to unfortunately go on another show.
Dr. Rancourt, it's so nice to meet you.
Same here, same here, can I call you Peter?
Sure, thank you, shall we do that?
Sure, so I thought maybe this would be an opportunity
to present your recent paper,
particularly in light of the claim today
that the Nobel Prize messenger RNA saved
a minimum of 14 million lives.
Right, yeah, no, I'd be happy to.
Did they actually claim that number, Peter?
14 million, no way.
Oh my gosh, is that a Nobel announcement
or some media report or?
Media report, but they're all following the same script.
Yeah, there was an article that claimed
that kind of number, I guess.
Yeah, it's a 14 to 25 million, if I recall correctly.
Right, right.
So I'll just very quickly say the result of our work
in that regard and then we can interact about it.
Okay, all right, let's go ahead and hit it.
Yep.
You'll go ahead, we're already on.
I'm not fooling around here.
You guys are too important for me,
so I already, you didn't say anything crazy already,
so go ahead and start, please.
There you go, we're just starting, okay.
I am thrilled to be on the program.
Jay and Dr. Rancourt, thank you so much.
It's an honor to meet you for the first time.
Yeah, you know, the world has been very interested
in your ecological analysis that involved countries
in the Southern Hemisphere.
Can you give us a capsule of that,
of what that paper showed?
I'd love to, Peter, if I may.
I'm also thrilled to meet you for the first time,
even though it's a virtual meeting.
But here we go, I've been working on all cause mortality
for a long time.
Our first paper on the subject was put out
in June of 2020.
And in that paper, we said immediately
that there were hotspots of immediate mortality
that were synchronous with the announcement
of the pandemic on the 11th of March, 2020.
And we said these immediate surges of mortality
that occur only in hotspots, New York, Northern Italy,
Madrid, a few places like that, and nowhere else.
There was nothing like it in 30 of the US states
that that mortality was inconsistent
with the spreading respiratory disease.
It couldn't be that because it was synchronous
around the world.
And it was, so it was very granular and synchronous
and it didn't spread.
So that was our immediate conclusion
when we started looking at this all cause mortality back then.
Well, let me respond to that.
You know, I was in the thick of it early on
in the pandemic, you know, trying to innovate
with ways of helping people.
But like every other person in America,
I was watching CNN or any newscast
and I was watching this mortality meter
in the upper right-hand part of the screen.
And let me tell you, when one of my patients dies,
from the time they die to the time
I completely finished the death certificate
and have it registered in the system,
it's about six weeks.
So I was wondering how could these instantaneous deaths
come up on a scoreboard because they each have a six week life?
Oh, Peter, this is very important.
We have to be very careful here.
I'm talking about all cause mortality.
So that means irrespective of any cause of death
that anyone might assign.
In other words, I'm talking about.
Even when I'm saying the deaths don't get recorded
until the death certificate is completed.
Really?
Yeah, so the idea is there's always a six week.
No, the databases that I'm working from,
they actually give you the date of death.
Now, the certificate might go into the system late,
but the data is, the actual data is by date of death.
In other words, sure, there's a lag
in terms of when you get the certificates in.
Sometimes there's a lag of as much as a couple of months.
And so you're updating the mortality data as we go.
And it's typically a month or two late, you see,
but then once it goes into the system,
it's by date of death.
Right, I know, but even the National Death Index,
I think runs about six months behind.
So when deaths occur,
it's possible that if someone dies in the hospital,
there may be a more immediate reporting system,
but most of the time there's no, no, no, but let me explain.
There's a misunderstanding here.
See, what I'm talking about is,
I mean, I'm sitting here in June doing my study, okay?
And so this is after the 11th of March, 2020.
No, I understand.
I'm just telling you real world.
I'm just wasting the question.
Real world in March of 2020,
we were seeing deaths go up every day.
Yes.
Okay.
And it's, I was wondering how in the world
are those data feeds that simultaneous
with this lag of a month or two months afterwards?
Well, you know, in terms of reporting
so-called COVID deaths in the media or on the TV screens,
they can be reporting whatever they want,
but actual official all-cause mortality data
is the total of deaths for that day in a given jurisdiction.
So what they might or might not be doing that,
that pops up on your screen in terms of deaths
and what those deaths mean, I don't know,
but I'm working from robust data,
which is actual all-cause mortality,
deaths assigned to a given date and it's by day.
And then some jurisdictions, when they report it,
they'll give it to you by week.
