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Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations

A novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China. This study aims to characterize key attributes of SARS-CoV-2 epidemiology as the infection emerged in China. An age-stratified mathematical model was constructed to describe transmission dynam...

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Autores principales: Ayoub, Houssein H., Chemaitelly, Hiam, Mumtaz, Ghina R., Seedat, Shaheen, Awad, Susanne F., Makhoul, Monia, Abu-Raddad, Laith J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Authors. Published by Elsevier Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673212/
https://www.ncbi.nlm.nih.gov/pubmed/33235991
http://dx.doi.org/10.1016/j.gloepi.2020.100042
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author Ayoub, Houssein H.
Chemaitelly, Hiam
Mumtaz, Ghina R.
Seedat, Shaheen
Awad, Susanne F.
Makhoul, Monia
Abu-Raddad, Laith J.
author_facet Ayoub, Houssein H.
Chemaitelly, Hiam
Mumtaz, Ghina R.
Seedat, Shaheen
Awad, Susanne F.
Makhoul, Monia
Abu-Raddad, Laith J.
author_sort Ayoub, Houssein H.
collection PubMed
description A novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China. This study aims to characterize key attributes of SARS-CoV-2 epidemiology as the infection emerged in China. An age-stratified mathematical model was constructed to describe transmission dynamics and estimate age-specific differences in biological susceptibility to infection, age-assortativeness in transmission mixing, and transition in rate of infectious contacts (and reproduction number R(0)) following introduction of mass interventions. The model estimated the infectious contact rate in early epidemic at 0.59 contacts/day (95% uncertainty interval-UI = 0.48–0.71). Relative to those 60–69 years, susceptibility was 0.06 in those ≤19 years, 0.34 in 20–29 years, 0.57 in 30–39 years, 0.69 in 40–49 years, 0.79 in 50–59 years, 0.94 in 70–79 years, and 0.88 in ≥80 years. Assortativeness in transmission mixing by age was limited at 0.004 (95% UI = 0.002–0.008). R(0) rapidly declined from 2.1 (95% UI = 1.8–2.4) to 0.06 (95% UI = 0.05–0.07) following interventions' onset. Age appears to be a principal factor in explaining the transmission patterns in China. The biological susceptibility to infection seems limited among children but high among those >50 years. There was no evidence for differential contact mixing by age.
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spelling pubmed-76732122020-11-19 Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations Ayoub, Houssein H. Chemaitelly, Hiam Mumtaz, Ghina R. Seedat, Shaheen Awad, Susanne F. Makhoul, Monia Abu-Raddad, Laith J. Glob Epidemiol Research Paper A novel coronavirus strain, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in China. This study aims to characterize key attributes of SARS-CoV-2 epidemiology as the infection emerged in China. An age-stratified mathematical model was constructed to describe transmission dynamics and estimate age-specific differences in biological susceptibility to infection, age-assortativeness in transmission mixing, and transition in rate of infectious contacts (and reproduction number R(0)) following introduction of mass interventions. The model estimated the infectious contact rate in early epidemic at 0.59 contacts/day (95% uncertainty interval-UI = 0.48–0.71). Relative to those 60–69 years, susceptibility was 0.06 in those ≤19 years, 0.34 in 20–29 years, 0.57 in 30–39 years, 0.69 in 40–49 years, 0.79 in 50–59 years, 0.94 in 70–79 years, and 0.88 in ≥80 years. Assortativeness in transmission mixing by age was limited at 0.004 (95% UI = 0.002–0.008). R(0) rapidly declined from 2.1 (95% UI = 1.8–2.4) to 0.06 (95% UI = 0.05–0.07) following interventions' onset. Age appears to be a principal factor in explaining the transmission patterns in China. The biological susceptibility to infection seems limited among children but high among those >50 years. There was no evidence for differential contact mixing by age. The Authors. Published by Elsevier Inc. 2020-11 2020-11-18 /pmc/articles/PMC7673212/ /pubmed/33235991 http://dx.doi.org/10.1016/j.gloepi.2020.100042 Text en © 2020 The Authors Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Research Paper
Ayoub, Houssein H.
Chemaitelly, Hiam
Mumtaz, Ghina R.
Seedat, Shaheen
Awad, Susanne F.
Makhoul, Monia
Abu-Raddad, Laith J.
Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
title Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
title_full Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
title_fullStr Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
title_full_unstemmed Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
title_short Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
title_sort characterizing key attributes of covid-19 transmission dynamics in china's original outbreak: model-based estimations
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7673212/
https://www.ncbi.nlm.nih.gov/pubmed/33235991
http://dx.doi.org/10.1016/j.gloepi.2020.100042
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