10. I hate to ascribe to malice what can be adequately explained by incompetence, but using this lie to sweep away the disaggregated data is such utter nonsense that I wonder how a silicon valley guy could make this claim by mistake.
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11. Then there's the bell curve business. If Hernstein and Murray gave the term a bad name, Ginn says "hold my beer". Most things in nature follow a bell curve, so viruses do too? Not science exactly. And do most things? What about log-normals? Exponentials? Etc etc etc.pic.twitter.com/D21jHAQ7s6
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12. But that's not the worst part. We have literally over a century's history of mathematical modeling epidemic progression. Some look somewhat bell-like. Others don't. It depends on the circumstances, details of the virus, behavior of the population, interventions, etc.
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13. [pause to take beta-blockers]
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14. This is unsubstantiated bullshit. IF the bell-curve were a "law of nature", it shouldn't necessarily apply to the vast range of human responses that people take to stop epidemics. Yet this assertion is supported with data from places where interventions slowed things down.pic.twitter.com/ASrkdKzj6p
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15. Wait, are already breaking the data down by country? We were cautioned against that as being misleading just a few paragraphs ago!
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16. Ah, Farr's law. I don't know how the author could have more effectively discredited himself to the epidemiology community with any two other words. It's an old rule-of-thumb that suggests epidemics take a bell-curve shape. BUT....pic.twitter.com/QOWxeUzQmT
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17. When I teach ID epidemiology OR data science, I tend to have my students read this 1990 paper as a cautionary tail against non-mechanistic modeling. http://documents.aidswiki.net/PHDDC/BREG.PDF It uses Farr's law to predict the size of the HIV epidemic.pic.twitter.com/3dKhSuiMQG
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18. The authors conclude that the HIV epidemic will encompass roughly 200,000 cases before fading away in the mid 1990s. This graph is from the original paper. You can't make this shit up.pic.twitter.com/RogE3sISuC
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19. Next up a very, very basic fallacy about the effect of flattening the curve. Almost *any* reasonable epidemiological model you use, from SIR to all sorts of fancy spatial PDE or agent-based approaches, will show that decreasing transmission rate decreases total epidemic size.pic.twitter.com/kWjGl2sVhZ
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Why would flattening the curve decrease total epidemic size? It makes sense that this would be the case with normal flu because of vaccinations and herd immunity. Neither of those apply to CV-19, and this virus is extra viral.
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Replying to @ScottAdamsSays @CT_Bergstrom
Because of what "rate" means.
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