Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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Detail(s)
| Original language | English |
|---|---|
| Pages (from-to) | 411-417 |
| Journal / Publication | International Journal of Infectious Diseases |
| Volume | 116 |
| Online published | 21 Jan 2022 |
| Publication status | Published - Mar 2022 |
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| DOI | DOI |
|---|---|
| Attachment(s) | Documents
Publisher's Copyright Statement
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| Link to Scopus | https://www.scopus.com/record/display.uri?eid=2-s2.0-85124421139&origin=recordpage |
| Permanent Link | https://scholars.cityu.edu.hk/en/publications/publication(5075f5e3-5877-4432-9b53-c910784a51c9).html |
Abstract
Objectives: The aim of the study was to reconstruct the complete transmission chain of the COVID-19 outbreak in Beijing's Xinfadi Market using data from epidemiological investigations, which contributes to reflecting transmission dynamics and transmission risk factors.
Methods: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates.
Results: The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events.
Conclusions: The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures.
Methods: We set up a transmission model, and the model parameters are estimated from the survey data via Markov chain Monte Carlo sampling. Bayesian data augmentation approaches are used to account for uncertainty in the source of infection, unobserved onset, and infection dates.
Results: The rate of transmission of COVID-19 within households is 9.2%. Older people are more susceptible to infection. The accuracy of our reconstructed transmission chain was 67.26%. In the gathering place of this outbreak, the Beef and Mutton Trading Hall of Xinfadi market, most of the transmission occurs within 20 m, only 19.61% of the transmission occurs over a wider area (>20 m), with an overall average transmission distance of 13.00 m. The deepest transmission generation is 9. In this outbreak, there were 2 abnormally high transmission events.
Conclusions: The statistical method of reconstruction of transmission trees from incomplete epidemic data provides a valuable tool to help understand the complex transmission factors and provides a practical guideline for investigating the characteristics of the development of epidemics and the formulation of control measures.
Research Area(s)
Citation Format(s)
Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China. / Luo, Tianyi; Wang, Jiaojiao; Wang, Quanyi et al.
In: International Journal of Infectious Diseases, Vol. 116, 03.2022, p. 411-417.
In: International Journal of Infectious Diseases, Vol. 116, 03.2022, p. 411-417.
Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review
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