Transmission dynamics and evolutionary history of 2019‐nCoV
To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogen...
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Veröffentlicht in: | Journal of medical virology 2020-05, Vol.92 (5), p.501-511 |
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description | To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10−3 to 1.8266 × 10−3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time.
Highlights
On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time. |
doi_str_mv | 10.1002/jmv.25701 |
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Highlights
On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time.</description><identifier>ISSN: 0146-6615</identifier><identifier>EISSN: 1096-9071</identifier><identifier>DOI: 10.1002/jmv.25701</identifier><identifier>PMID: 32027035</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>2019‐nCoV ; Bayes Theorem ; Bayesian analysis ; Betacoronavirus - genetics ; Biological evolution ; China ; Cluster analysis ; Coronavirus Infections - epidemiology ; Coronavirus Infections - transmission ; Coronavirus Infections - virology ; COVID-19 ; Disease Outbreaks ; Epidemiology ; Evolution ; evolutionary rate ; Gene mapping ; Gene sequencing ; Genetic diversity ; Genome, Viral ; Genomes ; Health surveillance ; Humans ; Identification methods ; Likelihood Functions ; Mapping ; Models, Genetic ; Mutation Rate ; Network analysis ; phylogenetic cluster ; Phylogenetics ; Phylogeny ; Pneumonia, Viral - epidemiology ; Pneumonia, Viral - transmission ; Prevention ; Public health ; SARS-CoV-2 ; Statistical inference ; Strains (organisms) ; Surveillance ; Thailand ; time to most recent common ancestor ; TMRCA ; transmission cluster ; United States ; Viral diseases ; Virology ; Viruses</subject><ispartof>Journal of medical virology, 2020-05, Vol.92 (5), p.501-511</ispartof><rights>2020 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4711-ca251830cd29b3015f515fe0db68234ad90099880862d0c3ff433cbc976941eb3</citedby><cites>FETCH-LOGICAL-c4711-ca251830cd29b3015f515fe0db68234ad90099880862d0c3ff433cbc976941eb3</cites><orcidid>0000-0002-3470-2196</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjmv.25701$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjmv.25701$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>230,314,776,780,881,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32027035$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Li, Xingguang</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Zhao, Xiaofang</creatorcontrib><creatorcontrib>Zai, Junjie</creatorcontrib><creatorcontrib>Zhao, Qiang</creatorcontrib><creatorcontrib>Li, Yi</creatorcontrib><creatorcontrib>Chaillon, Antoine</creatorcontrib><title>Transmission dynamics and evolutionary history of 2019‐nCoV</title><title>Journal of medical virology</title><addtitle>J Med Virol</addtitle><description>To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10−3 to 1.8266 × 10−3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time.
Highlights
On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time.</description><subject>2019‐nCoV</subject><subject>Bayes Theorem</subject><subject>Bayesian analysis</subject><subject>Betacoronavirus - genetics</subject><subject>Biological evolution</subject><subject>China</subject><subject>Cluster analysis</subject><subject>Coronavirus Infections - epidemiology</subject><subject>Coronavirus Infections - transmission</subject><subject>Coronavirus Infections - virology</subject><subject>COVID-19</subject><subject>Disease Outbreaks</subject><subject>Epidemiology</subject><subject>Evolution</subject><subject>evolutionary rate</subject><subject>Gene mapping</subject><subject>Gene sequencing</subject><subject>Genetic diversity</subject><subject>Genome, Viral</subject><subject>Genomes</subject><subject>Health surveillance</subject><subject>Humans</subject><subject>Identification