WEBVTT

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to unfortunately go on another show.

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Dr. Rancourt, it's so nice to meet you.

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Same here, same here, can I call you Peter?

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Sure, thank you, shall we do that?

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Sure, so I thought maybe this would be an opportunity

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to present your recent paper,

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particularly in light of the claim today

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that the Nobel Prize messenger RNA saved

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a minimum of 14 million lives.

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Right, yeah, no, I'd be happy to.

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Did they actually claim that number, Peter?

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14 million, no way.

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Oh my gosh, is that a Nobel announcement

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or some media report or?

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Media report, but they're all following the same script.

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Yeah, there was an article that claimed

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that kind of number, I guess.

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Yeah, it's a 14 to 25 million, if I recall correctly.

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Right, right.

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So I'll just very quickly say the result of our work

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in that regard and then we can interact about it.

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Okay, all right, let's go ahead and hit it.

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Yep.

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You'll go ahead, we're already on.

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I'm not fooling around here.

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You guys are too important for me,

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so I already, you didn't say anything crazy already,

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so go ahead and start, please.

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There you go, we're just starting, okay.

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I am thrilled to be on the program.

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Jay and Dr. Rancourt, thank you so much.

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It's an honor to meet you for the first time.

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Yeah, you know, the world has been very interested

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in your ecological analysis that involved countries

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in the Southern Hemisphere.

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Can you give us a capsule of that,

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of what that paper showed?

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I'd love to, Peter, if I may.

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I'm also thrilled to meet you for the first time,

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even though it's a virtual meeting.

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But here we go, I've been working on all cause mortality

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for a long time.

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Our first paper on the subject was put out

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in June of 2020.

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And in that paper, we said immediately

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that there were hotspots of immediate mortality

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that were synchronous with the announcement

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of the pandemic on the 11th of March, 2020.

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And we said these immediate surges of mortality

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that occur only in hotspots, New York, Northern Italy,

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Madrid, a few places like that, and nowhere else.

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There was nothing like it in 30 of the US states

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that that mortality was inconsistent

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with the spreading respiratory disease.

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It couldn't be that because it was synchronous

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around the world.

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And it was, so it was very granular and synchronous

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and it didn't spread.

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So that was our immediate conclusion

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when we started looking at this all cause mortality back then.

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Well, let me respond to that.

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You know, I was in the thick of it early on

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in the pandemic, you know, trying to innovate

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with ways of helping people.

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But like every other person in America,

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I was watching CNN or any newscast

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and I was watching this mortality meter

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in the upper right-hand part of the screen.

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And let me tell you, when one of my patients dies,

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from the time they die to the time

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I completely finished the death certificate

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and have it registered in the system,

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it's about six weeks.

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So I was wondering how could these instantaneous deaths

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come up on a scoreboard because they each have a six week life?

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Oh, Peter, this is very important.

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We have to be very careful here.

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I'm talking about all cause mortality.

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So that means irrespective of any cause of death

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that anyone might assign.

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In other words, I'm talking about.

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Even when I'm saying the deaths don't get recorded

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until the death certificate is completed.

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Really?

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Yeah, so the idea is there's always a six week.

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No, the databases that I'm working from,

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they actually give you the date of death.

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Now, the certificate might go into the system late,

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but the data is, the actual data is by date of death.

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In other words, sure, there's a lag

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in terms of when you get the certificates in.

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Sometimes there's a lag of as much as a couple of months.

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And so you're updating the mortality data as we go.

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And it's typically a month or two late, you see,

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but then once it goes into the system,

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it's by date of death.

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Right, I know, but even the National Death Index,

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I think runs about six months behind.

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So when deaths occur,

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it's possible that if someone dies in the hospital,

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there may be a more immediate reporting system,

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but most of the time there's no, no, no, but let me explain.

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There's a misunderstanding here.

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See, what I'm talking about is,

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I mean, I'm sitting here in June doing my study, okay?

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And so this is after the 11th of March, 2020.

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No, I understand.

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I'm just telling you real world.

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I'm just wasting the question.

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Real world in March of 2020,

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we were seeing deaths go up every day.

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Yes.

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Okay.

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And it's, I was wondering how in the world

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are those data feeds that simultaneous

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with this lag of a month or two months afterwards?

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Well, you know, in terms of reporting

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so-called COVID deaths in the media or on the TV screens,

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they can be reporting whatever they want,

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but actual official all-cause mortality data

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is the total of deaths for that day in a given jurisdiction.

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So what they might or might not be doing that,

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that pops up on your screen in terms of deaths

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and what those deaths mean, I don't know,

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but I'm working from robust data,

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which is actual all-cause mortality,

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deaths assigned to a given date and it's by day.

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And then some jurisdictions, when they report it,

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they'll give it to you by week.

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Some will give it to you by month

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if it's a smaller jurisdiction

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and they want better statistics,

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but you see it after the fact.

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You have to gather it, collate it,

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and you know up to when it is reliable

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according to the people that are providing the data, you see.

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So up to that date, you've got good data

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and that data never changes

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and that data has been reliable

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since they've been doing this for a hundred years now.

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It's very robust, very reliable data

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and it is collected irrespective of the cause of death.

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So this is just total deaths, okay?

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And then so what you do then is you look at the patterning time

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of those deaths in a given jurisdiction.

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It can be one state in the US,

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it can be the whole country or another country

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and you follow it as a function of time

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and what you will see immediately

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is that in Northern latitude countries,

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it has a seasonal pattern, a very clear seasonal pattern.

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There are always far more deaths in the winter

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than in the summer.

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So there's a winter peak in all cause mortality,

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then you go down to a summer trough

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and this pattern has been known for a hundred years.

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And what's interesting is in the Southern Hemisphere,

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that pattern is reversed

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because they're winters in our summer.

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So they get their maximum of deaths

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in that seasonal pattern during their winter,

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which is our summer.

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And this is a phenomenon that's well known,

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it's basic epidemiology,

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it's been known for a hundred years, it's very striking

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and it's not completely understood exactly why that is, okay?

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There are various models as to why the deaths

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are always higher in the winter,

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including deaths that are related to cardiac problems.

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The only deaths that don't follow that pattern

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are the main tumor type cancer deaths.

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They don't have a seasonal pattern,

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but everything else, the infections, the heart attacks,

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everything that is sensitive to stress, I guess,

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stress induced, they all have a very clear seasonal pattern,

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okay, in terms of mortality.

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And so you know what to expect

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because you have a pattern that you can see for a hundred years

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and you can see it up and down and up and down

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is very regular.

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And then COVID hits and they announce a pandemic,

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they declare a pandemic on the 11th of March 2020

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and you get an immediate surge in that all-cause mortality

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in certain hotspots.

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So only occurring in New York, Northern Italy, Madrid,

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Stockholm, a few places like that,

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very intense, very sharp surges of all-cause mortality

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right after they announced the pandemic.

