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The value of k for COVID-19 remains unclear, and it will likely depend on social contact structures and environmental settings. I would argue that COVID-19 does transmit in a relatively steady and efficient way while also having the potential for superspreading. 8/9
Preventing these superspreading events from happening - and conducting thorough contact tracing should they happen - can play an important role in avoiding new flare ups of COVID-19. 9/9
How is superspreading defined? something like where over 50% of cases don't transmit the disease at all, or is it a set range of the k value?
it doesn't seem to me that k is independent of the number of small exposures a person is likely to encounter. k might be quite low when prevalence is low, but when prevalence is high and people are likely to have a large number of small exposures, k might be higher.
@threadreaderapp please unroll
@threadreaderapp unroll please
The sample size of the Shenzhen study is small enough that in my view it shouldn't be put on the same level. That study also makes other conclusions, e.g. "children are at a similar risk of infection to the general population" that aren't supported given its confidence intervals
Thanks Christian. Do you think that the estimates would appear lower in the absence of social distancing measures (since super-spreaders would be in contact with more people)?
Estimates of k do not necessarily depend on R. So if you prevent secondary cases > 10, you still obtain a characteristic distribution of secondary cases <= 10, e.g., a certain proportion of index cases who do not transmit at all.
Hi Christian, have you looked at the work from Cowling et al from HK and Nishiura etc al from Japan, which adds weight to the superspreading argument especially in indoor, poorly ventilated environments.
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