I am not going to comment on the reasons for the mistakes, because I obviously have no idea why the authors chose to make them, but I think it's worth pointing out the very clear errors Maybe they'll be corrected
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Let's look at R(ADIR) It is basically the average of the ratio of new cases today to new cases over the last 5 days So where does immunity come in??? The authors don't say
pic.twitter.com/6uhb6rGvYp
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Thing is, we can quite easily see many reasons for R(ADIR) to be 0 - this simply means that there are no new cases today (essentially) The most likely reason for no new cases? SOCIAL DISTANCING
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Assuming - without any evidence whatsoever - that R(ADIR) = 0 means immunity is simply wrong It is possible (with a vaccine) that this could be the case, but it is by no means plausible
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On to the FOURTH big mistake: multiplying weirdly Basically they used that linear extrapolation to find that R(ADIR) = 0 when total cases/1,000 = 6.6, and then assumed that since this meant immunity, the other 993.4 people must've been exposed to COVID-19pic.twitter.com/MoH05TDwkZ
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This is nonsensical. Even taking their entire approach at face value, the correlation between these two variables was only r^2 = 0.22 It is, again, simply wrong to just multiply the values out like this, because quite clearly there is more going on
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There are numerous other errors in the study, but I think I've made my point If I were the author or the journal, I'd retract the study immediately But that's just me
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End of conversation
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This seems to be wrong from two perspectives. Not only is it wrong to assume that R(ADIR) = 0 means that all untested people have been exposed & are immune, it also assumes that exposure & recovery results in ongoing immunity. Is there evidence yet that people become immune?
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From my understanding, there is sufficient evidence for some immunity, but how long this will last and how protective it is is not certain
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