So I've seen these numbers thrown around a lot
I think this analysis is pretty flawed and very likely to be wrong
Some reasons #COVID19 https://twitter.com/ToryFibs/status/1242561095761637376 …
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What did the authors do? They fitted a fairly standard model of epidemics (SIR = Susceptible, Infected, Recovered) looking at the number of infections and deaths in the UK to see how many cases there might be in the populationpic.twitter.com/mqDdDvHQUP
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However, there was a key assumption in the model that makes it problematic The scientists assumed that only ~1% of people who caught
#COVID19 would be hospitalizedpic.twitter.com/VNgtbjFzDr
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Is this assumption reasonable? In my opinion, no. Even the sources cited in this paper don't agree with their key assumption, like WHO joint report into COVID-19 in Chinapic.twitter.com/BS07T4omP0
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If you assume that only 1% of people get hospitalized due to
#COVID19, the models follow pretty simply I.e. if 10,000 people are hospitalized, 1 million people would have the disease1 reply 0 retweets 3 likesShow this thread -
And despite the fancy language, that's essentially what the authors did. The SIR model they produced is mostly a function of this one assumption
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Now, the authors actually had a good reason for this. They are arguing that it is POSSIBLE that this is true, and we need more testing to ensure that it is not the casepic.twitter.com/hnHwT37Zlg
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However, that is not the message that the media - and most of twitter - have picked up onpic.twitter.com/gsTAdqDlQ3
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Based on all published evidence to date, it is INCREDIBLY UNLIKELY that the hospitalization rate is only 1% for #COVID19 infections
The true rate appears to be quite a bit higherpic.twitter.com/pygB0sP5ay
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What all this noise really shows is how one small assumption can completely change the predicted results of a model
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