Możesz dodawać lokalizację do Twoich Tweetów, jak miasto czy konkretne miejsce, z sieci lub innych aplikacji. W każdej chwili możesz usunąć historię lokalizacji swoich Tweetów. Dowiedz się więcej
There is increasing interest in superspreading for COVID-19, not least due to an excellent article by @kakape on the topic 1/9https://www.sciencemag.org/news/2020/05/why-do-some-covid-19-patients-infect-many-others-whereas-most-don-t-spread-virus-all …
Mathematically, we are interested in the variation of the number of secondary cases that are generated by one infected index case. This variation can be described by a negative binomial distribution using the dispersion parameter k. 2/9
Small values of k mean that only a few infected individuals contribute to most transmissions. Ebola, SARS and MERS have k values around 0.2. Assuming R0 = 2, this would result in 62% of cases not transmitting at all, while around 15% of cases cause 80% of transmissions. 3/9
High values of k mean steadier transmission without much superspreading. For example, k for influenza is thought to be around 1. Assuming R0 = 2, this would result in 33% of cases not transmitting at all, while around 40% of cases cause 80% of transmissions. 4/9
So what is k for COVID-19? In January, @JulRiou and I estimated a median k = 0.54. There was still considerably uncertainty and it remained unclear whether the transmission characteristics were more akin to SARS or influenza. 5/9 https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.4.2000058 …
A more recent study from @JustinLessler and colleagues using data from Shenzhen, China, estimated a similar k of 0.58. 6/9https://www.thelancet.com/journals/laninf/article/PIIS1473-3099(20)30287-5/fulltext …
Another study from @sbfunk, @AdamJKucharski and colleagues obtain a k around 0.1. https://wellcomeopenresearch.org/articles/5-67 A quick analysis of a published transmission cluster from Austria results in k = 0.16. 7/9 https://www.ages.at/service/service-presse/pressemeldungen/epidemiologische-abklaerung-am-beispiel-covid-19/ …
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
Could k be changed by interventions?: e.g. stay at home orders inflating k by both reducing super-spreader events, but also increasing probability of spreading to family/roommates.
Obesity induces a person to shed virus 40-100% longer duration. Obesity would effect the k value immensely.
Do we know how much of this over dispersion to account to the intrinsic factors of the individual (e.g. shedding) versus extrinsic factors having to do with the venue and the contact frequency?
Twitter jest przeciążony lub wystąpił chwilowy problem. Spróbuj ponownie lub sprawdź status Twittera, aby uzyskać więcej informacji.
