# Should 12 die for 1?

May 29 · 3 min read

The Mathematical Conundrum Of a Modern Medical Miracle Cure.

What exactly does the “efficacy rate” mean when it comes to vaccinations? I had to double-check this for myself, as it’s getting harder and harder to find credible sources on either side of the vax controversy. I try to approach this question without a pro-contra agenda, just to understand the risk profile better.

The usual efficacy number used in the drug industry is the Relative Risk Reduction (RRR) rate, which you see in the product description and marketing. For example, when the CDC says that the Pfizer-BioNTech vaccine is “95% effective,” it’s using the RRR rate (this is the official estimate after Phase 3 trials).

Pfizer’s original RRR number was derived by subtracting the number of COVID infections (8) in a trial vaccine population (162) which was then divided by the number of COVID-19 cases in the placebo group (162).

(162–8)/162 = 95.1%

We can have many opinions on whether this equation warrants an Emergency Use Authorization to inoculate the world, but that’s not the focus of my query.

I’m more interested in the Absolute Risk Reduction (ARR) rate, which doesn’t appear in product marketing as often (read: never) for the simple reason that it portrays the reality in a less marketable manner.

For the Pfizer jabs, the ARR rate after Phase 3 trials was 0.98%.

This means that the Number of people you Need to Vaccinate (NNV) to prevent one corona case is 117 (NNV = 1/ARR).

The latest results from Israel — the country with the most vaccinations — show that the actual ARR for Pfizer is closer to 0.46%.

That gives an NNV of 217.

(NOTE: These numbers come from LANCET, the world’s oldest and best-known peer-reviewed medical journal.)

In other words, for one jolly fellow to avoid Corona, 217 people have to get the jab.

It gets messier when we look at the infection fatality rate.

With an infection fatality rate of 0.05% (in people under 70, according to PANDATA.org), 2,000 need to be infected by the virus to produce one fatality.

So, to avoid one corona death, we need to vaccinate 217 x 2,000 = 434,000 people in total.

That would be fine, I suppose, if there wasn’t the other side of the coin, namely the “extremely rare” adverse cases related to the vaccinations themselves.

Using official National Vaccine Information Center numbers in the USA for 14th of May, 2021:

- 155,251,852 million vaccinated in the USA

- Total of 269,309 adverse effects registered by VAERS in the USA. Out of these, 90,133 are deemed “Not Serious,” which leaves 179,176 “serious” cases (see table below).

- 4,201 dead in the USA (of these, 38% died after getting ill within 48 hours of vaccination).

Let’s scale these numbers to the 434,000 figure (the number of vaccinations required to prevent one corona death according to the published ARR rate).

434,000 / 155,251,851 = 0.0028 multiplier (with two-decimal rounding).

- 0.0028 x 179,176 Serious Adverse effects = 502 adjusted

- 0.0028 x 4,201 deaths = 12 adjusted

In summary, based on existing published numbers, it looks like we need to vaccinate 434,000 people to stop one corona fatality, with the downside that it may cause 12 jab-related deaths and 502 serious adverse effects — if the past trends hold.

I think it’s important to use precise language when inoculating the world population so that everyone can assess the risk profile for themselves.

Correct me if I’m wrong. Please. I want to be wrong about this one.

(NOTE: These numbers do not take into account long-term vaccination effects, which may take up to a year to manifest. They also don’t take into account the fact that there is no proof that covid vaccines confer immunity. Or that according to NIH, the VAERS numbers may be underreported by a factor of between 1/10 to 1/100.)

## Obliterate Conformity

Break Dogma For Your Health Sake

## Obliterate Conformity

Break Dogma For Your Health Sake

Written by

## Jan Wellmann

Obliterate Conformity

## Obliterate Conformity

Break Dogma For Your Health Sake

## Digital Health Should Have a Larger Voice in COVID-19 Detection

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