This paper has recently been published, and is stoking headlines that tens of millions of people have been infected with #COVID19 in Britain already
It also contains numerous mathematical and epidemiological errors
This is worrying
https://twitter.com/DrAdrianHeald/status/1260951024954638337 …
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Which brings us to the SECOND big mistake: collinearity Without delving into too much depth, it's bad statistical practice to regress two things that we know are very closely relatedpic.twitter.com/GibiB0Hffl
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In this case, the authors calculated a variable (R(ADIR)) from new case numbers, and then regressed it against case numbers THESE TWO VARIABLES WILL ALWAYS BE CORRELATED BECAUSE THEY ARE CALCULATED FROM THE SAME INFORMATION
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It's like regressing your age in years against the average age you've been over the last decade, and then shouting "Eureka!" when you find they are closely tied Again, a simple mistake, and something a first-year stats student is taught not to do
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The THIRD big mistake is epidemiological. The authors assumed - with no evidence whatsoever - that an R(ADIR) of 0 meant immunity This is WRONGpic.twitter.com/2eCmbzdVDH
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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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