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So the fact that it is coordinated,

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the fact that the timing of the event

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is related to a political event,

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the announcement of a pandemic

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and that it is synchronous around the world

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and that it's only in those hotspots

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from our perspective,

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this cannot be the spread of a viral respiratory disease

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because it's well known that the time from seeding

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of a new pathogen in a population

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to when you get an actual surge in mortality,

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that time is extremely sensitive to the details

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of the population, of the society,

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of how they contact each other and so on

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and it can vary by months or years even.

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So to have synchronicity like that is impossible

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even with modern airplanes

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because even if you send out flights from the source

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all at the same time,

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then that's the seeding where they land

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but then the time between that original seeding

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to when you'll get a surge in mortality

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is highly dependent on the local circumstances.

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So you can't have synchronicity like that.

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So this was clearly not related to COVID like spread

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or anything like that at the beginning.

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So that was the first thing we noticed

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and then we kept studying all-cause mortality.

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I've written more than 30 papers

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on COVID related things analyzing data and so on.

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And what we find Dr. McCullough is that

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the excess all-cause mortality

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is inconsistent with a viral respiratory spread,

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absolutely inconsistent with it

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because it does not cross borders.

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If you look at European countries

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or states in the United States,

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you can have mortality in one jurisdiction

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and it stops at the border and is not in the other.

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So this mortality at the beginning

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was related to what was being done in those jurisdictions.

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So for example, we wrote a paper with John Johnson

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at Harvard University, we co-authored a paper

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where we showed that when you compare U.S. states

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if you take states that share a border

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and one locked down and the other didn't

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they all cause mortality in the lockdown state

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even though they're very similar

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and they're sharing a border is always higher,

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significantly higher than in the non-lock down state.

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So we're able to, we have a lot of reason

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to come to the very firm conclusion

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that what I believe now is that all of the excess

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all-cause mortality that occurred

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before the vaccines were rolled out

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between when they announced to that time

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is all due to lack of treatment

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and aggressive medical protocols in big hospitals

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and aggressive government measures

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that isolated people and stressed them out

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and including very vulnerable people

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like the 11 million who are disabled

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by serious mental illness in the United States,

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that kind of thing.

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So when you look at the age structure of this mortality

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and its geographical distribution

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and its association with all these things

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that they know were being done in these jurisdictions

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we have concluded that there was,

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there is no evidence for a particularly virulent

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new pathogen that was spreading

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that in fact all of the excess mortality

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everywhere we've looked in the world

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can be understood in terms of

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this is what happens when you do this to people.

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This is what happens when you stop treating them

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for all the usual things that they have

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and when you destroy their lives and stress them out

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and force them to be isolated

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this is what you get, you get this kind of mortality.

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And so this mortality is very heterogeneous

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until you start roll out the vaccines.

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Then once you start rolling out the vaccines

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because that was done pretty much simultaneously

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around the world, you have everywhere

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an increase in all-cause mortality.

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You move into a regime of higher all-cause mortality

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and then you stay there while you're rolling out the vaccines

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and then every time you roll out a booster

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you get a peak, an extra peak in all-cause mortality

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associated in time with that booster.

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And this is stunning, we see this

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and you can do it by age group.

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So you can look at the 90 plus year olds

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or the 80 to 90 year olds and so on.

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And you see a very sharp booster rollout

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because they did it very quickly in a given jurisdiction

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and immediately follows it

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is a very sharp unprecedented peak in all-cause mortality.

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So this is extremely clear, it cannot be an accident

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and therefore you can quantify it.

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You can say, well, how many deaths occurred

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given how many injections you gave?

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So that's what we do, we've been quantifying it.

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And what's surprisingly is what we find

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is that around the world in every jurisdiction

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we've now looked at over 100 countries,

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the mortality risk per injection

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is pretty much the same everywhere.

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So all ages, it's about 0.1%.

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So one, actually we refined it recently

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is 0.126% with an error bar on it.

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And so that means that for every 800 injections,

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one person will die.

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So it's one person per 800 injections.

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Now the important thing is that that risk of death

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per injection is not uniform with age.

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It increases exponentially with age

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and it is dramatically higher the older you are.

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The doubling time by age is four to five years of age.

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Every four to five years of extra age that you have,

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your risk of dying per injection doubles.

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So we hear about the deaths in young athletes and others,

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but I've always been struck by the McLachlan analysis

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from Queens University very early on using the VAERS.

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It was the only VAERS analysis

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that read every single vignette

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and then adjudicated the two different adjudicators

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and they had an agreement process

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to finally adjudicate the death.

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And they only had about 1,200 deaths

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at that point in time in VAERS.

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And what was striking is,

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that was when it was being rolled out in the nursing homes

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in January, February, March.

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It was the seniors, just as you said, that were dying.

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And in the McLachlan analysis,

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it was striking how quick it was.

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There was something like 16% or so died within a few hours.

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Of those who died died within a few hours of the shot.

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A large fraction was within 24 hours.

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And so it was almost as if the reactor genicity of the shot

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or the early production of the spike protein

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from the genetic material we know this occurs within an hour.

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We know it's circulatory in the bloodstream.

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It's simply not this lethal protein

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just is not tolerated by the elderly.

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It makes sense.

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In the McLachlan analysis,

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86% of the time there was no other explanation.

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They were in their usual state of health.

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They took the vaccine and then they succumbed to death.

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Absolutely.

16:34.760 --> 16:37.800
We also studied the VAERS data of the United States.

16:37.800 --> 16:40.920
And it's very, very clear that the deaths that do occur,

16:40.920 --> 16:44.360
most of them, there's a peak around three or four or five days

16:44.360 --> 16:45.880
from vaccination.

16:45.880 --> 16:48.600
It's very clear, as you say,

16:48.600 --> 16:52.520
that's clear in the VAERS data as it is.

16:52.560 --> 16:57.560
And also the VAERS data shows dependence on age

16:58.200 --> 16:59.680
that is exponential.

16:59.680 --> 17:01.440
We wrote a little paper about that.

17:01.440 --> 17:04.360
So it's seen directly in the VAERS data.

17:04.360 --> 17:07.600
But what's important to realize is that now that we've

17:07.600 --> 17:11.680
quantified it on the scale of entire populations,

17:11.680 --> 17:16.680
using these peaks, we now know that the risk of death

17:17.200 --> 17:19.600
is much higher than what you would conclude

17:19.600 --> 17:21.080
from the VAERS data.

17:21.080 --> 17:25.080
It's much higher than that because it's,

17:25.080 --> 17:29.800
well, as I said, so it's, if you use our numbers,

17:29.800 --> 17:33.320
it would correspond to more than 0.2%

17:33.320 --> 17:36.240
of the world population that would have died

17:36.240 --> 17:39.200
from a direct result of being injected

17:39.200 --> 17:41.680
in the last, less than three years.

17:41.680 --> 17:45.720
It would have been 17 million, 17 million people.

17:45.720 --> 17:46.560
It would have been.

17:46.560 --> 17:47.400
17 million, right.

17:47.400 --> 17:49.440
That's a roundabout number I've heard.

