CoVs) have been discovered in their natural reservoir host, bats 1–4 . Previous studies have shown that some bat SARSr-CoVs have the potential to infect humans 5–7 . Here we report the identification and characterization of a new coronavirus (2019-nCoV), which caused an epidemic of acute respiratory syndrome in humans in Wuhan, China. The epidemic, which started on 12 December 2019, had caused 2,794 laboratory-confirmed infections including 80 deaths by 26 January 2020. Full-length genome sequences were obtained from five patients at an early stage of the outbreak. The sequences are almost identical and share 79.6% sequence identity to SARS-CoV. Furthermore, we show that 2019-nCoV is 96% identical at the whole-genome level to a bat coronavirus. Pairwise protein sequence analysis of seven conserved non-structural proteins domains show that this virus belongs to the species of SARSr-CoV. In addition, 2019-nCoV virus isolated from the bronchoalveolar lavage fluid of a critically ill patient could be neutralized by sera from several patients. Notably, we confirmed that 2019-nCoV uses the same cell entry receptor—angiotensin converting enzyme II (ACE2)—as SARS-CoV.
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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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              A new coronavirus associated with human respiratory disease in China

              Emerging infectious diseases, such as severe acute respiratory syndrome (SARS) and Zika virus disease, present a major threat to public health 1–3 . Despite intense research efforts, how, when and where new diseases appear are still a source of considerable uncertainty. A severe respiratory disease was recently reported in Wuhan, Hubei province, China. As of 25 January 2020, at least 1,975 cases had been reported since the first patient was hospitalized on 12 December 2019. Epidemiological investigations have suggested that the outbreak was associated with a seafood market in Wuhan. Here we study a single patient who was a worker at the market and who was admitted to the Central Hospital of Wuhan on 26 December 2019 while experiencing a severe respiratory syndrome that included fever, dizziness and a cough. Metagenomic RNA sequencing 4 of a sample of bronchoalveolar lavage fluid from the patient identified a new RNA virus strain from the family Coronaviridae, which is designated here ‘WH-Human 1’ coronavirus (and has also been referred to as ‘2019-nCoV’). Phylogenetic analysis of the complete viral genome (29,903 nucleotides) revealed that the virus was most closely related (89.1% nucleotide similarity) to a group of SARS-like coronaviruses (genus Betacoronavirus, subgenus Sarbecovirus) that had previously been found in bats in China 5 . This outbreak highlights the ongoing ability of viral spill-over from animals to cause severe disease in humans.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                26 March 2020
                : 2020.03.25.20043828
                Affiliations
                [1 ]Department of Epidemiology of Microbial Diseases, Yale School of Public Health, New Haven, CT, 06510, USA
                [2 ]Biozentrum, University of Basel, 4056 Basel, Switzerland
                [3 ]Swiss Institute of Bioinformatics, 1015 Lausanne, Switzerland
                [4 ]BlueDot, Toronto, ON, M5J 1A7, Canada
                [5 ]Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, 06510, USA
                [6 ]Connecticut State Department of Public Health, Hartford, CT, 06510, USA
                [7 ]Department of Laboratory Medicine, Yale School of Medicine, New Haven, CT, 06510, USA
                [8 ]Department of Ecology & Evolutionary Biology, Yale University, New Haven, CT, 06510, USA
                [9 ]Institute of Microbiology and Infection, University of Birmingham, Birmingham B15 2TT, UK
                [10 ]Department of Laboratory Medicine, University of Washington, Seattle, WA, 98195, USA
                [11 ]Vaccine & Infectious Diseases Division, Fred Hutchinson Cancer Research Center, Seattle, WA, 98109, USA
                [12 ]Department of Immunobiology, Yale University, New Haven, CT, 06510, USA
                [13 ]Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA
                [14 ]Yale Institute of Global Health, Yale University, New Haven, CT, 06510, USA
                [15 ]Department of Internal Medicine, Yale School of Medicine, New Haven, CT, 06510, USA
                [16 ]Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, M5B 1A6,Canada
                [17 ]Department of Medicine, Division of Infectious Diseases, University of Toronto, Toronto, ON, M5S 3H2, Canada
                [18 ]Department of Pediatrics, Yale School of Medicine, New Haven, CT, 06510, USA
                [19 ]Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, 06510, USA
                [20 ]Yale School of Nursing, Yale University, New Haven, CT, 06510, USA
                [21 ]Yale New Haven Health, Department of Infection Prevention, New Haven, CT, 06510, USA
                [22 ]These authors contributed equally
                [23 ]Senior author
                [24 ]Lead contact
                Article
                10.1101/2020.03.25.20043828
                7276058
                32511630
                437555f2-af63-41a5-8418-f90295c68b21

                It is made available under a CC-BY-NC-ND 4.0 International license.

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                genomic epidemiology,sars-cov-2,minion sequencing,phylogenetics,travel risk,covid-19,coronavirus

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