Some will give it to you by month
if it's a smaller jurisdiction
and they want better statistics,
but you see it after the fact.
You have to gather it, collate it,
and you know up to when it is reliable
according to the people that are providing the data, you see.
So up to that date, you've got good data
and that data never changes
and that data has been reliable
since they've been doing this for a hundred years now.
It's very robust, very reliable data
and it is collected irrespective of the cause of death.
So this is just total deaths, okay?
And then so what you do then is you look at the patterning time
of those deaths in a given jurisdiction.
It can be one state in the US,
it can be the whole country or another country
and you follow it as a function of time
and what you will see immediately
is that in Northern latitude countries,
it has a seasonal pattern, a very clear seasonal pattern.
There are always far more deaths in the winter
than in the summer.
So there's a winter peak in all cause mortality,
then you go down to a summer trough
and this pattern has been known for a hundred years.
And what's interesting is in the Southern Hemisphere,
that pattern is reversed
because they're winters in our summer.
So they get their maximum of deaths
in that seasonal pattern during their winter,
which is our summer.
And this is a phenomenon that's well known,
it's basic epidemiology,
it's been known for a hundred years, it's very striking
and it's not completely understood exactly why that is, okay?
There are various models as to why the deaths
are always higher in the winter,
including deaths that are related to cardiac problems.
The only deaths that don't follow that pattern
are the main tumor type cancer deaths.
They don't have a seasonal pattern,
but everything else, the infections, the heart attacks,
everything that is sensitive to stress, I guess,
stress induced, they all have a very clear seasonal pattern,
okay, in terms of mortality.
And so you know what to expect
because you have a pattern that you can see for a hundred years
and you can see it up and down and up and down
is very regular.
And then COVID hits and they announce a pandemic,
they declare a pandemic on the 11th of March 2020
and you get an immediate surge in that all-cause mortality
in certain hotspots.
So only occurring in New York, Northern Italy, Madrid,
Stockholm, a few places like that,
very intense, very sharp surges of all-cause mortality
right after they announced the pandemic.
So the fact that it is coordinated,
the fact that the timing of the event
is related to a political event,
the announcement of a pandemic
and that it is synchronous around the world
and that it's only in those hotspots
from our perspective,
this cannot be the spread of a viral respiratory disease
because it's well known that the time from seeding
of a new pathogen in a population
to when you get an actual surge in mortality,
that time is extremely sensitive to the details
of the population, of the society,
of how they contact each other and so on
and it can vary by months or years even.
So to have synchronicity like that is impossible
even with modern airplanes
because even if you send out flights from the source
all at the same time,
then that's the seeding where they land
but then the time between that original seeding
to when you'll get a surge in mortality
is highly dependent on the local circumstances.
So you can't have synchronicity like that.
So this was clearly not related to COVID like spread
or anything like that at the beginning.
So that was the first thing we noticed
and then we kept studying all-cause mortality.
I've written more than 30 papers
on COVID related things analyzing data and so on.
And what we find Dr. McCullough is that
the excess all-cause mortality
is inconsistent with a viral respiratory spread,
absolutely inconsistent with it
because it does not cross borders.
If you look at European countries
or states in the United States,
you can have mortality in one jurisdiction
and it stops at the border and is not in the other.
So this mortality at the beginning
was related to what was being done in those jurisdictions.
So for example, we wrote a paper with John Johnson
at Harvard University, we co-authored a paper
where we showed that when you compare U.S. states
if you take states that share a border
and one locked down and the other didn't
they all cause mortality in the lockdown state
even though they're very similar
and they're sharing a border is always higher,
significantly higher than in the non-lock down state.
So we're able to, we have a lot of reason
to come to the very firm conclusion
that what I believe now is that all of the excess
all-cause mortality that occurred
before the vaccines were rolled out
between when they announced to that time
is all due to lack of treatment
and aggressive medical protocols in big hospitals
and aggressive government measures
that isolated people and stressed them out
and including very vulnerable people
like the 11 million who are disabled
by serious mental illness in the United States,
that kind of thing.
So when you look at the age structure of this mortality
and its geographical distribution
and its association with all these things
that they know were being done in these jurisdictions
we have concluded that there was,
there is no evidence for a particularly virulent
new pathogen that was spreading
that in fact all of the excess mortality
everywhere we've looked in the world
can be understood in terms of
this is what happens when you do this to people.
This is what happens when you stop treating them
for all the usual things that they have
and when you destroy their lives and stress them out
and force them to be isolated
this is what you get, you get this kind of mortality.
