Genomic epidemiology of a densely sampled COVID-19 outbreak in China

Lily Geidelberg; Olivia Boyd; David Jorgensen; Igor Siveroni; Fabrícia F. Nascimento; Robert Johnson; Manon Ragonnet-Cronin; Han Fu; Haowei Wang; Xiaoyue Xi; Wei Chen; Dehui Liu; Yingying Chen; Mengmeng Tian; Wei Tan; Junjie Zai; Wanying Sun; Jiandong Li; Junhua Li; Erik M. Volz; Xingguang Li; Qing Nie

doi:10.1093/ve/veaa102

Genomic epidemiology of a densely sampled COVID-19 outbreak in China

Lily Geidelberg, Olivia Boyd, David Jorgensen, Igor Siveroni, Fabrícia F. Nascimento, Robert Johnson, Manon Ragonnet-Cronin, Han Fu, Haowei Wang, Xiaoyue Xi, Wei Chen, Dehui Liu, Yingying Chen, Mengmeng Tian, Wei Tan, Junjie Zai, Wanying Sun, Jiandong Li, Junhua Li, Erik M. VolzXingguang Li, Qing Nie

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

18 Citas (Scopus)

Resumen

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 (R0) 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.

Idioma original	Inglés
Número de artículo	veaa102
Publicación	Virus Evolution
Volumen	7
N.º	1
DOI	https://doi.org/10.1093/ve/veaa102
Estado	Publicada - 1 ene. 2021
Publicado de forma externa	Sí

Acceder al documento

10.1093/ve/veaa102

Otros archivos y enlaces

Enlace a la publicación en Scopus

Citar esto

Geidelberg, L., Boyd, O., Jorgensen, D., Siveroni, I., Nascimento, F. F., Johnson, R., Ragonnet-Cronin, M., Fu, H., Wang, H., Xi, X., Chen, W., Liu, D., Chen, Y., Tian, M., Tan, W., Zai, J., Sun, W., Li, J., Li, J., ... Nie, Q. (2021). Genomic epidemiology of a densely sampled COVID-19 outbreak in China. Virus Evolution, 7(1), Artículo veaa102. https://doi.org/10.1093/ve/veaa102

@article{adbaa20ccd534958b096de4d9efa85a0,

title = "Genomic epidemiology of a densely sampled COVID-19 outbreak in China",

abstract = "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 (R0) 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.",

keywords = "SARS-CoV-2, genetic epidemiology, modelling, phylodynamics, phylogenetics, structured coalescent",

author = "Lily Geidelberg and Olivia Boyd and David Jorgensen and Igor Siveroni and Nascimento, {Fabr{\'i}cia F.} and Robert Johnson and Manon Ragonnet-Cronin and Han Fu and Haowei Wang and Xiaoyue Xi and Wei Chen and Dehui Liu and Yingying Chen and Mengmeng Tian and Wei Tan and Junjie Zai and Wanying Sun and Jiandong Li and Junhua Li and Volz, {Erik M.} and Xingguang Li and Qing Nie",

note = "Publisher Copyright: {\textcopyright} 2021 The Author(s) 2021. Published by Oxford University Press.",

year = "2021",

month = jan,

day = "1",

doi = "10.1093/ve/veaa102",

language = "English",

volume = "7",

journal = "Virus Evolution",

issn = "2057-1577",

number = "1",

}

Geidelberg, L, Boyd, O, Jorgensen, D, Siveroni, I, Nascimento, FF, Johnson, R, Ragonnet-Cronin, M, Fu, H, Wang, H, Xi, X, Chen, W, Liu, D, Chen, Y, Tian, M, Tan, W, Zai, J, Sun, W, Li, J, Li, J, Volz, EM, Li, X & Nie, Q 2021, 'Genomic epidemiology of a densely sampled COVID-19 outbreak in China', Virus Evolution, vol. 7, n.º 1, veaa102. https://doi.org/10.1093/ve/veaa102

TY - JOUR

T1 - Genomic epidemiology of a densely sampled COVID-19 outbreak in China

AU - Geidelberg, Lily

AU - Boyd, Olivia

AU - Jorgensen, David

AU - Siveroni, Igor

AU - Nascimento, Fabrícia F.

AU - Johnson, Robert

AU - Ragonnet-Cronin, Manon

AU - Fu, Han

AU - Wang, Haowei

AU - Xi, Xiaoyue

AU - Chen, Wei

AU - Liu, Dehui

AU - Chen, Yingying

AU - Tian, Mengmeng

AU - Tan, Wei

AU - Zai, Junjie

AU - Sun, Wanying

AU - Li, Jiandong

AU - Li, Junhua

AU - Volz, Erik M.

AU - Li, Xingguang

AU - Nie, Qing

PY - 2021/1/1

Y1 - 2021/1/1

N2 - 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 (R0) 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.

AB - 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 (R0) 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.

KW - SARS-CoV-2

KW - genetic epidemiology

KW - modelling

KW - phylodynamics

KW - phylogenetics

KW - structured coalescent

UR - http://www.scopus.com/inward/record.url?scp=85104240691&partnerID=8YFLogxK

U2 - 10.1093/ve/veaa102

DO - 10.1093/ve/veaa102

M3 - Article

AN - SCOPUS:85104240691

SN - 2057-1577

VL - 7

JO - Virus Evolution

JF - Virus Evolution

IS - 1

M1 - veaa102

ER -

Genomic epidemiology of a densely sampled COVID-19 outbreak in China

Resumen

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto