Abstract
Coronavirus disease 2019 (COVID-19) was first identified in late 2019 in Wuhan, Hubei Province, China and spread globally in months, sparking worldwide concern. However, it is unclear whether super-spreading events occurred during the early outbreak phase, as has been observed for other emerging viruses. Here, we analyse 208 publicly available SARS-CoV-2 genome sequences collected during the early outbreak phase. We combine phylogenetic analysis with Bayesian inference under an epidemiological model to trace person-to-person transmission. The dispersion parameter of the offspring distribution in the inferred transmission chain was estimated to be 0.23 (95% CI: 0.13–0.38), indicating there are individuals who directly infected a disproportionately large number of people. Our results showed that super-spreading events played an important role in the early stage of the COVID-19 outbreak.
Highlights
Coronavirus disease 2019 (COVID-19) was first identified in late 2019 in Wuhan, Hubei Province, China and spread globally in months, sparking worldwide concern
Some sporadic reports suggested that SSEs may have occurred under certain circumstances[12,13,14], it is still unknown whether SSEs played a role during the early phase of the COVID-19 pandemic
There was considerable uncertainty in the inferred transmission tree, which is not shown in the medoid tree but is explored by the Markov Chain Monte Carlo (MCMC)
Summary
Coronavirus disease 2019 (COVID-19) was first identified in late 2019 in Wuhan, Hubei Province, China and spread globally in months, sparking worldwide concern It is unclear whether super-spreading events occurred during the early outbreak phase, as has been observed for other emerging viruses. In 2005, Lloyd-Smith et al.[11] proposed an “individual reproductive number” (denoted as ν), representing the number of secondary cases caused by a particular infected individual, which was drawn from a continuous probability distribution with mean R0 In this framework, specific SSEs are events from the right tail of the distribution of ν and propensity for SSEs can be identified by estimating the skewness of the distribution of ν. These findings provide an important basis for guiding the development of prevention and control policies, especially for countries at the early stages of the COVID-19 pandemic
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