10. And I guess now he's falling back on the old "It's been peer reviewed" defense. Well, Adrian, consider this a post-publication peer review.pic.twitter.com/ilGakKlflT
Complex systems, wicked problems. Society, technology, science and more. @UNC professor. @NYTimes columnist. My newsletter is @insight: http://www.theinsight.org
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10. And I guess now he's falling back on the old "It's been peer reviewed" defense. Well, Adrian, consider this a post-publication peer review.pic.twitter.com/ilGakKlflT
11. Oddly enough, I do know a little bit about peer review. In addition to writing a thousand of them or so in my career, I've written a little bit about peer review and what it does and does not guarantee. https://callingbullshit.org/tools/tools_legit.html …pic.twitter.com/dELFXheXo2
12. OK, let' try to get back to the paper. I'm really struggling to understand what is going on here. This doesn't look like any infectious disease epidemiological method I've ever seen, and there's no citation given. But I can try to reconstruct the thought process.pic.twitter.com/S4GuiZZzRo
13. And as always, I welcome corrections from any authors that haven't blocked me already. The idea seems to be to extrapolate to figure out how many cases per capita would be reported by the time you reach R=0.
14. And this turns out to be about 400,000. Yet there are 60M+ in the UK, which gives a scaling of at least 150 cases per reported cases if everyone gets infected.
15. I have SO MANY questions. Just a few of them: 1) What is the causal basis for the relation between R and reported cases? Is this susceptible depletion? Something behavioral? Or is the claim it doesn't matter? 2) Given (1), why can you extrapolate and why linearly?
16. 3) Why extrapolate to everyone being infected instead of the final epidemic size or even herd immunity threshold? 4) The regression shown accounts for only a modest fraction of the variation. How does the remaining variation impact the predictions?
17. 5) Testing effort varies over time and across locations. How does this play into the estimation procedure? 6) To extrapolate in this way implicitly assumes all regions will follow the same trajectory given enough time. How reasonable is that?
18. And then we get to the conclusion: the authors think COVID19 is about 1/5th as deadly as most others think, and about 5-10 times as prevalent. I.e., we're much closer to herd immunity than we thought and the cost of getting there is much lower.pic.twitter.com/HiAmtt3vq1
19. In the discussion the authors suggest their regression reveals susceptible depletion. But the historic number of confirmed cases is likely influenced by the same common causes that influence declines in R, e.g. control measures. This is a huge causal inference failure.pic.twitter.com/Xu1VI4llJH
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