17:50.280 --> 17:53.040
In the United States, in the domestic VAERS data,

17:53.040 --> 17:57.000
we're at over 18,000 deaths that the CDC,

17:57.000 --> 17:58.880
now when we report a death,

17:58.880 --> 18:00.920
and I've reported a death to the VAERS system

18:00.920 --> 18:02.000
as a practicing doctor.

18:02.000 --> 18:06.000
So I know what it takes and the CDC does receive it.

18:06.000 --> 18:07.920
It gets a temporary VAERS number.

18:07.920 --> 18:09.280
It waits for the death certificate.

18:09.280 --> 18:11.880
Again, that's six weeks later, comes in.

18:11.880 --> 18:13.920
And then it ultimately gets a permanent VAERS number.

18:13.920 --> 18:17.240
So what's up in VAERS for permanent VAERS number deaths?

18:17.280 --> 18:20.080
It's about 18,000, 1,100.

18:20.080 --> 18:22.560
It's within the first day of the shot.

18:22.560 --> 18:25.280
Okay, so it's very tightly temply related.

18:25.280 --> 18:28.360
In the FDA testimony at the VRBAC meetings,

18:29.280 --> 18:34.160
independent scientists have put a under-reporting factor

18:34.160 --> 18:36.480
on the deaths at about 30.

18:36.480 --> 18:38.560
In the peer-reviewed literature, there's one paper

18:38.560 --> 18:40.680
that is pointing to 40 as a number.

18:40.680 --> 18:44.920
But if we take 30, we're looking at just under 600,000

18:44.960 --> 18:48.080
Americans dying with the vaccine.

18:48.080 --> 18:52.200
We're about 4% of the U.S. population.

18:52.200 --> 18:54.720
So when you kind of do that number,

18:54.720 --> 18:59.640
you're gonna get out there to that 20 million number.

18:59.640 --> 19:00.480
Very close.

19:00.480 --> 19:03.240
That's one approach is to try to estimate

19:03.240 --> 19:05.680
the under-reporting in VAERS.

19:05.680 --> 19:08.520
That's a very, it's a relatively tenuous approach

19:08.520 --> 19:11.080
because there's a lot of uncertainty involved

19:11.080 --> 19:13.120
in trying to do that.

19:13.120 --> 19:16.600
One thing that's different is that the culture,

19:16.600 --> 19:20.960
the propaganda, is very, very different before the pandemic

19:20.960 --> 19:23.720
is declared and then after the pandemic is declared.

19:23.720 --> 19:26.640
And that has a big influence on whether or not MDs

19:26.640 --> 19:29.600
will report and so on and people also,

19:29.600 --> 19:33.560
whether or not people feel that the person that died,

19:33.560 --> 19:35.200
it could have been that and so on.

19:35.200 --> 19:40.000
So it's very hard to estimate that under-reporting rate

19:40.000 --> 19:41.400
and it will be different for deaths

19:41.440 --> 19:44.040
and for a major adverse reaction and so on.

19:44.040 --> 19:45.960
So that's a difficult thing to do

19:45.960 --> 19:49.080
but I respect people who try to do that.

19:49.080 --> 19:50.280
Yeah, it's difficult.

19:50.280 --> 19:53.200
It's just one of many methods.

19:53.200 --> 19:55.480
The practical aspects of it are you're right.

19:55.480 --> 19:59.800
In VAERS, paper by Meister and colleagues from 2016

19:59.800 --> 20:03.480
said about 86% of the reports are by a doctor,

20:03.480 --> 20:06.200
a healthcare worker or a pharmaceutical company

20:06.200 --> 20:07.920
which can report to VAERS.

20:07.920 --> 20:10.200
So you know, individuals don't report.

20:10.200 --> 20:13.680
I can tell you, I practically can't do the report

20:13.680 --> 20:15.800
unless I have the vaccine card.

20:15.800 --> 20:18.520
So many deaths at home that are brought in,

20:18.520 --> 20:20.840
you know, there's just no way I'm gonna have the vaccine card.

20:20.840 --> 20:22.120
It's just, I just can't do it.

20:22.120 --> 20:24.280
So it really has to be a patient under my care.

20:24.280 --> 20:25.880
I have to suspect it.

20:25.880 --> 20:28.040
The family has to provide the vaccine card.

20:28.040 --> 20:31.040
Then I go through the laborious part of doing the entry.

20:31.040 --> 20:34.320
Now, it's interesting in the first year of the pandemic,

20:34.320 --> 20:39.320
there was a paper using census data

20:39.400 --> 20:40.920
in vaccine administration,

20:40.920 --> 20:44.120
it's ecological analysis, pentas, octos and cell equipment.

20:44.120 --> 20:45.920
You know, they came up with a number

20:45.920 --> 20:49.720
around 170 or so 1,000 people,

20:49.720 --> 20:52.600
Americans died of the vaccine in the first year.

20:52.600 --> 20:55.160
And then Mark Skidmore using social networks,

20:55.160 --> 20:56.560
a different analysis.

20:56.560 --> 21:01.560
He came up with about 278,000 individuals.

21:02.480 --> 21:04.640
And now this VAERS underreporting,

21:04.640 --> 21:08.880
again, divide that 600 by, you know, by the 2021 numbers.

21:09.320 --> 21:11.680
We have about three sources of evidence.

21:11.680 --> 21:16.680
Well, we calculated the risk of dying per injection.

21:18.640 --> 21:21.200
And we estimated the best value for the United States.

21:21.200 --> 21:24.120
And we came up with a number of about 300,000 deaths

21:24.120 --> 21:26.080
that would have been, that would have been.

21:26.080 --> 21:28.720
In what overall or just in 2021?

21:29.760 --> 21:32.120
That at the time that we wrote the paper,

21:32.120 --> 21:36.240
it's almost, no, no, no, I mean vaccine deaths.

21:36.240 --> 21:37.240
Yeah.

21:37.280 --> 21:38.640
It would be a little bit more now

21:38.640 --> 21:41.040
because there have been more vaccinations and so on.

21:41.040 --> 21:43.400
But it was roughly that kind of number.

21:43.400 --> 21:45.080
It was at the time, the same time

21:45.080 --> 21:47.640
that Mark Skidmore had published his paper,

21:47.640 --> 21:49.440
we were publishing ours at the same time

21:49.440 --> 21:51.440
and we came up with the same number.

21:51.440 --> 21:52.280
Basically.

21:52.280 --> 21:55.760
No, no, because we have a national death index

21:55.760 --> 21:59.840
and because we have vaccine administration data

21:59.840 --> 22:01.920
and in some countries like, you know, Denmark,

22:01.920 --> 22:05.080
I just visited Denmark have exquisite data systems.

22:05.080 --> 22:08.040
It's simply merging the vaccine administration data

22:08.040 --> 22:09.720
and the death data and doing, you know,

22:09.720 --> 22:12.400
a reasonable temporal analysis.

