Why Social Distancing Should Not Be the New Normal

Analysis by Dr. Joseph Mercola

STORY AT-A-GLANCE

• According to some, social distancing is part of “the new normal.” Alas, there’s plenty of evidence to suggest social distancing and lockdowns will not be necessary at all, and were probably a bad idea in the first place
• The rate of SARS-CoV-2 mortality never experienced exponential growth, as was predicted, which suggests a majority of people may have had some sort of prior resistance to the virus
• Statistical data reveals a mathematical pattern that has stayed consistent regardless of the interventions implemented. After two weeks of exponential growth, the growth curve quickly becomes sub-exponential
• Evidence for resistance to SARS-CoV-2 is emerging. One recent study found 40% to 60% of people who had not been exposed to SARS-CoV-2 still had resistance to the virus on the T-cell level. According to the authors, this suggests there’s cross-reactive T cell recognition between circulating ‘common cold’ coronaviruses and SARS-CoV-2
• One statistician believes the ratio of people that are not susceptible to COVID-19 could be as high as 80%. Once sensible behaviors such as staying home when sick are entered into this model, any potential benefit of lockdown efforts vanish altogether

According to some, Bill Gates prominently among them, social distancing is part of “the new normal.” Alas, there’s plenty of evidence to suggest social distancing and lockdowns will not be necessary at all, and were probably a bad idea in the first place.

According to Nobel-prize-winning scientist Michael Levitt,1 the rate of SARS-CoV-2 mortality never experienced exponential growth, as was predicted, which suggests a majority of people may have had some sort of prior resistance or immunity.

Levitt, a professor of structural biology at the Stanford School of Medicine, received the Nobel Prize in 2013 for his development of multiscale models for complex chemical systems.

No Exponential Growth in Mortality

Statistical data, he points out, reveal a mathematical pattern that has stayed consistent regardless of the interventions implemented. As reported by Freddie Sayers in the video above:

“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 …

He believes that both some degree of prior immunity and large numbers of asymptomatic cases are important factors … He describes indiscriminate lockdown measures as ‘a huge mistake,’ and advocates a ‘smart lockdown’ policy, focused on … protecting elderly people.”

Quarantining the Healthy Was Unnecessary

Now, evidence for prior resistance to SARS-CoV-2 is emerging, adding support to Levitt’s suspicions that the lack of exponential mortality growth may be due to the fact that a majority simply aren’t (and weren’t) susceptible to the disease in the first place.

At this point, we can clearly see that an all-encompassing global totalitarian plan had been quietly put together, piece by piece, behind the scenes, only to be put into action once a pandemic — real or imagined — emerged. A key player in the coordination of this plan has been Bill Gates, who stands to profit in any number of ways, both from vaccines and technological rollouts.

A study2 published May 14, 2020, in the journal Cell, found 40% to 60% of people who had not been exposed to SARS-CoV-2 still had resistance to the virus on the T-cell level. According to the authors, this suggests there’s “cross-reactive T cell recognition between circulating ‘common cold’ coronaviruses and SARS-CoV-2.”

In other words, exposure to coronaviruses that cause the common cold appear to allow your immune system to recognize and fight off SARS-CoV-2 as well. This is great news. Professor Karl Friston, another statistician whose expertise is in mathematical modeling, believes resistance or prior immunity could be as high as 80%. Sayers reports:3

“[Friston] invented the now standard ‘statistical parametric mapping’ technique for understanding brain imaging — and for the past months he has been applying his particular method of Bayesian analysis, which he calls ‘dynamic causal modelling,’ to the available Covid-19 data …

His models suggest that the stark difference between outcomes in the UK and Germany, for example, is not primarily an effect of different government actions (such as … earlier lockdowns), but is better explained by intrinsic differences between the populations that make the ‘susceptible population’ in Germany — the group that is vulnerable to Covid-19 — much smaller than in the UK …

Even within the UK, the numbers point to the same thing: that the ‘effective susceptible population’ was never 100%, and was at most 50% and probably more like only 20% of the population.”