Nobel Prize-winning scientist shares Flu data showing strict lockdowns were a mistake
Nobel Prize-winning scientist shares COVID-19 data showing strict lockdowns were an overreaction.
"Professor Michael Levitt, who teaches structural biology at the Stanford School of Medicine, won the 2013 Nobel Prize in Chemistry for "the development of multiscale models for complex chemical systems. " And according to Levitt, coronavirus data show that sweeping lockdown measures were an overreaction that may actually backfire.
Levitt has been analyzing the COVID-19 outbreak from a statistical perspective since January and has been remarkably accurate in his predications. The data show that the outbreak never actually grew exponentially, suggesting harsh lockdown measures, which have drastically impacted the world economy, were probably unnecessary.
His observation is a simple one: that in outbreak after outbreak of this disease, a similar mathematical pattern is observable regardless of government interventions. After around a two week exponential growth of cases (and, subsequently, deaths) some kind of break kicks in, and growth starts slowing down. The curve quickly becomes "sub-exponential" . . .
"I think the policy of herd immunity is the right policy. I think Britain was on exactly the right track before they were fed wrong numbers. And they made a huge mistake. I see the standout winners as Germany and Sweden. They didn't practise too much lockdown and they got enough people sick to get some herd immunity," Levitt explained. . . ".