The numbers are fake

I’d already reached the same conclusion about the legitimacy of the statistics after keeping track of the official Corona-chan numbers for a few days. The spread of the disease was far too smooth to be genuine. So, as usual, the one thing we can be certain isn’t true is the official story:

In terms of the virus data, the number of cumulative deaths reported is described by a simple mathematical formula to a very high accuracy, according to a quantitative-finance specialist who ran a regression of the data for Barron’s. A near-perfect 99.99{de336c7190f620554615b98f51c6a13b1cc922a472176e2638084251692035b3} of variance is explained by the equation, this person said.

Put in an investing context, that variance, or so-called r-squared value, would mean that an investor could predict tomorrow’s stock price with almost perfect accuracy. In this case, the high r-squared means there is essentially zero unexpected variability in reported cases day after day.

Barron’s re-created the regression analysis of total deaths caused by the virus, which first emerged in the central Chinese city of Wuhan at the end of last year, and found similarly high variance. We ran it by Melody Goodman, associate professor of biostatistics at New York University’s School of Global Public Health.

“I have never in my years seen an r-squared of 0.99,” Goodman says. “As a statistician, it makes me question the data.”

Real human data are never perfectly predictive when it comes to something like an epidemic, Goodman says, since there are countless ways that a person could come into contact with the virus.

For context, Goodman says a “really good” r-squared, in terms of public health data, would be a 0.7. “Anything like 0.99,” she said, “would make me think that someone is simulating data. It would mean you already know what is going to happen.”

About the only thing we know at this point is that non-Chinese victims are not dying at anywhere near the same rate as Chinese victims. And Occam’s Razor strongly suggests that the manufactured numbers underestimate the actual ones.