Timing the SARS-CoV-2 index case in Hubei province.


Journal

Science (New York, N.Y.)
ISSN: 1095-9203
Titre abrégé: Science
Pays: United States
ID NLM: 0404511

Informations de publication

Date de publication:
23 04 2021
Historique:
received: 20 11 2020
accepted: 15 03 2021
pubmed: 20 3 2021
medline: 1 5 2021
entrez: 19 3 2021
Statut: ppublish

Résumé

Understanding when severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged is critical to evaluating our current approach to monitoring novel zoonotic pathogens and understanding the failure of early containment and mitigation efforts for COVID-19. We used a coalescent framework to combine retrospective molecular clock inference with forward epidemiological simulations to determine how long SARS-CoV-2 could have circulated before the time of the most recent common ancestor of all sequenced SARS-CoV-2 genomes. Our results define the period between mid-October and mid-November 2019 as the plausible interval when the first case of SARS-CoV-2 emerged in Hubei province, China. By characterizing the likely dynamics of the virus before it was discovered, we show that more than two-thirds of SARS-CoV-2-like zoonotic events would be self-limited, dying out without igniting a pandemic. Our findings highlight the shortcomings of zoonosis surveillance approaches for detecting highly contagious pathogens with moderate mortality rates.

Identifiants

pubmed: 33737402
pii: science.abf8003
doi: 10.1126/science.abf8003
pmc: PMC8139421
doi:

Banques de données

Dryad
['10.5061/dryad.4f4qrfjbm']

Types de publication

Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

412-417

Subventions

Organisme : NIAID NIH HHS
ID : R01 AI135992
Pays : United States

Commentaires et corrections

Type : UpdateOf

Informations de copyright

Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

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Auteurs

Jonathan Pekar (J)

Bioinformatics and Systems Biology Graduate Program, University of California San Diego, La Jolla, CA 92093, USA.
Department of Biomedical Informatics, University of California San Diego, La Jolla, CA 92093, USA.

Michael Worobey (M)

Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ 85721, USA. worobey@arizona.edu jwertheim@health.ucsd.edu.

Niema Moshiri (N)

Department of Computer Science and Engineering, University of California San Diego, La Jolla, CA 92093, USA.

Konrad Scheffler (K)

Illumina, Inc., San Diego, CA 92122, USA.

Joel O Wertheim (JO)

Department of Medicine, University of California San Diego, La Jolla, CA 92093, USA. worobey@arizona.edu jwertheim@health.ucsd.edu.

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Classifications MeSH