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Superspreading potential of SARS-CoV-2 across multiple infection generations
Hee Kyoung Kim1, Jun-Sik Lim2, Seonghui Cho3
1Division of Disease Control Capacity Building, Korea Disease Control and Prevention Agency, Cheongju, Republic of Korea; Department of Epidemiology and Health Informatics, Graduate School of Public Health, Korea University, Seoul, Republic of Korea.
Background:
Early during the coronavirus disease 2019 pandemic, the first patients to be identified as the source of infection in the clustered outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were labeled superspreaders in South Korea. However, the extent to which superspreading potential varies across different infection generations remains unclear. This study aims to estimate SARS-CoV-2 superspreading potentials across different infection generations from the largest clustered outbreak of the SARS-CoV-2 wild type in Seoul, South Korea, 2020.
Methods:
We retrieved the infector-infectee line list data on an outbreak occurred between May 6 and June 5, 2020. To evaluate the superspreading potential (k), we fitted the offspring distributions that had been acquired by counting the number of observed secondary infections for each case and each generation into negative binomial distributions.
Results:
The outbreak yielded a k value of 0.18 (95 % confidence interval [CI]: 0.13-0.27) and similar superspreading potentials across the different infection generations. Regarding the superspreading potential in each generation (i.e., individuals' heterogeneity across different generations), k was estimated to be 0.27 (95 % CI: 0.15-0.51), 0.14 (0.03-0.23), 0.10 (0.01-0.17), 0.55 (0.12-5.80 ×105), and 0.19 (0.07-0.73) for the first, second, third, fourth, and fifth or more generations, respectively.
Conclusions:
Our findings revealed no significant variations in the superspreading potential of SARS-CoV-2 across cases from different infection generations in the clustered outbreak. Assessing superspreading potential during the epidemic is necessary to refine epidemic modeling and enhance public health understanding of the transmission dynamic of epidemics.
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