Characterizing key attributes of COVID-19 transmission dynamics in China's original outbreak: Model-based estimations
Author | Ayoub, Houssein H. |
Author | Chemaitelly, Hiam |
Author | Mumtaz, Ghina R. |
Author | Seedat, Shaheen |
Author | Awad, Susanne F. |
Author | Makhoul, Monia |
Author | Abu-Raddad, Laith J. |
Available date | 2025-03-13T07:43:58Z |
Publication Date | 2020 |
Publication Name | Global Epidemiology |
Resource | Scopus |
Identifier | http://dx.doi.org/10.1016/j.gloepi.2020.100042 |
ISSN | 25901133 |
Abstract | 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 R0) 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). R0 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. |
Sponsor | Funding text 1: The modeling infrastructure was made possible by NPRP grant number 9-040-3-008 from the Qatar National Research Fund (a member of Qatar Foundation). GM acknowledges support by UK Research and Innovation as part of the Global Challenges Research Fund, grant number ES/P010873/1. The statements made herein are solely the responsibility of the authors. The authors are also grateful for support provided by the Biostatistics, Epidemiology, and Biomathematics Research Core at Weill Cornell Medicine-Qatar. The publication of this article was funded by Qatar National Library.; Funding text 2: The modeling infrastructure was made possible by NPRP grant number 9-040-3-008 from the Qatar National Research Fund (a member of Qatar Foundation) . GM acknowledges support by UK Research and Innovation as part of the Global Challenges Research Fund , grant number ES/P010873/1 . The statements made herein are solely the responsibility of the authors. The authors are also grateful for support provided by the Biostatistics, Epidemiology, and Biomathematics Research Core at Weill Cornell Medicine-Qatar. The publication of this article was funded by Qatar National Library. |
Language | en |
Publisher | Elsevier |
Subject | China Coronavirus COVID-19 Epidemiology Mathematical model SARS-CoV-2 |
Type | Article |
Volume Number | 2 |
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