22:12.400 --> 22:13.640
You know, I've led, you know,

22:13.640 --> 22:16.440
over two dozen day safety monitoring boards

22:16.440 --> 22:19.320
for novel drugs, devices, other things,

22:19.320 --> 22:22.600
for the FDA, for the NIH, for BARDA.

22:22.600 --> 22:26.960
And we always use a 30 day empiric number.

22:26.960 --> 22:30.080
So any death that comes within 30 days

22:30.080 --> 22:32.000
of an experimental product,

22:32.000 --> 22:34.200
it just counted on the product period.

22:34.200 --> 22:37.480
We don't have to go weeding through any jurisdiction,

22:37.480 --> 22:42.480
any country that has a detailed data set of deaths

22:42.640 --> 22:45.920
and that for those individuals who died,

22:45.920 --> 22:50.600
you can know when they were vaccinated with this vaccine

22:50.600 --> 22:52.400
and how many times they were vaccinated.

22:52.400 --> 22:54.480
That would be extremely helpful.

22:54.480 --> 22:56.440
That would be the golden data, right?

22:56.440 --> 22:57.280
That would be.

22:57.280 --> 23:00.400
Listen, there are easily three dozen countries

23:00.400 --> 23:03.360
that have that and they've been pushed.

23:03.360 --> 23:05.960
So the United States has formerly been pushed

23:05.960 --> 23:08.040
by myself and others to merge the data.

23:08.040 --> 23:08.880
They won't do that.

23:08.880 --> 23:11.520
I met with Dr. Manicki,

23:11.520 --> 23:14.360
Vervechi Manicki in Denmark.

23:14.360 --> 23:16.120
They clearly can do it.

23:16.120 --> 23:18.320
And there's not a single country

23:18.320 --> 23:20.800
that will merge the vaccine administration data

23:20.800 --> 23:22.240
with the death data.

23:22.240 --> 23:23.480
Yes.

23:23.480 --> 23:24.360
Well, there you go.

23:24.360 --> 23:25.920
That's one of the problems.

23:25.920 --> 23:28.840
And one of the best things we can do at this stage

23:28.840 --> 23:30.880
without having that merged data

23:30.880 --> 23:32.040
is what we're doing.

23:32.040 --> 23:34.280
You all cause mortality by the time.

23:34.280 --> 23:35.520
And very important.

23:35.520 --> 23:38.400
Now, all cause mortality just quickly

23:40.200 --> 23:42.600
in terms of causes of death in general

23:42.600 --> 23:45.200
before the pandemic in Westernized countries,

23:45.200 --> 23:48.160
it's about 40% known cancer,

23:48.160 --> 23:51.440
40% known cardiovascular disease

23:51.440 --> 23:53.520
and about 20% other causes.

23:53.520 --> 23:56.000
And so cancer and cardiac disease are always

23:56.000 --> 23:58.480
kind of neck and neck for the number one causes.

23:58.480 --> 24:01.480
But the point is in almost every analysis,

24:01.480 --> 24:05.480
the vast majority of the cause of death is known.

24:05.480 --> 24:07.520
In fact, I reviewed a paper

24:07.520 --> 24:11.040
even among college age kids who die.

24:11.040 --> 24:13.440
And the number was far in excess

24:13.440 --> 24:16.720
of having the vignette known.

24:16.720 --> 24:19.120
It was a suicide, a motor vehicle accident,

24:19.120 --> 24:22.720
a drug overdose or a known cancer case.

24:22.720 --> 24:27.280
During COVID, the excess mortality that we found

24:27.280 --> 24:29.800
before the vaccines were rolled out

24:29.800 --> 24:33.040
could matched very well what the government

24:33.040 --> 24:35.440
was calling COVID deaths, okay?

24:35.440 --> 24:39.880
In terms of excess mortality by time,

24:39.880 --> 24:42.640
with all the bumps and all the ups and downs

24:42.640 --> 24:46.600
nationally for the US, the COVID deaths

24:46.600 --> 24:48.640
that the US was reporting

24:48.640 --> 24:51.480
matched that excess mortality quite closely.

24:51.480 --> 24:53.200
But when you looked at their data,

24:53.200 --> 24:55.680
they actually admitted that up to half

24:55.680 --> 24:58.160
and even more than half depending on the state

24:58.160 --> 25:03.160
of those deaths had comorbidity of bacterial pneumonia, okay?

25:04.680 --> 25:08.000
And at the same time, the prescription rates

25:08.000 --> 25:12.240
for antibiotics were dropped by 50% across the Western world.

25:12.240 --> 25:15.640
So we believe that a lot of people died

25:15.640 --> 25:18.700
from bacterial pneumonia during this period.

25:21.200 --> 25:24.920
In fact, the southern states in the United States

25:24.920 --> 25:27.200
normally get two to three times

25:27.200 --> 25:29.760
more prescriptions of antibiotics.

25:29.760 --> 25:30.800
I don't know if you knew that,

25:30.800 --> 25:32.400
but the poor southern states

25:32.400 --> 25:34.320
where there's higher levels of poverty,

25:34.320 --> 25:36.520
they get a lot of prescriptions of antibiotics,

25:36.520 --> 25:37.840
those were cut.

25:37.840 --> 25:41.800
What we found is that all cause mortality correlated perfectly

25:41.800 --> 25:44.560
with the fraction of the population

25:44.560 --> 25:46.760
that was living in poverty in the United States.

25:46.760 --> 25:50.440
Yeah, it's just stunning.

25:50.440 --> 25:51.800
It's a straight line.

25:51.800 --> 25:55.560
So if in a state that had no people living in poverty,

25:55.560 --> 25:57.200
there would have been no excess death

25:57.200 --> 26:00.800
according to this proportionality that we found.

26:00.800 --> 26:05.600
And so it was the poor people, they tend to be obese,

26:05.600 --> 26:07.880
they normally get prescribed a lot of antibiotics,

26:07.880 --> 26:10.400
meaning they're susceptible to lung infections.

26:10.400 --> 26:13.200
Those are the people who died in the United States

26:13.200 --> 26:16.320
and especially the elderly among in that group.

26:16.320 --> 26:18.720
Yeah, I think this is really, really important

26:18.720 --> 26:20.320
for the audience to hear.

26:20.400 --> 26:22.400
In just a minute we have left, though,

26:22.400 --> 26:27.400
I do have to ask you for your interpretation of this.

26:28.120 --> 26:30.720
Two former years of Pennsylvania scientists,

26:30.720 --> 26:33.760
a man or woman were awarded the Nobel Prize

26:33.760 --> 26:37.120
for their work in modifying messenger RNA

26:37.120 --> 26:40.000
to make it more durable in the human body,

26:40.000 --> 26:43.480
not really for creating the whole entity.

26:43.480 --> 26:48.000
There's over 9,000 patent documents on messenger RNA

26:48.000 --> 26:51.120
and the United States has been in this business since 1985.

26:51.120 --> 26:54.760
You know, the top patent holders for messenger RNA

26:54.760 --> 26:59.120
are a curvac and Sanofi and BioNTech Moderna

26:59.120 --> 27:01.360
in the US government, but no single person

27:01.360 --> 27:03.240
quote invented messenger RNA.

