Publication
Superspreading and Temporal Dynamics of COVID-19 Transmission: Insights from Transmission Settings and Case Detection in Shenzhen
A dual-dimension clustering analysis framework for understanding COVID-19 transmission heterogeneity and super-spreading events in Shenzhen.
- Date
- June 1, 2025
- Venue
- Emerging Infectious Diseases (SCI Q2 Top, Under Review)
This paper proposes a novel “transmission setting - case detection mode” dual-dimension clustering analysis framework to systematically analyze transmission chain data of 1,329 COVID-19 cases in Shenzhen.
Key Contributions:
- Developed dual-dimension clustering analysis framework
- Quantified transmission heterogeneity using negative binomial distribution model
- Estimated serial intervals (SI) under different scenarios
Key Findings:
- 13.7% of transmission chains caused 80% of total infections (super-spreading phenomenon)
- Identified transportation (k = 0.10) as high-risk environment
- Active surveillance measures can shorten SI to approximately 1.6 days
(*equal contribution, †corresponding author)