| > Never does he predict that 1% will be infected. But he uses exactly this in his argument, in the same paragraph: "If we had not known about a new virus out there, and had not checked individuals with PCR tests, the number of total deaths due to “influenza-like illness” would not seem unusual this year. At most, we might have casually noted that flu this season seems to be a bit worse than average." Then later: "Some worry that the 68 deaths from Covid-19 in the U.S. as of March 16 will increase exponentially to 680, 6,800, 68,000, 680,000 … along with similar catastrophic patterns around the globe. Is that a realistic scenario, or bad science fiction?" Then he claims that "The most valuable piece of information for answering those questions would be to know the current prevalence of the infection in a random sample of a population" But I claim he already had, at the moment he wrote that article, much better data than that already available: specifically, that all the statistics everybody could find even in the Wikipedia already gave much more information that he claimed has to be obtained by "a random sample of a population." One can evaluate "how random" all already known cases, at the time he wrote the article, were. But also one can evaluate, if these known cases, even if they weren't random, were actually saying more, not less, by the nature the numbers were obtained. And that was exactly the case: time and again, in country after country, the statistics included much more people than the small randomness based study would include, and it gave reasonable estimates about both the speed of the spread and percentage of the people affected. His argument was not based on analyzing already available data, but on "not knowing" by *refusing to even look at the already available data. Which is fraudulent, ignorant or both. But there were some big names doing exactly the same, exactly at the time he published that article. So his article was just political, not scientific at all. |