s to another earlier paper16 by German investigators that had come to a similar conclusion.
That German paper,17 the preprint of which was posted April 22, 2020, on Medrxiv, found helper T cells that targeted the SARS-CoV-2 spike protein in 15 of 18 patients hospitalized with COVID-19. As reported by Science:18
“The teams also asked whether people who haven’t been infected with SARS-CoV-2 also produce cells that combat it. Thiel and colleagues19 analyzed blood from 68 uninfected people and found that 34% hosted helper T cells that recognized SARS-CoV-2.
The La Jolla team20 detected this crossreactivity in about half of stored blood samples collected between 2015 and 2018, well before the current pandemic began …
The results suggest ‘one reason that a large chunk of the population may be able to deal with the virus is that we may have some small residual immunity from our exposure to common cold viruses,’ says viral immunologist Steven Varga of the University of Iowa. However, neither of the studies attempted to establish that people with crossreactivity don’t become as ill from COVID-19.
Before these studies, researchers didn’t know whether T cells played a role in eliminating SARS-CoV-2, or even whether they could provoke a dangerous immune system overreaction. ‘These papers are really helpful because they start to define the T cell component of the immune response,’ [Columbia University virologist Angela] Rasmussen says.”
Statistician Believes Majority Are Resistant to COVID-19
Life Enhancement Ultra... Buy New $14.99 ($0.12 / Count) (as of 04:34 EDT - Details) These studies add support to the latest COVID-19 mortality models suggesting widespread resistance and prior immunity. Freddie Sayers, executive editor of UnHerd, recently interviewed professor Karl Friston, a statistician whose expertise is mathematical modeling, who believes prior immunity across the global population might be as high as 80%. Sayers reports:21
“[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 … 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.”
These statistics really throw the idea of social distancing being an unavoidable part of the post-COVID-19 “new normal” into question. What’s more, once sensible behaviors such as staying home when sick are entered into Friston’s model, the effect of lockdown efforts vanish altogether, so global lockdowns were likely completely unnecessary in the first place.
Nobel-Prize Winning Scientist Debunks Growth Projections
Michael Levitt,22 a professor of structural biology at the Stanford School of Medicine who received the Nobel Prize in 2013 for his development of multiscale models for complex chemical systems, has also presented strong evidence that supports Friston’s model.
According to Levitt, statistical data reveals a mathematical pattern that has stayed consistent regardless of the government interventions implemented. While early models predicted an exponential explosion of COVID-19 deaths, those predictions never materialized. As reported by 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 w