methods</subject><subject>Likelihood Functions</subject><subject>Mapping</subject><subject>Models, Genetic</subject><subject>Mutation Rate</subject><subject>Network analysis</subject><subject>phylogenetic cluster</subject><subject>Phylogenetics</subject><subject>Phylogeny</subject><subject>Pneumonia, Viral - epidemiology</subject><subject>Pneumonia, Viral - transmission</subject><subject>Prevention</subject><subject>Public health</subject><subject>SARS-CoV-2</subject><subject>Statistical inference</subject><subject>Strains (organisms)</subject><subject>Surveillance</subject><subject>Thailand</subject><subject>time to most recent common ancestor</subject><subject>TMRCA</subject><subject>transmission cluster</subject><subject>United States</subject><subject>Viral diseases</subject><subject>Virology</subject><subject>Viruses</subject><issn>0146-6615</issn><issn>1096-9071</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kctKxDAUhoMoOl4WvoAU3Oiiek7Sps1CQQavKG7UbUjTVDu0iTbTkdn5CD6jT2LGUVHBRTiQ8_HxH35CNhH2EIDuj9rJHk0zwAUyQBA8FpDhIhkAJjzmHNMVsur9CAByQekyWWEUaAYsHZCDm05Z39be185G5dSqttY-UraMzMQ1_Th8q24aPdR-7MJ0VUQBxdvLqx26u3WyVKnGm43PuUZuT45vhmfx5fXp-fDoMtZJhhhrRVPMGeiSioIBplUanoGy4DlliSoFgBB5DjmnJWhWVQljutAi4yJBU7A1cjj3PvZFa0pt7LhTjXzs6jaEk07V8vfG1g_y3k1khpznOQbBzqegc0-98WMZTtamaZQ1rveSspRCCiKhAd3-g45c39lwXqCyjCICzqjdOaU7531nqu8wCHJWigylyI9SArv1M_03-dVCAPbnwHPdmOn_JnlxdTdXvgMpsZXV</recordid><startdate>202005</startdate><enddate>202005</enddate><creator>Li, Xingguang</creator><creator>Wang, Wei</creator><creator>Zhao, Xiaofang</creator><creator>Zai, Junjie</creator><creator>Zhao, Qiang</creator><creator>Li, Yi</creator><creator>Chaillon, Antoine</creator><general>Wiley Subscription Services, Inc</general><general>John Wiley and Sons Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QL</scope><scope>7TK</scope><scope>7U9</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>H94</scope><scope>K9.</scope><scope>M7N</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-3470-2196</orcidid></search><sort><creationdate>202005</creationdate><title>Transmission dynamics and evolutionary history of 2019‐nCoV</title><author>Li, Xingguang ; Wang, Wei ; Zhao, Xiaofang ; Zai, Junjie ; Zhao, Qiang ; Li, Yi ; Chaillon, Antoine</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4711-ca251830cd29b3015f515fe0db68234ad90099880862d0c3ff433cbc976941eb3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>2019‐nCoV</topic><topic>Bayes Theorem</topic><topic>Bayesian analysis</topic><topic>Betacoronavirus - genetics</topic><topic>Biological evolution</topic><topic>China</topic><topic>Cluster analysis</topic><topic>Coronavirus Infections - epidemiology</topic><topic>Coronavirus Infections - transmission</topic><topic>Coronavirus Infections - virology</topic><topic>COVID-19</topic><topic>Disease Outbreaks</topic><topic>Epidemiology</topic><topic>Evolution</topic><topic>evolutionary rate</topic><topic>Gene mapping</topic><topic>Gene sequencing</topic><topic>Genetic diversity</topic><topic>Genome, Viral</topic><topic>Genomes</topic><topic>Health surveillance</topic><topic>Humans</topic><topic>Identification methods</topic><topic>Likelihood Functions</topic><topic>Mapping</topic><topic>Models, Genetic</topic><topic>Mutation Rate</topic><topic>Network analysis</topic><topic>phylogenetic cluster</topic><topic>Phylogenetics</topic><topic>Phylogeny</topic><topic>Pneumonia, Viral - epidemiology</topic><topic>Pneumonia, Viral - transmission</topic><topic>Prevention</topic><topic>Public health</topic><topic>SARS-CoV-2</topic><topic>Statistical inference</topic><topic>Strains (organisms)</topic><topic>Surveillance</topic><topic>Thailand</topic><topic>time to most recent common ancestor</topic><topic>TMRCA</topic><topic>transmission cluster</topic><topic>United States</topic><topic>Viral