And so this mortality is very heterogeneous
until you start roll out the vaccines.
Then once you start rolling out the vaccines
because that was done pretty much simultaneously
around the world, you have everywhere
an increase in all-cause mortality.
You move into a regime of higher all-cause mortality
and then you stay there while you're rolling out the vaccines
and then every time you roll out a booster
you get a peak, an extra peak in all-cause mortality
associated in time with that booster.
And this is stunning, we see this
and you can do it by age group.
So you can look at the 90 plus year olds
or the 80 to 90 year olds and so on.
And you see a very sharp booster rollout
because they did it very quickly in a given jurisdiction
and immediately follows it
is a very sharp unprecedented peak in all-cause mortality.
So this is extremely clear, it cannot be an accident
and therefore you can quantify it.
You can say, well, how many deaths occurred
given how many injections you gave?
So that's what we do, we've been quantifying it.
And what's surprisingly is what we find
is that around the world in every jurisdiction
we've now looked at over 100 countries,
the mortality risk per injection
is pretty much the same everywhere.
So all ages, it's about 0.1%.
So one, actually we refined it recently
is 0.126% with an error bar on it.
And so that means that for every 800 injections,
one person will die.
So it's one person per 800 injections.
Now the important thing is that that risk of death
per injection is not uniform with age.
It increases exponentially with age
and it is dramatically higher the older you are.
The doubling time by age is four to five years of age.
Every four to five years of extra age that you have,
your risk of dying per injection doubles.
So we hear about the deaths in young athletes and others,
but I've always been struck by the McLachlan analysis
from Queens University very early on using the VAERS.
It was the only VAERS analysis
that read every single vignette
and then adjudicated the two different adjudicators
and they had an agreement process
to finally adjudicate the death.
And they only had about 1,200 deaths
at that point in time in VAERS.
And what was striking is,
that was when it was being rolled out in the nursing homes
in January, February, March.
It was the seniors, just as you said, that were dying.
And in the McLachlan analysis,
it was striking how quick it was.
There was something like 16% or so died within a few hours.
Of those who died died within a few hours of the shot.
A large fraction was within 24 hours.
And so it was almost as if the reactor genicity of the shot
or the early production of the spike protein
from the genetic material we know this occurs within an hour.
We know it's circulatory in the bloodstream.
It's simply not this lethal protein
just is not tolerated by the elderly.
It makes sense.
In the McLachlan analysis,
86% of the time there was no other explanation.
They were in their usual state of health.
They took the vaccine and then they succumbed to death.
Absolutely.
We also studied the VAERS data of the United States.
And it's very, very clear that the deaths that do occur,
most of them, there's a peak around three or four or five days
from vaccination.
It's very clear, as you say,
that's clear in the VAERS data as it is.
And also the VAERS data shows dependence on age
that is exponential.
We wrote a little paper about that.
So it's seen directly in the VAERS data.
But what's important to realize is that now that we've
quantified it on the scale of entire populations,
using these peaks, we now know that the risk of death
is much higher than what you would conclude
from the VAERS data.
It's much higher than that because it's,
well, as I said, so it's, if you use our numbers,
it would correspond to more than 0.2%
of the world population that would have died
from a direct result of being injected
in the last, less than three years.
It would have been 17 million, 17 million people.
It would have been.
17 million, right.
That's a roundabout number I've heard.
In the United States, in the domestic VAERS data,
we're at over 18,000 deaths that the CDC,
now when we report a death,
and I've reported a death to the VAERS system
as a practicing doctor.
So I know what it takes and the CDC does receive it.
It gets a temporary VAERS number.
It waits for the death certificate.
Again, that's six weeks later, comes in.
And then it ultimately gets a permanent VAERS number.
So what's up in VAERS for permanent VAERS number deaths?
It's about 18,000, 1,100.
It's within the first day of the shot.
Okay, so it's very tightly temply related.
In the FDA testimony at the VRBAC meetings,
independent scientists have put a under-reporting factor
on the deaths at about 30.
In the peer-reviewed literature, there's one paper
that is pointing to 40 as a number.
But if we take 30, we're looking at just under 600,000
Americans dying with the vaccine.
We're about 4% of the U.S. population.
So when you kind of do that number,
you're gonna get out there to that 20 million number.
Very close.
That's one approach is to try to estimate
the under-reporting in VAERS.
That's a very, it's a relatively tenuous approach
because there's a lot of uncertainty involved
in trying to do that.