27:03.240 --> 27:07.560
But these two people just got the Nobel Prize for modifying it

27:07.560 --> 27:09.080
for COVID-19 vaccines.

27:09.080 --> 27:11.520
But all the press releases today

27:11.520 --> 27:14.480
say that the COVID-19 vaccines have saved

27:14.480 --> 27:17.720
somewhere between 14 and 25 million lives.

27:17.720 --> 27:19.880
The vaccines have saved lives.

27:19.880 --> 27:22.360
Dr. Rancourt, is there any way

27:22.360 --> 27:25.960
that COVID vaccines could be given this type of attribution

27:25.960 --> 27:27.280
of saving lives?

27:27.280 --> 27:31.320
Listen, it's unambiguous.

27:31.320 --> 27:34.600
All cause mortality by time in all countries

27:34.600 --> 27:36.080
that we've studied across the world,

27:36.080 --> 27:39.360
there is not a single example of evidence

27:39.360 --> 27:41.960
where you could conclude that lives were saved

27:41.960 --> 27:43.760
by the vaccine rollouts.

27:43.760 --> 27:46.280
There is no decrease in all cause mortality.

27:46.280 --> 27:47.640
It's the opposite.

27:47.640 --> 27:50.280
You go to a higher regime of all cause mortality.

27:50.280 --> 27:51.880
And then when you roll out the boosters,

27:51.880 --> 27:54.920
you get extra peaks on top of that fire regime.

27:54.920 --> 27:57.800
There is no jurisdiction where you can say,

27:57.800 --> 27:59.560
uh-huh, the vaccines are coming in.

27:59.560 --> 28:01.440
Now the deaths are coming down.

28:01.440 --> 28:03.240
No way, that does not happen.

28:03.240 --> 28:04.320
It never happens.

28:04.320 --> 28:05.680
It's the opposite.

28:05.680 --> 28:09.400
You have temporal association between vaccine rollouts

28:09.400 --> 28:13.320
and extra excess mortality.

28:13.320 --> 28:16.760
This may be the first Nobel Prize

28:16.800 --> 28:19.920
that's associated with increased worldwide mortality.

28:19.920 --> 28:22.160
I'll have to go back and year by year.

28:22.160 --> 28:24.800
But what a stunning observation.

28:24.800 --> 28:26.560
It's a bit of fascinating interview.

28:26.560 --> 28:27.400
Thank you so much.

28:27.400 --> 28:29.280
The other thing I have to say just quickly

28:29.280 --> 28:31.800
is that there are many, many countries around the world

28:31.800 --> 28:34.200
like Australia, Israel, many, many countries

28:34.200 --> 28:37.120
where there was absolutely no excess

28:37.120 --> 28:40.720
all cause mortality until the vaccine was rolled out.

28:40.720 --> 28:41.560
Right.

28:41.560 --> 28:44.800
But there was no excess that could be associated

28:44.800 --> 28:47.880
with any pathogen, there was no excess mortality.

28:47.880 --> 28:49.640
This is like country after country.

28:49.640 --> 28:52.840
There are many, many countries in Latin America,

28:52.840 --> 28:55.520
in the equatorial region, you know,

28:55.520 --> 28:57.600
where there is no excess mortality

28:57.600 --> 29:00.080
until the vaccines are rolled out.

29:00.080 --> 29:00.920
That's true.

29:00.920 --> 29:04.080
And there are some analyses during the pre-vaccine era

29:04.080 --> 29:04.920
of the pandemic.

29:04.920 --> 29:08.560
When there was a death, it tended to occur in people,

29:08.560 --> 29:11.720
you know, that were already beyond their life expectancy.

29:11.720 --> 29:13.960
Right. So that would not show up as an excess

29:14.000 --> 29:15.080
in all cause mortality.

29:15.080 --> 29:16.000
That's right.

29:16.000 --> 29:18.840
Yeah. So listen, thank you so much for your time.

29:18.840 --> 29:20.160
Jay, thanks for hosting us.

29:20.160 --> 29:21.640
And I've learned so much.

29:21.640 --> 29:24.920
I, you know, I hope your message continues to get out there.

29:24.920 --> 29:26.440
You're doing terrific work.

29:26.440 --> 29:27.280
Thanks, Peter.

29:27.280 --> 29:30.640
These types of ecological analyses are very important.

29:30.640 --> 29:32.080
They do have to be reconciled

29:32.080 --> 29:34.680
because you're reporting real information.

29:34.680 --> 29:37.360
So now it's all about interpretation

29:37.360 --> 29:40.160
and reconciliation with what we understand

29:40.160 --> 29:41.320
and other sources of data.

29:41.320 --> 29:42.160
I agree with you.

29:42.160 --> 29:44.880
There's not a single prospective double-blind,

29:44.880 --> 29:46.680
randomized, placebo control trial

29:46.680 --> 29:50.560
that showed the vaccines reduced the rate of death.

29:50.560 --> 29:52.240
And Peter, the best.

29:52.240 --> 29:53.080
Peter, the best.

29:53.080 --> 29:55.600
Over 3,400 papers in the previous literature

29:55.600 --> 30:00.320
of vaccine injuries, disabilities, and fatal cases.

30:00.320 --> 30:02.000
And we have many sources of data.

30:02.000 --> 30:07.160
Sadly, the Nobel laureates will not have their discovery

30:07.160 --> 30:08.000
linked to you.

30:08.000 --> 30:10.720
But remember, Peter, the reason why they got awarded

30:10.720 --> 30:13.520
Nobel prize is because it lasts much longer

30:13.520 --> 30:14.880
than they ever anticipated.

30:14.880 --> 30:17.000
It's turned out so much better.

30:17.000 --> 30:19.000
When they released this technology,

30:19.000 --> 30:21.360
they told us it was going to last a few weeks.

30:21.360 --> 30:23.000
It lasts much longer.

30:23.000 --> 30:25.360
That's why they decided to give them the Nobel.

30:25.360 --> 30:27.120
You know, that's a great point.

30:27.120 --> 30:29.520
Now listen, synthetic messenger RNA

30:29.520 --> 30:33.960
was injected to produce a missing protein like insulin

30:33.960 --> 30:38.080
and a type 1 diabetic or alpha-glycocidase and Fabrice's.

30:38.080 --> 30:39.880
Long-acting would be good.

30:39.880 --> 30:42.560
But when one's producing an antigen,

30:42.560 --> 30:46.200
a potentially lethal protein like the spike protein,

30:46.200 --> 30:47.320
long-acting is bad.

30:47.320 --> 30:48.160
You're right, Jay.

30:48.160 --> 30:50.080
You'd want it just there for very briefly,

30:50.080 --> 30:52.040
like tetanus, toxoid, and out.

30:52.040 --> 30:53.880
Listen, you guys, I have to finish up now.

30:53.880 --> 30:54.880
Yes, I'm sorry.

30:54.880 --> 30:55.920
Thank you so much for having me on the program.

