Were 20M deaths averted due to the COVID vaccines? The Lancet says YES. I say NFW.
At the FLCCC conference in Orlando, someone asked me to comment on the Lancet paper that claimed that vaccines saved 20M lives. Here's why I think the Lancet paper cannot be correct.
Here’s the Lancet paper making the claim that up to 19.8M lives have been saved by the COVID vaccines:
When I got back from the FLCCC conference in Orlando, I read the paper and quickly realized this was based on a mathematical model.
I reached out to Jessica Rose who is an expert on mathematical models, and unfortunately, her response, while priceless, is unprintable here because of the language that was used.
Let’s just do a quick sanity test of this paper
20M lives saved eh? Well the Pfizer trial established that, best case, the vaccine might save around 1 COVID life per 22,000 vaccinations (it wasn’t statistically significant, but this is the best estimate we have). This assumes that the vaccine itself did not kill anyone in the trial which I think is very unlikely (this is the more important “all-cause mortality” measurement that nobody likes to talk about because it was negative in the trial because more people died who took the drug than the placebo).
We have no evidence that the people who died in the vaccine arm of the trial didn’t die from the vaccine other than the absurd claims of the manufacturer who never provided any rationale for making such claims and to this day, refuses to go on camera to defend their assertion. <begin sarcasm> Not that anyone cares about this since we all know, vaccines never kill anyone, don’t we? <sarcasm off>
So even in the best circumstances, this would mean that 22,000 * 20M people would have to be vaccinated to achieve this result.
So this means 440B people would have to be vaccinated to achieve this outcome.
This is a problem of course because, the last time I checked, there are only 7.7B people on planet Earth.
Of course, the real answer is that the mRNA vaccines saved nobody because they killed at least 50 people for every person that they might have saved from COVID. See my presentation for references on this.
Scientists and Mathematical models
This is probably a good time to bring up an article written by William Briggs.
Briggs has been an outspoken truth teller for a very long time. Here’s his bio.
Briggs is the author of the book, “Everything you believe is wrong” which makes the remarkable claim that:
If you are an Expert, professional, bureaucrat, teacher, professor, Democrat or Republican, liberal, progressive or conservative, consider yourself in any way in the educated classes, the odds are high that everything you believe is wrong.
Here’s an excerpt from one of the reviews on Amazon:
Want to recognize how we get manipulated by the media and politicians and how to counter it? This is the book for you. It gives you the tools for you to counter the manipulation. In short, employ the logic espoused in this book and you will have the tools to counter the overwhelming nonsense we are bombarded with every day. This book is not for the child-like pearl-clutchers or the easily swayed. You definitely need your adult thinking cap on when reading it.
Just 3 days after my FLCCC talk, Briggs wrote this article: All Those Warnings About Models Are True: Researchers Given Same Data Come To Huge Number Of Conflicting Findings which I happened to come across quite by accident shortly after it was written. You don’t have to read beyond the title to get the message.
Or just look at this graph for what the researchers (all given the exact same data), determined:
The article points out how insanely absurd the mathematical models used by scientists are.
Unless the models have been validated using prospective data, the paper shows that they are literally no better than throwing a dart at a dart board.
Only when a mathematical model can be used to accurately predict events that haven’t happened yet should we trust them.
No, the vaccines haven’t “saved” 20M lives. No possible way.
Mathematical models should only be trusted if they have been shown to predict what has not yet happened.