One thing that's different is that the culture,
the propaganda, is very, very different before the pandemic
is declared and then after the pandemic is declared.
And that has a big influence on whether or not MDs
will report and so on and people also,
whether or not people feel that the person that died,
it could have been that and so on.
So it's very hard to estimate that under-reporting rate
and it will be different for deaths
and for a major adverse reaction and so on.
So that's a difficult thing to do
but I respect people who try to do that.
Yeah, it's difficult.
It's just one of many methods.
The practical aspects of it are you're right.
In VAERS, paper by Meister and colleagues from 2016
said about 86% of the reports are by a doctor,
a healthcare worker or a pharmaceutical company
which can report to VAERS.
So you know, individuals don't report.
I can tell you, I practically can't do the report
unless I have the vaccine card.
So many deaths at home that are brought in,
you know, there's just no way I'm gonna have the vaccine card.
It's just, I just can't do it.
So it really has to be a patient under my care.
I have to suspect it.
The family has to provide the vaccine card.
Then I go through the laborious part of doing the entry.
Now, it's interesting in the first year of the pandemic,
there was a paper using census data
in vaccine administration,
it's ecological analysis, pentas, octos and cell equipment.
You know, they came up with a number
around 170 or so 1,000 people,
Americans died of the vaccine in the first year.
And then Mark Skidmore using social networks,
a different analysis.
He came up with about 278,000 individuals.
And now this VAERS underreporting,
again, divide that 600 by, you know, by the 2021 numbers.
We have about three sources of evidence.
Well, we calculated the risk of dying per injection.
And we estimated the best value for the United States.
And we came up with a number of about 300,000 deaths
that would have been, that would have been.
In what overall or just in 2021?
That at the time that we wrote the paper,
it's almost, no, no, no, I mean vaccine deaths.
Yeah.
It would be a little bit more now
because there have been more vaccinations and so on.
But it was roughly that kind of number.
It was at the time, the same time
that Mark Skidmore had published his paper,
we were publishing ours at the same time
and we came up with the same number.
Basically.
No, no, because we have a national death index
and because we have vaccine administration data
and in some countries like, you know, Denmark,
I just visited Denmark have exquisite data systems.
It's simply merging the vaccine administration data
and the death data and doing, you know,
a reasonable temporal analysis.
You know, I've led, you know,
over two dozen day safety monitoring boards
for novel drugs, devices, other things,
for the FDA, for the NIH, for BARDA.
And we always use a 30 day empiric number.
So any death that comes within 30 days
of an experimental product,
it just counted on the product period.
We don't have to go weeding through any jurisdiction,
any country that has a detailed data set of deaths
and that for those individuals who died,
you can know when they were vaccinated with this vaccine
and how many times they were vaccinated.
That would be extremely helpful.
That would be the golden data, right?
That would be.
Listen, there are easily three dozen countries
that have that and they've been pushed.
So the United States has formerly been pushed
by myself and others to merge the data.
They won't do that.
I met with Dr. Manicki,
Vervechi Manicki in Denmark.
They clearly can do it.
And there's not a single country
that will merge the vaccine administration data
with the death data.
Yes.
Well, there you go.
That's one of the problems.
And one of the best things we can do at this stage
without having that merged data
is what we're doing.
You all cause mortality by the time.
And very important.
Now, all cause mortality just quickly
in terms of causes of death in general
before the pandemic in Westernized countries,
it's about 40% known cancer,
40% known cardiovascular disease
and about 20% other causes.
And so cancer and cardiac disease are always
kind of neck and neck for the number one causes.
But the point is in almost every analysis,
the vast majority of the cause of death is known.
In fact, I reviewed a paper
even among college age kids who die.
And the number was far in excess
of having the vignette known.
It was a suicide, a motor vehicle accident,
a drug overdose or a known cancer case.
During COVID, the excess mortality that we found
before the vaccines were rolled out
could matched very well what the government
was calling COVID deaths, okay?
In terms of excess mortality by time,
with all the bumps and all the ups and downs
nationally for the US, the COVID deaths
that the US was reporting
matched that excess mortality quite closely.
But when you looked at their data,
they actually admitted that up to half
and even more than half depending on the state
of those deaths had comorbidity of bacterial pneumonia, okay?
And at the same time, the prescription rates
for antibiotics were dropped by 50% across the Western world.
So we believe that a lot of people died
from bacterial pneumonia during this period.
In fact, the southern states in the United States
normally get two to three times
more prescriptions of antibiotics.