30:55.920 --> 30:57.520
Thank you very much, Peter.

30:57.520 --> 30:58.360
Cheers.

30:58.360 --> 30:59.200
Bye.

30:59.200 --> 31:00.200
Nice to meet you.

31:00.200 --> 31:01.240
Bye now.

31:01.240 --> 31:03.800
So I think, yes, oh, that's perfect.

31:03.800 --> 31:05.960
Wow, I can't believe how well that went

31:05.960 --> 31:08.040
despite the fact that I dropped the ball.

31:09.040 --> 31:13.560
Are we on, are we on, are we live now?

31:13.560 --> 31:15.000
Yeah, we still are live, yes.

31:15.000 --> 31:16.960
OK, cool.

31:16.960 --> 31:18.880
I'm really, really happy with that.

31:18.880 --> 31:21.680
You have become a real jogger knot

31:21.680 --> 31:25.920
with presenting this in a way that, again, you know,

31:25.920 --> 31:29.680
I anticipated that the death certificates

31:29.680 --> 31:32.160
would come in as a discussion point.

31:32.160 --> 31:35.200
But without wanting to interrupt, then,

31:35.200 --> 31:38.480
there was a lot of delay of death reporting in 2020.

31:38.480 --> 31:42.040
And so only in retrospect can this be sorted out.

31:42.040 --> 31:44.560
If you tried to do this in 2020,

31:44.560 --> 31:46.560
you would not have seen this signal.

31:46.560 --> 31:48.880
Right, right.

31:48.880 --> 31:53.520
Yeah, no, they all cause mortality data is solid data.

31:53.520 --> 31:58.640
And it's certified up to the date, up to the latest date,

31:58.640 --> 31:59.680
where they've certified it.

31:59.680 --> 32:00.760
And it never changes.

32:00.760 --> 32:04.240
They never go back and change it and manipulate it.

32:04.240 --> 32:06.280
They never do.

32:06.280 --> 32:08.560
There is no historic example of that.

32:08.560 --> 32:11.840
So it's good data.

32:11.840 --> 32:16.200
Yeah, I think that this is the kind of bedrock

32:16.200 --> 32:20.520
that a movement can be built on in the sense of, you know,

32:20.520 --> 32:24.400
really trying to wear before when we would try to open

32:24.400 --> 32:25.920
people's eyes.

32:25.920 --> 32:29.520
There wasn't a lot of, you know, concrete to stand on.

32:29.520 --> 32:31.440
And so it's hard to tell people where to go.

32:31.440 --> 32:33.960
And I think now with your work, and I

32:33.960 --> 32:37.640
think Jessica Hockett also has a real knack for showing

32:37.640 --> 32:40.320
people, you know, look, it's not there.

32:40.320 --> 32:46.480
And these kinds of sharp, clear presentations of data,

32:46.480 --> 32:52.240
which show that there was not this biological phenomenon

32:52.240 --> 32:53.720
that we were told there was.

32:53.720 --> 32:56.960
It's not muddled by interpretation.

32:56.960 --> 32:59.280
You don't have to convolute the interpretation

32:59.280 --> 33:01.520
with the data to get the data.

33:01.520 --> 33:03.040
It's the raw.

33:03.040 --> 33:04.040
You know, this is it.

33:04.040 --> 33:06.240
Mortality versus time.

33:06.240 --> 33:08.960
And the way to get excess mortality compared

33:08.960 --> 33:12.360
to the historic trend is relatively straightforward.

33:12.360 --> 33:13.280
You have to be careful.

33:13.280 --> 33:15.120
You have to do statistically correctly.

33:15.120 --> 33:18.600
If the signal is small, there's some uncertainty.

33:18.600 --> 33:20.160
But these signals are huge.

33:20.160 --> 33:23.800
The excess mortality, when it occurs, is very big.

33:23.800 --> 33:25.680
So this is robust data.

33:25.680 --> 33:26.800
Yes.

33:26.800 --> 33:29.720
Yeah, it's solid.

33:29.720 --> 33:32.280
Can I ask you do you know, one of the things

33:32.280 --> 33:34.960
that happens when I present this solid data,

33:34.960 --> 33:38.520
a lot of the people who are uncomfortable with the idea

33:38.520 --> 33:41.200
that the government is not doing what it's said

33:41.200 --> 33:43.200
and that they've been lying and that maybe these

33:43.200 --> 33:47.040
are bad for you and so on, they will just go off the deep end

33:47.040 --> 33:50.320
with, oh, but it's a new variant that's come into play.

33:50.320 --> 33:52.680
You know, exactly at the moment where

33:52.680 --> 33:56.000
you have this sharp roll out of a booster to 80 plus year olds,

33:56.000 --> 33:58.520
you had a variant come in affecting those populations

33:58.520 --> 34:00.360
at exactly the same time.

34:00.360 --> 34:02.120
And this has happened every time.

34:02.120 --> 34:05.320
So when you say it's a booster that is directly

34:05.320 --> 34:07.680
temporally associated with each of these peaks

34:07.680 --> 34:10.280
in every age group and in every country you've looked at,

34:10.280 --> 34:14.800
when you roll out a booster, I would say that it's a variant.

34:14.800 --> 34:18.360
OK, so this is the kind of argument you get into.

34:18.360 --> 34:21.880
And you know, as well as I do, that the methodology

34:21.880 --> 34:26.120
for deducing the prevalence of variants in a society

34:26.120 --> 34:28.600
is just complete garbage science, right?

34:28.600 --> 34:32.080
Were you able to see garbage in your data

34:32.080 --> 34:36.280
the signal of, so I would assume,

34:36.280 --> 34:38.600
and I'm just going off my gut here,

34:38.600 --> 34:43.760
that the first two shots had a pretty wide uptake relative

34:43.760 --> 34:46.000
to the third and then relative to a booster.

34:46.000 --> 34:47.640
And the boosters would have been taken up

34:47.640 --> 34:50.440
by more and more old vulnerable people.

34:50.440 --> 34:51.520
Is that not correct?

34:51.520 --> 34:55.440
Yeah, but you see, we've got data by age group.

34:55.440 --> 34:59.720
So the data by age group is both for the vaccination

34:59.720 --> 35:00.960
and for the mortality.

35:00.960 --> 35:01.720
Right.

35:01.720 --> 35:05.720
OK, so it's how many people in that age group were injected.

35:05.720 --> 35:06.160
I see.

35:06.160 --> 35:10.200
And don't forget also that the elderly people

35:10.200 --> 35:12.160
get really targeted.

35:12.160 --> 35:18.320
There is this incredibly criminal, baseless policy

35:18.320 --> 35:20.120
that you have to protect them, therefore, you

35:20.120 --> 35:23.400
have to prioritize them for injection.

35:23.400 --> 35:27.680
And we've proven now that that is the opposite of what you

35:27.680 --> 35:28.120
should do.

35:28.120 --> 35:32.000
If you want to do a correct risk benefit analysis,

35:32.000 --> 35:35.080
you have to take into account that the risk of harm,

35:35.080 --> 35:37.560
the risk of death, increases exponentially

35:37.560 --> 35:39.320
with age for God's sakes.

