If you are surprised that a complex prediction model did not accurately predict the future, the problem is on your end. Why did you ever think that was a thing?
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I want people in charge who always get it right and don't need models or facts. So, you.
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Excellent choice.
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Completely disagree with “worked Perfectly” since States who did not shutdown are doing fine. I appreciate you defending your position, Rare on Twitter
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(1)If you assume that 2 million Americans are going to die “unless we use quarantine measures”, then you can always justify the quarantine measures if you don’t hit 2 million deaths. It’s foolish bc it ignores the possibility that the virus wasn’t going to kill 2 million
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(2) Americans even without quarantine measures.
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“Sizing the risk when there are great unknowns” can persuade only when the audience is led to believe the “experts” used some unstated facts & experience to gauge the lower/upper bounds they offer, better than throwing darts. The Imperial College model threw darts.
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Why did the 1-to2.2 million deaths estimate assume NO containment/mitigation efforts? They had a wealth of data from SARS, MERS, H1N1, no? (Well, in the case of H1N1, Obama/Biden did practically nothing, but nevertheless...)
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You and I know that complex models have a number of built in assumptions that reference the bias of the modeler. So if you're comfortable dismissing accuracy as a goal in leu of "persuasion", then it's just fancy propaganda.
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There is complete and continuous evidence that they are wrong.
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They were 95 percent confident based on all assumptions being 100 percent correct. Plus no code errors
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