I don't know if you knew that,
but the poor southern states
where there's higher levels of poverty,
they get a lot of prescriptions of antibiotics,
those were cut.
What we found is that all cause mortality correlated perfectly
with the fraction of the population
that was living in poverty in the United States.
Yeah, it's just stunning.
It's a straight line.
So if in a state that had no people living in poverty,
there would have been no excess death
according to this proportionality that we found.
And so it was the poor people, they tend to be obese,
they normally get prescribed a lot of antibiotics,
meaning they're susceptible to lung infections.
Those are the people who died in the United States
and especially the elderly among in that group.
Yeah, I think this is really, really important
for the audience to hear.
In just a minute we have left, though,
I do have to ask you for your interpretation of this.
Two former years of Pennsylvania scientists,
a man or woman were awarded the Nobel Prize
for their work in modifying messenger RNA
to make it more durable in the human body,
not really for creating the whole entity.
There's over 9,000 patent documents on messenger RNA
and the United States has been in this business since 1985.
You know, the top patent holders for messenger RNA
are a curvac and Sanofi and BioNTech Moderna
in the US government, but no single person
quote invented messenger RNA.
But these two people just got the Nobel Prize for modifying it
for COVID-19 vaccines.
But all the press releases today
say that the COVID-19 vaccines have saved
somewhere between 14 and 25 million lives.
The vaccines have saved lives.
Dr. Rancourt, is there any way
that COVID vaccines could be given this type of attribution
of saving lives?
Listen, it's unambiguous.
All cause mortality by time in all countries
that we've studied across the world,
there is not a single example of evidence
where you could conclude that lives were saved
by the vaccine rollouts.
There is no decrease in all cause mortality.
It's the opposite.
You go to a higher regime of all cause mortality.
And then when you roll out the boosters,
you get extra peaks on top of that fire regime.
There is no jurisdiction where you can say,
uh-huh, the vaccines are coming in.
Now the deaths are coming down.
No way, that does not happen.
It never happens.
It's the opposite.
You have temporal association between vaccine rollouts
and extra excess mortality.
This may be the first Nobel Prize
that's associated with increased worldwide mortality.
I'll have to go back and year by year.
But what a stunning observation.
It's a bit of fascinating interview.
Thank you so much.
The other thing I have to say just quickly
is that there are many, many countries around the world
like Australia, Israel, many, many countries
where there was absolutely no excess
all cause mortality until the vaccine was rolled out.
Right.
But there was no excess that could be associated
with any pathogen, there was no excess mortality.
This is like country after country.
There are many, many countries in Latin America,
in the equatorial region, you know,
where there is no excess mortality
until the vaccines are rolled out.
That's true.
And there are some analyses during the pre-vaccine era
of the pandemic.
When there was a death, it tended to occur in people,
you know, that were already beyond their life expectancy.
Right. So that would not show up as an excess
in all cause mortality.
That's right.
Yeah. So listen, thank you so much for your time.
Jay, thanks for hosting us.
And I've learned so much.
I, you know, I hope your message continues to get out there.
You're doing terrific work.
Thanks, Peter.
These types of ecological analyses are very important.
They do have to be reconciled
because you're reporting real information.
So now it's all about interpretation
and reconciliation with what we understand
and other sources of data.
I agree with you.
There's not a single prospective double-blind,
randomized, placebo control trial
that showed the vaccines reduced the rate of death.
And Peter, the best.
Peter, the best.
Over 3,400 papers in the previous literature
of vaccine injuries, disabilities, and fatal cases.
And we have many sources of data.
Sadly, the Nobel laureates will not have their discovery
linked to you.
But remember, Peter, the reason why they got awarded
Nobel prize is because it lasts much longer
than they ever anticipated.
It's turned out so much better.
When they released this technology,
they told us it was going to last a few weeks.
It lasts much longer.
That's why they decided to give them the Nobel.
You know, that's a great point.
Now listen, synthetic messenger RNA
was injected to produce a missing protein like insulin
and a type 1 diabetic or alpha-glycocidase and Fabrice's.
Long-acting would be good.
But when one's producing an antigen,
a potentially lethal protein like the spike protein,
long-acting is bad.
You're right, Jay.
You'd want it just there for very briefly,
like tetanus, toxoid, and out.
Listen, you guys, I have to finish up now.
Yes, I'm sorry.
Thank you so much for having me on the program.
Thank you very much, Peter.
Cheers.
Bye.