35:39.320 --> 35:42.560
So that has to fold in.

35:42.560 --> 35:44.600
And they're not doing that whatsoever.

35:44.600 --> 35:46.720
They're assuming a flat line.

35:46.720 --> 35:49.240
And that's part of the problem.

35:49.240 --> 35:53.000
So the booster is what we see in our data

35:53.000 --> 35:56.400
is that the boosters are definitely more toxic as you

35:56.400 --> 35:59.120
go to more and more advanced rates.

35:59.120 --> 36:05.280
So I can show you, by age, in a country that has good data,

36:05.280 --> 36:09.200
doses 1 and 2, you get this exponential rise.

36:09.200 --> 36:11.920
But then the first booster, it's higher.

36:11.920 --> 36:16.240
It's the same doubling time, but it's a higher rise.

36:16.240 --> 36:18.520
And then those four, it's an even higher rise.

36:18.520 --> 36:21.600
And that systematically occurs every time.

36:21.600 --> 36:23.320
I really think that makes perfect sense

36:23.320 --> 36:27.040
with what we think the prevailing immunological mechanism

36:27.040 --> 36:28.480
is, and every time you activate it,

36:28.480 --> 36:32.400
you have potentially more of a catastrophic reaction.

36:32.400 --> 36:35.680
Yeah, a simple-minded person, like I described this in our paper,

36:35.680 --> 36:41.720
we compare the vaccines to being exposed to a toxic substance.

36:41.720 --> 36:45.480
And in that field, when you look at toxicology studies

36:45.480 --> 36:48.360
and so on, if the animal or the subject hasn't

36:48.360 --> 36:51.000
had time to recover from a first dose,

36:51.000 --> 36:55.280
then the second dose adds significant more damage.

36:55.280 --> 36:57.000
It's often it's not even linear.

36:57.000 --> 37:00.040
And then you can induce death that way.

37:00.040 --> 37:03.120
So successive doses, in a short enough time

37:03.120 --> 37:06.280
that you don't recover from the damage from the last dose,

37:06.280 --> 37:08.240
will do that, will do that as well.

37:08.240 --> 37:10.680
That's just a really simple-minded approach,

37:10.680 --> 37:15.160
ignoring all the immunological theory, right?

37:15.160 --> 37:17.280
Yeah, but that's in a way very powerful.

37:17.280 --> 37:19.920
I mean, that's what you want.

37:19.920 --> 37:21.120
Yeah, yeah.

37:21.120 --> 37:25.600
You want signals that emerge despite trying to miss them,

37:25.600 --> 37:28.920
despite trying to account for all.

37:28.920 --> 37:30.480
I mean, I think it's brilliant.

37:30.480 --> 37:34.120
And because also it gives me hope,

37:34.120 --> 37:36.280
because there is also this possibility

37:36.280 --> 37:39.320
that they had scrambled the numbers sufficiently

37:39.320 --> 37:42.840
so that the signal would be gone.

37:42.840 --> 37:47.360
Well, you know, the numbers, the data is scrambled

37:47.360 --> 37:49.400
when you look at all ages data.

37:49.400 --> 37:54.400
It's a lot harder to see the synchronicities

37:54.400 --> 37:56.280
and the correlations in time

37:56.280 --> 37:59.080
with data that's not discriminated by age group.

37:59.080 --> 38:01.360
Because, in fact, is exponential, you see?

38:01.360 --> 38:03.720
And because they're vaccinating different ages

38:03.720 --> 38:07.440
at different times, you get a lot of scrambling there.

38:07.440 --> 38:09.560
So the first people who looked at this

38:09.560 --> 38:11.400
who would just do the easiest thing,

38:11.400 --> 38:13.720
they'd wave their arms and say,

38:13.720 --> 38:16.160
I can't see anything here.

38:16.160 --> 38:19.000
But then once you start to discriminate by age,

38:19.000 --> 38:22.000
oh my God, these signals just come right out, you know?

38:23.120 --> 38:23.960
Yeah.

38:23.960 --> 38:26.960
Well, basically before you discriminate by age,

38:26.960 --> 38:30.040
you're comparing a potato to a potato.

38:30.040 --> 38:32.560
And then as soon as you have a given age group,

38:32.560 --> 38:34.360
you've got a series of spikes.

38:34.360 --> 38:37.880
Instead of a potato, it's like a series of spikes, you see?

38:37.880 --> 38:38.920
Yep.

38:38.920 --> 38:39.760
Yep.

38:39.760 --> 38:42.760
So it's very interesting in that sense.

38:43.600 --> 38:45.440
So our next paper, Jay, is going to be,

38:45.440 --> 38:47.080
what is your next paper?

38:47.080 --> 38:50.480
Our next paper is going to be more than 125 countries.

38:50.480 --> 38:52.600
We're just going to do the whole world.

38:52.600 --> 38:54.920
And we're almost done.

38:54.920 --> 38:57.640
We've got all the data analysis done and everything.

38:57.640 --> 39:01.000
And we're just setting it up to illustrate it as best we can.

39:01.000 --> 39:03.000
And we found some stunning results.

39:03.920 --> 39:07.920
Really interesting results that teach you a lot about health

39:07.920 --> 39:10.480
and about the nature of what happens

39:10.480 --> 39:12.440
when you do this on the world, right?

39:13.360 --> 39:16.040
So we're hoping to get this next paper out

39:16.040 --> 39:17.240
within a month or two.

39:18.240 --> 39:21.880
And we're working full blast for this one.

39:21.880 --> 39:25.680
We're using a statistical method called cluster analysis

39:25.680 --> 39:30.440
in order to find the countries that had similar behaviors

39:30.440 --> 39:32.360
in terms of their mortality.

39:32.360 --> 39:35.400
And we find these very definite clusters come out

39:35.400 --> 39:38.040
across different continents and so on

39:38.040 --> 39:41.120
that we can ascribe, that we can interpret

39:41.120 --> 39:43.320
in terms of what was going on there.

39:43.320 --> 39:45.720
So it's very neat.

39:45.720 --> 39:49.760
So kind of an habit to show do countries behave the same or not.

39:49.760 --> 39:53.160
So if you just look at a bunch of all cosmortality curves,

39:53.160 --> 39:56.240
one after the other, it's hard to make head or tail out of it

39:56.240 --> 39:59.560
because they can be so different and they're all over the place.

39:59.560 --> 40:02.680
And but if you use a statistical method to say,

40:02.680 --> 40:05.440
well, which of these belong together, you know,

40:05.440 --> 40:09.080
that kind of thing, you start to see these patterns

40:09.080 --> 40:12.640
and you start to then you look for factors

40:12.640 --> 40:14.280
that correlate to that behavior

40:14.280 --> 40:16.400
and you can say intelligent things.

40:16.400 --> 40:17.960
So that's our next paper.

40:17.960 --> 40:20.280
We're going to show how you can do that.

