Nobel Prize Winning Scientist: Lockdowns Were Overreaction That May Backfire

RALEIGH – At the onset of the COVID-19 pandemic panic in the United States, as shutdown policies were only beginning to proliferate, some wondered what would be more frightening: the model projections of death from the unknown virus, or the very tangible and immediate destruction to livelihoods caused by the lockdown policies.

Still, public health officials and a fearful public accepted the notion that coerced social distancing, forcing the closure of businesses and immediate waves of lay offs, was worth it. After all, this was about saving lives. The Stay-at-Home orders and assembly bans would ensure a slower spread to avoid the feared overwhelming of healthcare systems.

Now, weeks past the peak, and hospitals half empty, the model projections have proven massively exaggerated. In fact, some experts are confident that the lockdown mandates were not only ineffective, but a significant net negative where they were instituted. One of those experts is Professor Michael Levitt, who teaches structural biology at the Stanford School of Medicine. He won the 2013 Nobel Prize in Chemistry for “the development of multiscale models for complex chemical systems,” but, he says, the popular model projections that influenced lockdown policies were flawed from the beginning.

Not only does Levitt think the lockdown policies were an overreaction, but he worries that they were more damaging than helpful. He spoke with Unherd, and explained why the shutdown edicts, from North Carolina to Italy, were the wrong policy from the outset.

“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”.

This may seem like a technical distinction, but its implications are profound. The ‘unmitigated’ scenarios modelled by (among others) Imperial College, and which tilted governments across the world into drastic action, relied on a presumption of continued exponential growth — that with a consistent R number of significantly above 1 and a consistent death rate, very quickly the majority of the population would be infected and huge numbers of deaths would be recorded. But Professor Levitt’s point is that that hasn’t actually happened anywhere, even in countries that have been relatively lax in their responses. […]

“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. […]

“I see the standout losers as countries like Austria, Australia and Israel that had very strict lockdown but didn’t have many cases,” he said. “They have damaged their economies, caused massive social damage, damaged the educational year of their children, but not obtained any herd immunity.

“There is no doubt in my mind, that when we come to look back on this, the damage done by lockdown will exceed any saving of lives by a huge factor,” Levitt predicted.”

Yet, social distancing lockdowns will be credited with avoiding the worst case scenarios projected by those very flawed models. John Q. Public will believe it, because it is a reasonable assumption to make given the limited information.

‘If we didn’t do lockdowns, there would have been far more deaths,’ will be the most widely accepted counterfactual fallacy in 2020. It won’t matter that Nobel prize winners point out that the patterns are the same between countries that did strict lockdowns and those that did virtually nothing; the consensus has to be that they worked, that they were worth it, lest people begin to question the merit of accepting life altering lockdowns for the common good.

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