Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02139.
Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139.
Proc Natl Acad Sci U S A. 2020 Nov 24;117(47):29416-29418. doi: 10.1073/pnas.2018490117. Epub 2020 Nov 2.
Superspreaders, infected individuals who result in an outsized number of secondary cases, are believed to underlie a significant fraction of total SARS-CoV-2 transmission. Here, we combine empirical observations of SARS-CoV and SARS-CoV-2 transmission and extreme value statistics to show that the distribution of secondary cases is consistent with being fat-tailed, implying that large superspreading events are extremal, yet probable, occurrences. We integrate these results with interaction-based network models of disease transmission and show that superspreading, when it is fat-tailed, leads to pronounced transmission by increasing dispersion. Our findings indicate that large superspreading events should be the targets of interventions that minimize tail exposure.
超级传播者是指导致大量二次感染病例的感染者,被认为是导致大量 SARS-CoV-2 传播的原因之一。在这里,我们结合对 SARS-CoV 和 SARS-CoV-2 传播的经验观察和极值统计,表明二次感染病例的分布与长尾分布一致,这意味着大的超级传播事件是极端但可能发生的事件。我们将这些结果与基于相互作用的疾病传播网络模型相结合,表明当超级传播是长尾分布时,通过增加分散度会导致显著的传播。我们的研究结果表明,大规模的超级传播事件应该成为干预的目标,以最大限度地减少尾部暴露。