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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Replying to @GidMK
Yes there are a fair number of problems with the paper, but your critique is off. The study is essentially trying to fit R(t) = R0 (1 - p) where p is proportion resistant using cumulative number of confirmed infections x as a surrogate for p, positing p = a x
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In both cases linearity is not an unreasonable assumption (your criticism 1), although one should be cautious that * 'a' may not be stable wrt time, as testing regimes change (to allow different dates to be compared, cumulative hospitalisations might be better)
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* 'p' might reflect proportion of an 'effective' actively mixing population, rather than the whole population * there could be several other confounding factors systematically relating the two variables But it's not an unreasonable relation to investigate.
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Replying to @heald_j
But that's not true. They do not calculate either R or R0, they calculate a completely different metric from case numbers and called it R which tbh is another serious problem with the paper
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Replying to @GidMK
Look more closely, and you'll find that they have constructed R_ADIR as an estimate of R(t)
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Replying to @heald_j
That's certainly what they argue, but it is at best an extremely vague estimate and definitely not a realistic calculation
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Replying to @GidMK
See their definition of R_ADIR It's not a very sophisticated estimator; & its associated date should be shifted to account for date of test usually being sometime after the date of end of incubation. But I can't see why it should be grossly off, even if Fig 2 doesn't look righ
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There's something to be said for seeing what a rough-and-ready close to the data estimate looks like. Even if more strongly model-driven estimates of R(t) might be better, it's useful to see how much the model is adjusting what the data would naively indicate
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Replying to @heald_j
Perhaps I'd agree if the rest of the paper wasn't as obviously flawed
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Also, if it's a rough and ready calculation it should a) be openly and clearly stated as such in the paper, b) not used for extrapolations without a very wide confidence bound and c) not be used by the lead author to generate a media circus about most of the UK being infected
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