Nice to meet you.
Bye now.
So I think, yes, oh, that's perfect.
Wow, I can't believe how well that went
despite the fact that I dropped the ball.
Are we on, are we on, are we live now?
Yeah, we still are live, yes.
OK, cool.
I'm really, really happy with that.
You have become a real jogger knot
with presenting this in a way that, again, you know,
I anticipated that the death certificates
would come in as a discussion point.
But without wanting to interrupt, then,
there was a lot of delay of death reporting in 2020.
And so only in retrospect can this be sorted out.
If you tried to do this in 2020,
you would not have seen this signal.
Right, right.
Yeah, no, they all cause mortality data is solid data.
And it's certified up to the date, up to the latest date,
where they've certified it.
And it never changes.
They never go back and change it and manipulate it.
They never do.
There is no historic example of that.
So it's good data.
Yeah, I think that this is the kind of bedrock
that a movement can be built on in the sense of, you know,
really trying to wear before when we would try to open
people's eyes.
There wasn't a lot of, you know, concrete to stand on.
And so it's hard to tell people where to go.
And I think now with your work, and I
think Jessica Hockett also has a real knack for showing
people, you know, look, it's not there.
And these kinds of sharp, clear presentations of data,
which show that there was not this biological phenomenon
that we were told there was.
It's not muddled by interpretation.
You don't have to convolute the interpretation
with the data to get the data.
It's the raw.
You know, this is it.
Mortality versus time.
And the way to get excess mortality compared
to the historic trend is relatively straightforward.
You have to be careful.
You have to do statistically correctly.
If the signal is small, there's some uncertainty.
But these signals are huge.
The excess mortality, when it occurs, is very big.
So this is robust data.
Yes.
Yeah, it's solid.
Can I ask you do you know, one of the things
that happens when I present this solid data,
a lot of the people who are uncomfortable with the idea
that the government is not doing what it's said
and that they've been lying and that maybe these
are bad for you and so on, they will just go off the deep end
with, oh, but it's a new variant that's come into play.
You know, exactly at the moment where
you have this sharp roll out of a booster to 80 plus year olds,
you had a variant come in affecting those populations
at exactly the same time.
And this has happened every time.
So when you say it's a booster that is directly
temporally associated with each of these peaks
in every age group and in every country you've looked at,
when you roll out a booster, I would say that it's a variant.
OK, so this is the kind of argument you get into.
And you know, as well as I do, that the methodology
for deducing the prevalence of variants in a society
is just complete garbage science, right?
Were you able to see garbage in your data
the signal of, so I would assume,
and I'm just going off my gut here,
that the first two shots had a pretty wide uptake relative
to the third and then relative to a booster.
And the boosters would have been taken up
by more and more old vulnerable people.
Is that not correct?
Yeah, but you see, we've got data by age group.
So the data by age group is both for the vaccination
and for the mortality.
Right.
OK, so it's how many people in that age group were injected.
I see.
And don't forget also that the elderly people
get really targeted.
There is this incredibly criminal, baseless policy
that you have to protect them, therefore, you
have to prioritize them for injection.
And we've proven now that that is the opposite of what you
should do.
If you want to do a correct risk benefit analysis,
you have to take into account that the risk of harm,
the risk of death, increases exponentially
with age for God's sakes.
So that has to fold in.
And they're not doing that whatsoever.
They're assuming a flat line.
And that's part of the problem.
So the booster is what we see in our data
is that the boosters are definitely more toxic as you
go to more and more advanced rates.
So I can show you, by age, in a country that has good data,
doses 1 and 2, you get this exponential rise.
But then the first booster, it's higher.
It's the same doubling time, but it's a higher rise.
And then those four, it's an even higher rise.
And that systematically occurs every time.
I really think that makes perfect sense
with what we think the prevailing immunological mechanism
is, and every time you activate it,
you have potentially more of a catastrophic reaction.
Yeah, a simple-minded person, like I described this in our paper,
we compare the vaccines to being exposed to a toxic substance.
And in that field, when you look at toxicology studies
and so on, if the animal or the subject hasn't
had time to recover from a first dose,
then the second dose adds significant more damage.
It's often it's not even linear.
And then you can induce death that way.
So successive doses, in a short enough time
that you don't recover from the damage from the last dose,
will do that, will do that as well.
That's just a really simple-minded approach,
ignoring all the immunological theory, right?
Yeah, but that's in a way very powerful.
I mean, that's what you want.
Yeah, yeah.
