EU In Deal With Pfizer Despite Strange Vaccine Test Data

EU In Deal With Pfizer Despite Strange Vaccine Test Data
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EU In Deal With Pfizer Despite Strange Vaccine Test Data; Figures Suggest Lack of Real-Risk Testing and Unjustified Claims

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European Commission Signs A Deal With Pfizer

WASHINGTON, D.C. (November 11, 2020) - The European Commission has signed a deal with Pfizer for a supply of its COVID vaccine even through the preliminary data about its initial test is strange if not suspicious, at least according to Professor John Banzhaf, the inventor of the mathematical Banzhaf Index.

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Pfizer has announced that preliminary figures from its initial testing show that its COVID vaccine may be more than 90% effective in preventing test volunteers from becoming infected, but there's something strange if not suspicious about this result based upon the limited data the company has so far provided, says Banzhaf.

According to Pfizer, an initial test of about 44,000 subjects (actually 43,538) - about half of whom received the recommended doses of the test vaccine, and about half received a completely ineffective placebo - showed that 94 became infected, but that it prevented expected infections in about 90% of those 22,000 who received the actual vaccine.

In other words, it found that fewer than 10% of infections were in participants who had been given the vaccine while more than 90% of the cases were in people who had been given a placebo.

Infection Rate Figures

So the overall infection rate of all the 44,000 subjects was only 0.2% [94/44000 = 00.002 = 0.2%]. Even if one makes the simplifying assumption that all of the 94 infected cases came only from the group which received the ineffective placebo injection [22,000] - a worst case scenario - the infection rate would be only 0.4% [94/22000 = .004 = 0.4%].

But virtually all states, and even testing regions within states, usually show an infection rate of well over 1.0%. For example, Connecticut was recently happy to report that its infection rate had fallen from 6.1% to only 2.5%.

So, if these reported figures are accurate, it suggests that everyone in both groups had an exposure to the coronavirus which was - for some reason which was either not known, or not revealed to the media - far lower than in virtually any population group in the U.S., and, in many cases, an entire order-of-magnitude lower.

This alone should cast some doubt on the validity and applicability of the results to actual populations in which more realistic exposures levels, as measured by the infection rate, are much higher, and frequently over 5.0%, suggests Banzhaf, who dealt extensively with the epidemiology of tobacco smoke.

In other words, testing a vaccine against disease X, in a population where the infection rate for disease X is an order of magnitude much lower than in many populations, does not provide a realistic or reliable test of its effectiveness in the real world where the infection rate is often many times higher, explains Banzhaf.

Are Numbers Being Misrepresented

Furthermore, if the number of people receiving the vaccine who contracted the virus was only 10% of those in the placebo group who contracted it [i.e., it was 90% effective in preventing expected infections in the vaccinated group], and the total number of infections in both groups was 94, simple math shows that about 85 subjects in the non-vaccine group [85/22000 = .004] became infected compared with about 8-9 [8/22000 = 0.0004] subjects who actually received the vaccine.

But a number like 8 or 9, out of a total of some 22,000, hardly seems sufficient to draw a reliable statistical conclusion that the vaccine was 90% effective.

That's because a change of only a few detected infections in the test group - something which could easily be caused by a random statistical fluctuation - could cause a significant change in the claimed effectiveness.

That's why most epidemiologists and statisticians would not draw any firm conclusions from such a small result, especially after factoring in the limited reliability of some of the tests.

For example, if in a survey of 1000 voters, only 9 said that they favored proposition X, the margin of error in determining the percentage of voters favoring X among the entire population would be large, because the number in another different population could well as high as 10 or 12, or as low as 6 or 7.

So Banzhaf suggests that everyone, including the EU, reserve judgment as to what the initial figures may tend to show, at least until there is some further explanation as to why they may seem strange, and hope that the eventual vaccine can be shown to be safe, and anywhere near 90% effective.

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