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Genomic epidemiology of a densely sampled COVID-19 outbreak in China.
Geidelberg, Lily; Boyd, Olivia; Jorgensen, David; Siveroni, Igor; Nascimento, Fabrícia F; Johnson, Robert; Ragonnet-Cronin, Manon; Fu, Han; Wang, Haowei; Xi, Xiaoyue; Chen, Wei; Liu, Dehui; Chen, Yingying; Tian, Mengmeng; Tan, Wei; Zai, Junjie; Sun, Wanying; Li, Jiandong; Li, Junhua; Volz, Erik M; Li, Xingguang; Nie, Qing.
Affiliation
  • Geidelberg L; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Boyd O; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Jorgensen D; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Siveroni I; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Nascimento FF; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Johnson R; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Ragonnet-Cronin M; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Fu H; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Wang H; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Xi X; Department of Mathematics, Imperial College London, London SW7 2AZ, UK.
  • Chen W; Department of Microbiology, Weifang Center for Disease Control and Prevention, Weifang 261061, China.
  • Liu D; Department of Microbiology, Weifang Center for Disease Control and Prevention, Weifang 261061, China.
  • Chen Y; Department of Microbiology, Weifang Center for Disease Control and Prevention, Weifang 261061, China.
  • Tian M; Department of Microbiology, Weifang Center for Disease Control and Prevention, Weifang 261061, China.
  • Tan W; Department of Respiratory Medicine, Weifang People's Hospital, Weifang 261061, China.
  • Zai J; Immunology Innovation Team, School of Medicine, Ningbo University, Ningbo 315211, China.
  • Sun W; Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen 518083, China.
  • Li J; Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen 518083, China.
  • Li J; Shenzhen Key Laboratory of Unknown Pathogen Identification, BGI-Shenzhen, Shenzhen 518083, China.
  • Volz EM; Department of Infectious Disease Epidemiology and MRC Centre for Global Infectious Disease Analysis, Imperial College London, Norfolk Place W2 1PG, UK.
  • Li X; Department of Hospital Office, The First People's Hospital of Fangchenggang, Fangchenggang, 538021, China.
  • Nie Q; Department of Microbiology, Weifang Center for Disease Control and Prevention, Weifang 261061, China.
Virus Evol ; 7(1): veaa102, 2021 Jan.
Article in En | MEDLINE | ID: mdl-33747543
Analysis of genetic sequence data from the SARS-CoV-2 pandemic can provide insights into epidemic origins, worldwide dispersal, and epidemiological history. With few exceptions, genomic epidemiological analysis has focused on geographically distributed data sets with few isolates in any given location. Here, we report an analysis of 20 whole SARS- CoV-2 genomes from a single relatively small and geographically constrained outbreak in Weifang, People's Republic of China. Using Bayesian model-based phylodynamic methods, we estimate a mean basic reproduction number (R 0) of 3.4 (95% highest posterior density interval: 2.1-5.2) in Weifang, and a mean effective reproduction number (Rt) that falls below 1 on 4 February. We further estimate the number of infections through time and compare these estimates to confirmed diagnoses by the Weifang Centers for Disease Control. We find that these estimates are consistent with reported cases and there is unlikely to be a large undiagnosed burden of infection over the period we studied.
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Screening_studies Language: En Journal: Virus Evol Year: 2021 Document type: Article Country of publication: United kingdom

Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies / Screening_studies Language: En Journal: Virus Evol Year: 2021 Document type: Article Country of publication: United kingdom