You want signals that emerge despite trying to miss them,
despite trying to account for all.
I mean, I think it's brilliant.
And because also it gives me hope,
because there is also this possibility
that they had scrambled the numbers sufficiently
so that the signal would be gone.
Well, you know, the numbers, the data is scrambled
when you look at all ages data.
It's a lot harder to see the synchronicities
and the correlations in time
with data that's not discriminated by age group.
Because, in fact, is exponential, you see?
And because they're vaccinating different ages
at different times, you get a lot of scrambling there.
So the first people who looked at this
who would just do the easiest thing,
they'd wave their arms and say,
I can't see anything here.
But then once you start to discriminate by age,
oh my God, these signals just come right out, you know?
Yeah.
Well, basically before you discriminate by age,
you're comparing a potato to a potato.
And then as soon as you have a given age group,
you've got a series of spikes.
Instead of a potato, it's like a series of spikes, you see?
Yep.
So it's very interesting in that sense.
So our next paper, Jay, is going to be,
what is your next paper?
Our next paper is going to be more than 125 countries.
We're just going to do the whole world.
And we're almost done.
We've got all the data analysis done and everything.
And we're just setting it up to illustrate it as best we can.
And we found some stunning results.
Really interesting results that teach you a lot about health
and about the nature of what happens
when you do this on the world, right?
So we're hoping to get this next paper out
within a month or two.
And we're working full blast for this one.
We're using a statistical method called cluster analysis
in order to find the countries that had similar behaviors
in terms of their mortality.
And we find these very definite clusters come out
across different continents and so on
that we can ascribe, that we can interpret
in terms of what was going on there.
So it's very neat.
So kind of an habit to show do countries behave the same or not.
So if you just look at a bunch of all cosmortality curves,
one after the other, it's hard to make head or tail out of it
because they can be so different and they're all over the place.
And but if you use a statistical method to say,
well, which of these belong together, you know,
that kind of thing, you start to see these patterns
and you start to then you look for factors
that correlate to that behavior
and you can say intelligent things.
So that's our next paper.
We're going to show how you can do that.
And what can you give us a teaser?
Like what kinds of things?
Is it protocols that correlate?
OK, OK.
What are the main signals that comes out?
It's really stunning, is the incredibly large mortality
that happened before the COVID,
before the vaccines were rolled out
in the Eastern bloc in Russia,
in Russia and the Eastern bloc countries,
had huge mortality, OK?
And it wasn't, this is interesting
because it didn't occur right after the pandemic was announced.
They didn't have that spike that the Western world had.
But then the winter that followed,
after, you know, almost a year of going crazy with,
we have to save ourselves,
the winter that followed was incredibly destructive.
Wow.
Huge mortality.
And we believe that that is related to the safety net
that baby boomers lost at the dissolution
of the Soviet Union in the early 1990s.
So these are the people at the ages
that are vulnerable to die at high risk of dying.
They no longer have the safety net
that the state was guaranteeing for them.
They're in, you know, they're in poverty.
They're in difficult times.
And the government is coming in and threatening
to vaccinating them, starting to roll out the flu shots
and forcing them to be isolated,
forcing them, you know, to be psychologically stressed
and so on, massive deaths in that group.
Wow.
Yeah.
That's going to be a story inside of stories
that it sounds like to me.
Yeah, we've got a lot of stories inside of stories
when we start looking at the world like that.
Yes.
And the beautiful thing about looking at the world too
is it's a really beautiful illustration
of epidemiology itself.
You know, you see this no seasonal pattern
in the equatorial region.
It's reversed in the southern hemisphere.
It's the other way in the northern hemisphere.
The magnitude changes with latitude,
but you also have the underlying population effects.
So it's quite fascinating.
The thing I regret most is that we can't have data
for China, they won't give it.
And there's no data for equatorial Africa,
which would be really important.
But then again, if we were,
if we were getting good mortality data in equatorial Africa,
that would probably put a lot of pressure
on the people who exploit those countries
to stop being so violent and so cruel.
So maybe, you know, it kind of goes together.
They're totally oppressed and exploited,
and we don't know anything about their mortality.
Crazy, I had no idea that.
That must've been a kind of a surprising find or not
that that was...
Yes, when we make a map of the world,
the middle of Africa is white because there's no data.
That is not a good sign for living in Africa.
You cannot find data.
And the people who project and who guess the data,
like the UN does this, it's completely unreliable.
Wow.
Yeah.
That is terrifying, actually, if you think about it.
