Superspreading and Temporal Dynamics of COVID-19 Transmission: Insights from Transmission Settings and Case Detection in Shenzhen

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)