tetano
Editor, Senior Moderator
Biosaf Health
. 2025 Oct 5;7(6):395-402.
doi: 10.1016/j.bsheal.2025.09.010. eCollection 2025 Dec.
Attribution of super-spreaders to the COVID-19 outbreak
Rui Wang[SUP] 1 [/SUP], Jianping Huang[SUP] 1 [/SUP], Xinbo Lian[SUP] 1 [/SUP], Yingjie Zhao[SUP] 1 [/SUP], Shujuan Hu[SUP] 1 [/SUP], Beidou Zhang[SUP] 1 [/SUP]
Affiliations
The explosive growth during the early stages and the sustained transmission in the later phases of the coronavirus disease 2019 (COVID-19) pandemic may be closely linked to superspreading events (SSEs), yet in-depth research into their specific mechanisms and quantitative effects remains limited. This study, based on data from 4,519 COVID-19 cases across eight regions in China, reconstructed transmission chains and quantified key parameters such as the basic reproduction number (R [SUB]0[/SUB]) and dispersion parameter (k), revealing a high degree of heterogeneity in COVID-19 transmission. The results showed that the majority of COVID-19 cases were mild, with female cases in some regions being significantly older than males. Epidemic curves were highly similar in geographically proximal areas, with the longest transmission chain reaching nine generations. The transmission parameters revealed a serial interval of 1.27-4.71 days, R [SUB]0[/SUB] ranging from 0.87 to 2.65, and k values between 0.50-2.04, demonstrating that super-spreaders serve as critical drivers of epidemic spread. We found that 1.35 % of cases identified as super-spreaders directly responsible for 40.09 % of secondary cases. Occupationally, students and catering staff were identified as high-risk groups for super-spreading. Geographically, household or community transmission served as the main driver of SSEs in six regions, while school-based transmission dominated in one region. These findings provide crucial scientific evidence for advancing our understanding of COVID-19 transmission dynamics and informing precision prevention strategies.
Keywords: Coronavirus disease 2019 (COVID-19); Dispersion parameter; Reproduction number; Superspreading events (SSEs); Transmission chain.
. 2025 Oct 5;7(6):395-402.
doi: 10.1016/j.bsheal.2025.09.010. eCollection 2025 Dec.
Attribution of super-spreaders to the COVID-19 outbreak
Rui Wang[SUP] 1 [/SUP], Jianping Huang[SUP] 1 [/SUP], Xinbo Lian[SUP] 1 [/SUP], Yingjie Zhao[SUP] 1 [/SUP], Shujuan Hu[SUP] 1 [/SUP], Beidou Zhang[SUP] 1 [/SUP]
Affiliations
- PMID: 41541058
- PMCID: PMC12802358
- DOI: 10.1016/j.bsheal.2025.09.010
The explosive growth during the early stages and the sustained transmission in the later phases of the coronavirus disease 2019 (COVID-19) pandemic may be closely linked to superspreading events (SSEs), yet in-depth research into their specific mechanisms and quantitative effects remains limited. This study, based on data from 4,519 COVID-19 cases across eight regions in China, reconstructed transmission chains and quantified key parameters such as the basic reproduction number (R [SUB]0[/SUB]) and dispersion parameter (k), revealing a high degree of heterogeneity in COVID-19 transmission. The results showed that the majority of COVID-19 cases were mild, with female cases in some regions being significantly older than males. Epidemic curves were highly similar in geographically proximal areas, with the longest transmission chain reaching nine generations. The transmission parameters revealed a serial interval of 1.27-4.71 days, R [SUB]0[/SUB] ranging from 0.87 to 2.65, and k values between 0.50-2.04, demonstrating that super-spreaders serve as critical drivers of epidemic spread. We found that 1.35 % of cases identified as super-spreaders directly responsible for 40.09 % of secondary cases. Occupationally, students and catering staff were identified as high-risk groups for super-spreading. Geographically, household or community transmission served as the main driver of SSEs in six regions, while school-based transmission dominated in one region. These findings provide crucial scientific evidence for advancing our understanding of COVID-19 transmission dynamics and informing precision prevention strategies.
Keywords: Coronavirus disease 2019 (COVID-19); Dispersion parameter; Reproduction number; Superspreading events (SSEs); Transmission chain.