Reconstruction of the Transmission Chain of COVID-19 Outbreak in Beijing's Xinfadi Market, China. (March 2022)

• Record Type: Journal Article
• Authors: Luo, Tianyi; Wang, Jiaojiao; Wang, Quanyi; Wang, Xiaoli; Zhao, Pengfei; Zeng, Daniel Dajun; Zhang, Qingpeng; Cao, Zhidong
• Is Part Of: International journal of infectious diseases. Volume 116(2022)
• Page Start: 411
• Page End: 417
• Publication Date: 2022-03
• Subjects: Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2022.01.035
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: 1 Transmission chain of COVID-19 outbreak in Beijing was reconstructed. 2 The Bayesian data augmentation approaches account for uncertain data. 3 The risk of infection among different populations was quantified. 4 The source and captured transmission patterns of COVID-19 were effectively traced. 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.