If you live in a place where the UN does not have data
on all cause mortality, that must mean bad things.
Yeah.
So those are the kinds of things you discover
when you start to take a broad brush approach to all of this.
Yep.
Well, I appreciate your flexibility here.
And this, you've been, you've been just fabulous.
I didn't mean to cut you off,
but I feel like I'm taking too much of your time now
given the deal we had.
It was a pleasure to be here, as always.
I think I was here once or twice before.
Yes.
And we should have you again,
especially if you're gonna have another paper out.
It was really, I'm happy that we have this connection now
so that we can get an article in the defender right away
and that we can get the rest of the network away
as soon as the thing is out there.
So just keep me in the loop.
I will absolutely do that.
Yes.
And I'm sorry again for screwing up the time
and thank you for being so flexible
that we actually caught Peter anyway.
I apologize, I've done it myself.
I think you did an excellent job.
I think Peter's got some stuff to think about.
And if he didn't have an interview light after this,
I bet he'd still be talking to us.
Yeah.
Yeah, I think it gave him things that he didn't,
he didn't go to the place where,
no, no, there was the COVID was a terrible thing.
We could have saved people by treating it.
He didn't go there.
No, he didn't.
And he did when I interviewed him a few months ago.
And so I do think there's been movement
and also you may not be aware,
but he actually basically came out
and questioned the vaccine schedule in America
a couple of weeks ago,
which was, he also admitted was a first for him.
He might be shifting
and this may have been a very big meeting.
So let's make many people are evolving.
I've met a lot of immunologists
who are questioning fundamental immunology, you know,
themselves.
They've come to it there.
Well, it's fundamental to fundamental immunology
is that respiratory viruses are fought off
by seroprevalence antibodies.
And then they definitely need to revamp it.
Goodness sakes.
Okay, well, thank you very much for joining me, Danny.
And I will, I'm just so pleased
that we became friends and please stay in touch
and I'll have you on again in time.
I'm here.
Okay, thank you.
Thank you.
Bye-bye now.
Wow, I gotta tell you guys, I really,
I really blew that time.
So Peter is in central time
and he said six o'clock,
which is seven o'clock our time.
And I for some reason thought for the,
since I put it in my calendar,
that it was seven o'clock my time, six o'clock Peter's time.
And since Denny and I are in the same time zone,
that made sense.
But actually, which is super annoying in my calendar,
it starts at six p.m.
So all I had to do was look at my calendar
and I would have known that it's not at seven at six.
And lucky for me,
Denny was flexible.
Peter logged back in and we caught him at six 30
and we got Denny to give him a 20 minute elevator pitch.
Actually Peter even started the show for me
because I was so frazzled that I said,
no, no, you guys are already live.
Just start.
And so then Peter actually opened the show.
There was no intro.
One of the sloppiest shows of my life,
but at least we had a nice backdrop for our council fire.
I thought that worked really, really well.
I was actually super pleased with the council fire.
I don't know if I can do this.
Oh, I wanted to do it like that.
This was pretty good, right?
I mean, it looks nice.
And so the next time I have a couple of guests on,
I will go with the council fire again.
I like that.
I have to recruit my boy to get a little better fire
going in the beginning there,
but they almost started the rest of the,
the rest of the, the yard on fire.
That was kind of funny when they were thrown in the cardboard.
I don't know if you noticed that.
Anyway, I'm going to cut it shorty here
because I think it's a beautiful show as it is.
And I don't want to ruin something
that turned out much better than it should have given
that I dropped the ball.
Thank you very much for joining me.
This has been GIGO Biological High Resistance
Illinois Information Brief.
Brought to you by a biologist.
This was a council fire with,
this is what I would have had.
And I've been not dropping the ball.
This is what I would have had.
Oh, whoops.
This is what we did tonight.
It was a council fire with Denny Rancor and Peter McCullough.
And it was an excellent, excellent show.
I really enjoyed it.
I'm still trying to figure out how to use this.
I guess I got, I switched my cameras around.
I was, I was scrambling for my cameras
like at the last minute.
And then I got the mail at 10 after six.
We thought it was at six o'clock.
And so I had to like plug in four HDMI cables
in their right place, turn on three star two computers
and set up the stream in less than a minute
and then get zoom going anyway.
I blew it, but it didn't go bad.
And that's because Denny and Peter really needed to meet.
And so I'm humbled and honored that they got to meet
on this show.
Thanks for joining me.
I'll see you tomorrow.