The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions.


Journal

PLoS computational biology
ISSN: 1553-7358
Titre abrégé: PLoS Comput Biol
Pays: United States
ID NLM: 101238922

Informations de publication

Date de publication:
12 2020
Historique:
received: 24 07 2020
accepted: 26 10 2020
revised: 08 01 2021
pubmed: 29 12 2020
medline: 20 1 2021
entrez: 28 12 2020
Statut: epublish

Résumé

In January 2020, a COVID-19 outbreak was detected in Sichuan Province of China. Six weeks later, the outbreak was successfully contained. The aim of this work is to characterize the epidemiology of the Sichuan outbreak and estimate the impact of interventions in limiting SARS-CoV-2 transmission. We analyzed patient records for all laboratory-confirmed cases reported in the province for the period of January 21 to March 16, 2020. To estimate the basic and daily reproduction numbers, we used a Bayesian framework. In addition, we estimated the number of cases averted by the implemented control strategies. The outbreak resulted in 539 confirmed cases, lasted less than two months, and no further local transmission was detected after February 27. The median age of local cases was 8 years older than that of imported cases. We estimated R0 at 2.4 (95% CI: 1.6-3.7). The epidemic was self-sustained for about 3 weeks before going below the epidemic threshold 3 days after the declaration of a public health emergency by Sichuan authorities. Our findings indicate that, were the control measures be adopted four weeks later, the epidemic could have lasted 49 days longer (95% CI: 31-68 days), causing 9,216 more cases (95% CI: 1,317-25,545).

Identifiants

pubmed: 33370263
doi: 10.1371/journal.pcbi.1008467
pii: PCOMPBIOL-D-20-01325
pmc: PMC7794025
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

e1008467

Déclaration de conflit d'intérêts

H.Y. has received research funding from Sanofi Pasteur, GlaxoSmithKline, Yichang HEC Changjiang Pharmaceutical Company, and Shanghai Roche Pharmaceutical Company. A.V. reports grants from Metabiota inc., outside the submitted work. M.A. has received research funding from Seqirus. None of those research funding is related to COVID-19. All other authors report no competing interests.

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Auteurs

Quan-Hui Liu (QH)

College of Computer Science, Sichuan University, Chengdu, China.

Ana I Bento (AI)

Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America.

Kexin Yang (K)

College of Computer Science, Sichuan University, Chengdu, China.

Hang Zhang (H)

College of Computer Science, Sichuan University, Chengdu, China.

Xiaohan Yang (X)

Department of Engineering and Computer Science, New York University Shanghai, Shanghai, China.
Institution of New Economic Development, Chengdu, China.

Stefano Merler (S)

Bruno Kessler Foundation, Trento, Italy.

Alessandro Vespignani (A)

Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts United States of America.
ISI Foundation, Turin, Italy.

Jiancheng Lv (J)

College of Computer Science, Sichuan University, Chengdu, China.

Hongjie Yu (H)

School of Public Health, Fudan University, Key Laboratory of Public Health Safety, Ministry of Education, Shanghai, China.

Wei Zhang (W)

West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.

Tao Zhou (T)

Big Data Research Center, University of Electronic Science and Technology of China, Chengdu, China.
Tianfu Complexity Science Research Center, Chengdu, China.

Marco Ajelli (M)

Department of Epidemiology and Biostatistics, School of Public Health, Indiana University Bloomington, Bloomington, Indiana, United States of America.
Laboratory for the Modeling of Biological and Socio-technical Systems, Northeastern University, Boston, Massachusetts United States of America.

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