Genomic epidemiology of a densely sampled COVID-19 outbreak in China
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Published version
Author(s)
Type
Journal Article
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 February 4th. 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.
Date Issued
2021-01
Date Acceptance
2020-12-08
Citation
Virus Evolution, 2021, 7 (1), pp.1-7
ISSN
2057-1577
Publisher
Oxford University Press (OUP)
Start Page
1
End Page
7
Journal / Book Title
Virus Evolution
Volume
7
Issue
1
Copyright Statement
© The Author(s) 2021. Published by Oxford University Press.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
License URL
Sponsor
Medical Research Council (MRC)
Medical Research Council
Identifier
https://academic.oup.com/ve/article/7/1/veaa102/6170691
Grant Number
MR/R015600/1
MR/R015600/1
Subjects
SARS-CoV-2
genetic epidemiology
modelling
phylodynamics
phylogenetics
structured coalescent
0603 Evolutionary Biology
0605 Microbiology
Publication Status
Published
Date Publish Online
2021-03-14