Lotecnotec's take on Pfraud
The Digger's article was great, but maybe it can be even easier to understand
The Digger just took the paywall off his article explaining what Jikky and Guetzkow have been revealing to us about Pfizer fraud data errors, and after perhaps the third reading, I “get it” well enough to attempt my own, even simpler, explanation.
As explained in the above, “95% efficacy” is derived from comparing “confirmed cases” in the vaccinated group (8) with those in the placebo group (162). Let’s do all our calculations at the Python3 prompt, shall we?>>> (162 - 8) / 162
0.9506172839506173
There’s our 95% efficiency. But wait! As Jikky, Josh, and Phil explain, the tests included measurements of N antibodies, and there were 160 of the control group (placebo cohort) which went from negative to positive; and 75 in the vaccinated group! Phil goes on to explain that development of N antibodies is a good indicator of symptomatic infection. So now let’s revisit our Python prompt:
>>> (160 - 75) / 160
0.53125
Oops! That shows 53% efficacy, not 95%! But wait, there’s more!
Turns out that vaccination of covid-naive patients actually inhibits production of N antibodies; only 40% of the vaccinated-then-infected develop them; and then again, only 93% of the unvaccinated do. So let’s correct both figures to find the actual number of infectees. If 160 is 93% of x
, then x
is 160 divided by 0.93, and the same method applies to the vaccinated.
>>> 160 / .93
172.04301075268816
>>> 75 / .40
187.5
>>> ((160 / .93) - (75 / .40)) / (160 / .93)
-0.08984375000000007
So now we have: tada! -9% efficacy: vaccination produced a (likely statistically insignificant) increase in symptomatic infection over placebo. And that’s without even going down the “absolute risk” rabbit hole with Phil.
There’s another huge data dump expected on or about July 1st, just 3 days from now. Assuming Pfizer is releasing the least damaging data first, this next one should be a doozy. Watch for it at https://phmpt.org/pfizers-documents/.