diseases</topic><topic>Virology</topic><topic>Viruses</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Li, Xingguang</creatorcontrib><creatorcontrib>Wang, Wei</creatorcontrib><creatorcontrib>Zhao, Xiaofang</creatorcontrib><creatorcontrib>Zai, Junjie</creatorcontrib><creatorcontrib>Zhao, Qiang</creatorcontrib><creatorcontrib>Li, Yi</creatorcontrib><creatorcontrib>Chaillon, Antoine</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Neurosciences Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Algology Mycology and Protozoology Abstracts (Microbiology C)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of medical virology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Xingguang</au><au>Wang, Wei</au><au>Zhao, Xiaofang</au><au>Zai, Junjie</au><au>Zhao, Qiang</au><au>Li, Yi</au><au>Chaillon, Antoine</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Transmission dynamics and evolutionary history of 2019‐nCoV</atitle><jtitle>Journal of medical virology</jtitle><addtitle>J Med Virol</addtitle><date>2020-05</date><risdate>2020</risdate><volume>92</volume><issue>5</issue><spage>501</spage><epage>511</epage><pages>501-511</pages><issn>0146-6615</issn><eissn>1096-9071</eissn><abstract>To investigate the time origin, genetic diversity, and transmission dynamics of the recent 2019‐nCoV outbreak in China and beyond, a total of 32 genomes of virus strains sampled from China, Thailand, and the USA with sampling dates between 24 December 2019 and 23 January 2020 were analyzed. Phylogenetic, transmission network, and likelihood‐mapping analyses of the genome sequences were performed. On the basis of the likelihood‐mapping analysis, the increasing tree‐like signals (from 0% to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We identified three phylogenetic clusters using the Bayesian inference framework and three transmission clusters using transmission network analysis, with only one cluster identified by both methods using the above genome sequences of 2019‐nCoV strains. The estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10−3 to 1.8266 × 10−3 substitutions per site per year. On the basis of our study, undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real‐time.
Highlights
On the basis of our results in the present study, the increasing tree‐like signals (from 0 to 8.2%, 18.2%, and 25.4%) over time may be indicative of increasing genetic diversity of 2019‐nCoV in human hosts. We only find one cluster identified by two methods (phylogenetic clusters using the Bayesian inference framework and transmission clusters using transmission network analysis). We estimated mean evolutionary rate for 2019‐nCoV ranged from 1.7926 × 10‐3 to 1.8266 × 10‐3 substitutions per site per year. Our study also highlights that undertaking epidemiological investigations and genomic data surveillance could positively impact public health in terms of guiding prevention efforts to reduce 2019‐nCOV transmission in real time.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>32027035</pmid><doi>10.1002/jmv.25701</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0002-3470-2196</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | 2019‐nCoV Bayes Theorem Bayesian analysis Betacoronavirus - genetics Biological evolution China Cluster analysis Coronavirus Infections - epidemiology Coronavirus Infections - transmission Coronavirus Infections - virology COVID-19 Disease Outbreaks Epidemiology Evolution evolutionary rate Gene mapping Gene sequencing Genetic diversity Genome, Viral Genomes Health surveillance Humans Identification methods Likelihood Functions Mapping Models, Genetic Mutation Rate Network analysis phylogenetic cluster Phylogenetics Phylogeny Pneumonia, Viral - epidemiology Pneumonia, Viral - transmission Prevention Public health SARS-CoV-2 Statistical inference Strains (organisms) Surveillance Thailand time to most recent common ancestor TMRCA transmission cluster United States Viral diseases Virology Viruses |
title | Transmission dynamics and evolutionary history of 2019‐nCoV |
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