They Were Fed Wrong Numbers And Made A Huge Mistake

Professor Michael Levitt, who teaches structural biology at the Stanford School of Medicine, has recently spoken out about the coronavirus data. He explains that the sweeping lockdown measures were an overreaction that will backfire. Levitt won the 2013 Nobel Prize in chemistry for the development of multi-scale models for complex chemical systems.

The data for the COVID-19 outbreak since January has been remarkably accurate within his predictions. He displays how the data shows that the outbreak never grew exponentially and that suggesting harsh lockdown measures were unnecessary in how they have drastically impacted the world economy. 

Back in early March, Levitt predicted a quicker coronavirus recovery than most. While left-wing media platforms are instilling fear in their viewers of warning of months, or even years, of massive deaths and disruptions, Levitt says the data simply doesn’t support such a dire scenario. “What we need is to control the panic. We’re going to be fine. Numbers are still noisy, but there are clear signs of slowed growth,” he says. 

Here’s what he noticed in China: On January 31st, the country had 46 new deaths due to the coronavirus, compared with 42 new deaths the day before. Although the rate of daily deaths had increased, the rate of increase had begun to ease off. The fact that new cases were being identified at a slower rate was more telling than the number of new cases itself. This was a sign that the trajectory of the outbreak had shifted and that the number of deaths will slow down even more over the next week. He had written a report on this and sent it to friends Feb 1. As he predicted, the numbers of death would be decreasing every day. He had predicted that the total number of confirmed COVID-19 cases in China would end around 80,000 with about 3,250 deaths. As of March 16, China counted a total of 80,292 cases and 3,245 deaths. Spot on.

In his study, a simple mathematical pattern is observable regardless of government interventions. After a two-week exponential growth of cases, a break kicks in, and growth starts slowing down. The curve quickly becomes “sub-exponential.” The world relies on this presumption of continued exponential growth, but Professor Levitt’s point is that it hasn’t happened anywhere yet. 

Instead of strict lockdown orders, he tells UnHerd that developing herd immunity is a better strategy to fighting COVID-19. “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 practice too much lockdown and they got enough people sick to get some herd immunity,” Levitt explains.

He predicts the standout losers as countries like Austria, Australia, and Israel that had strict lockdown but not many causes. They have damaged their economies, caused massive social damage, damaged the educational year of their children, but not obtained any herd immunity. 

When we come back from this, the damage done from lockdown will exceed any saving of lives by a huge factor.