Shrinkage in serial intervals across transmission generations of COVID-19.

COVID-19 Contact tracing Serial interval Statistical modelling Transmission generation

Journal

Journal of theoretical biology
ISSN: 1095-8541
Titre abrégé: J Theor Biol
Pays: England
ID NLM: 0376342

Informations de publication

Date de publication:
21 11 2021
Historique:
received: 13 03 2021
revised: 06 08 2021
accepted: 08 08 2021
pubmed: 15 8 2021
medline: 10 9 2021
entrez: 14 8 2021
Statut: ppublish

Résumé

One of the key epidemiological characteristics that shape the transmission of coronavirus disease 2019 (COVID-19) is the serial interval (SI). Although SI is commonly considered following a probability distribution at a population scale, recent studies reported a slight shrinkage (or contraction) of the mean of effective SI across transmission generations or over time. Here, we develop a likelihood-based statistical inference framework with truncation to explore the change in SI across transmission generations after adjusting the impacts of case isolation. The COVID-19 contact tracing surveillance data in Hong Kong are used for exemplification. We find that for COVID-19, the mean of individual SI is likely to shrink with a factor at 0.72 per generation (95%CI: 0.54, 0.96) as the transmission generation increases, where a threshold may exist as the lower boundary of this shrinking process. We speculate that one of the probable explanations for the shrinkage in SI might be an outcome due to the competition among multiple candidate infectors within the same case cluster. Thus, the nonpharmaceutical interventive strategies are crucially important to block the transmission chains, and mitigate the COVID-19 epidemic.

Identifiants

pubmed: 34390731
pii: S0022-5193(21)00280-0
doi: 10.1016/j.jtbi.2021.110861
pmc: PMC8356772
pii:
doi:

Types de publication

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

Langues

eng

Sous-ensembles de citation

IM

Pagination

110861

Informations de copyright

Copyright © 2021 Elsevier Ltd. All rights reserved.

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Auteurs

Shi Zhao (S)

JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: zhaoshi.cmsa@gmail.com.

Yu Zhao (Y)

School of Public Health and Management, Ningxia Medical University, Yinchuan, Ningxia, China; Key Laboratory of Environmental Factors and Chronic Disease Control, Xingqing District, Yinchuan, Ningxia, China. Electronic address: zhaoyu@nxmu.edu.cn.

Biao Tang (B)

School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China; Laboratory for Industrial and Applied Mathematics, Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada. Electronic address: btang66@yorku.ca.

Daozhou Gao (D)

Department of Mathematics, Shanghai Normal University, Shanghai, China. Electronic address: dzgao@shnu.edu.cn.

Zihao Guo (Z)

JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China. Electronic address: marc@cuhk.edu.hk.

Marc K C Chong (MKC)

JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: marc@cuhk.edu.hk.

Salihu S Musa (SS)

Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China; Department of Mathematics, Kano University of Science and Technology, Wudil, Nigeria. Electronic address: salihu-sabiu.musa@connect.polyu.hk.

Yongli Cai (Y)

School of Mathematics and Statistics, Huaiyin Normal University, Huaian, China. Electronic address: yonglicai@hytc.edu.cn.

Weiming Wang (W)

School of Mathematics and Statistics, Huaiyin Normal University, Huaian, China. Electronic address: weimingwang2003@163.com.

Daihai He (D)

Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China. Electronic address: daihai.he@polyu.edu.hk.

Maggie H Wang (MH)

JC School of Public Health and Primary Care, Chinese University of Hong Kong, Hong Kong, China; CUHK Shenzhen Research Institute, Shenzhen, China. Electronic address: maggiew@cuhk.edu.hk.

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