40:20.280 --> 40:21.800
And what can you give us a teaser?

40:21.800 --> 40:22.920
Like what kinds of things?

40:22.920 --> 40:25.040
Is it protocols that correlate?

40:25.040 --> 40:27.600
OK, OK.

40:27.600 --> 40:30.240
What are the main signals that comes out?

40:30.240 --> 40:36.160
It's really stunning, is the incredibly large mortality

40:36.160 --> 40:38.640
that happened before the COVID,

40:38.640 --> 40:41.000
before the vaccines were rolled out

40:41.040 --> 40:43.480
in the Eastern bloc in Russia,

40:43.480 --> 40:45.480
in Russia and the Eastern bloc countries,

40:45.480 --> 40:48.040
had huge mortality, OK?

40:48.040 --> 40:49.840
And it wasn't, this is interesting

40:49.840 --> 40:53.400
because it didn't occur right after the pandemic was announced.

40:53.400 --> 40:56.920
They didn't have that spike that the Western world had.

40:56.920 --> 40:59.240
But then the winter that followed,

40:59.240 --> 41:03.400
after, you know, almost a year of going crazy with,

41:03.400 --> 41:05.120
we have to save ourselves,

41:05.120 --> 41:09.120
the winter that followed was incredibly destructive.

41:09.120 --> 41:10.120
Wow.

41:10.120 --> 41:11.000
Huge mortality.

41:11.000 --> 41:17.600
And we believe that that is related to the safety net

41:17.600 --> 41:20.400
that baby boomers lost at the dissolution

41:20.400 --> 41:23.760
of the Soviet Union in the early 1990s.

41:23.760 --> 41:25.520
So these are the people at the ages

41:25.520 --> 41:28.840
that are vulnerable to die at high risk of dying.

41:28.840 --> 41:31.040
They no longer have the safety net

41:31.040 --> 41:33.040
that the state was guaranteeing for them.

41:33.040 --> 41:34.640
They're in, you know, they're in poverty.

41:34.640 --> 41:37.600
They're in difficult times.

41:37.600 --> 41:41.000
And the government is coming in and threatening

41:41.000 --> 41:44.880
to vaccinating them, starting to roll out the flu shots

41:44.880 --> 41:47.240
and forcing them to be isolated,

41:47.240 --> 41:50.960
forcing them, you know, to be psychologically stressed

41:50.960 --> 41:55.560
and so on, massive deaths in that group.

41:55.560 --> 41:56.680
Wow.

41:56.680 --> 41:57.680
Yeah.

41:57.680 --> 41:59.840
That's going to be a story inside of stories

41:59.840 --> 42:01.280
that it sounds like to me.

42:01.280 --> 42:04.040
Yeah, we've got a lot of stories inside of stories

42:04.040 --> 42:06.280
when we start looking at the world like that.

42:06.280 --> 42:07.560
Yes.

42:07.560 --> 42:10.080
And the beautiful thing about looking at the world too

42:10.080 --> 42:12.720
is it's a really beautiful illustration

42:12.720 --> 42:15.120
of epidemiology itself.

42:15.120 --> 42:18.880
You know, you see this no seasonal pattern

42:18.880 --> 42:20.600
in the equatorial region.

42:20.600 --> 42:22.800
It's reversed in the southern hemisphere.

42:22.800 --> 42:25.920
It's the other way in the northern hemisphere.

42:25.920 --> 42:28.120
The magnitude changes with latitude,

42:28.120 --> 42:31.440
but you also have the underlying population effects.

42:31.440 --> 42:33.720
So it's quite fascinating.

42:33.720 --> 42:36.840
The thing I regret most is that we can't have data

42:36.920 --> 42:39.120
for China, they won't give it.

42:39.120 --> 42:41.840
And there's no data for equatorial Africa,

42:41.840 --> 42:44.400
which would be really important.

42:44.400 --> 42:46.120
But then again, if we were,

42:46.120 --> 42:49.880
if we were getting good mortality data in equatorial Africa,

42:49.880 --> 42:52.080
that would probably put a lot of pressure

42:52.080 --> 42:54.400
on the people who exploit those countries

42:54.400 --> 42:58.440
to stop being so violent and so cruel.

43:01.080 --> 43:04.080
So maybe, you know, it kind of goes together.

43:04.080 --> 43:06.840
They're totally oppressed and exploited,

43:06.840 --> 43:09.640
and we don't know anything about their mortality.

43:11.080 --> 43:12.440
Crazy, I had no idea that.

43:12.440 --> 43:15.840
That must've been a kind of a surprising find or not

43:15.840 --> 43:16.680
that that was...

43:16.680 --> 43:18.520
Yes, when we make a map of the world,

43:18.520 --> 43:21.760
the middle of Africa is white because there's no data.

43:21.760 --> 43:24.760
That is not a good sign for living in Africa.

43:24.760 --> 43:26.480
You cannot find data.

43:26.480 --> 43:30.680
And the people who project and who guess the data,

43:30.680 --> 43:33.680
like the UN does this, it's completely unreliable.

43:34.280 --> 43:35.760
Wow.

43:35.760 --> 43:36.600
Yeah.

43:36.600 --> 43:39.160
That is terrifying, actually, if you think about it.

43:39.160 --> 43:42.120
If you live in a place where the UN does not have data

43:42.120 --> 43:45.520
on all cause mortality, that must mean bad things.

43:45.520 --> 43:46.440
Yeah.

43:46.440 --> 43:47.520
Yeah.

43:47.520 --> 43:49.680
So those are the kinds of things you discover

43:49.680 --> 43:53.760
when you start to take a broad brush approach to all of this.

43:56.320 --> 43:57.320
Yep.

43:57.320 --> 43:59.640
Well, I appreciate your flexibility here.

43:59.640 --> 44:02.120
And this, you've been, you've been just fabulous.

44:02.120 --> 44:03.160
I didn't mean to cut you off,

44:03.160 --> 44:05.760
but I feel like I'm taking too much of your time now

44:05.760 --> 44:08.280
given the deal we had.

44:09.800 --> 44:12.240
It was a pleasure to be here, as always.

44:12.240 --> 44:14.240
I think I was here once or twice before.

44:14.240 --> 44:15.080
Yes.

44:15.080 --> 44:16.080
And we should have you again,

44:16.080 --> 44:18.640
especially if you're gonna have another paper out.

44:18.640 --> 44:21.840
It was really, I'm happy that we have this connection now

44:21.840 --> 44:25.680
so that we can get an article in the defender right away

44:25.680 --> 44:29.040
and that we can get the rest of the network away

44:29.040 --> 44:30.320
as soon as the thing is out there.

44:30.320 --> 44:33.000
So just keep me in the loop.

44:33.000 --> 44:34.640
I will absolutely do that.

44:34.640 --> 44:35.400
Yes.

44:35.400 --> 44:37.320
And I'm sorry again for screwing up the time

44:37.320 --> 44:38.960
and thank you for being so flexible

44:38.960 --> 44:40.360
that we actually caught Peter anyway.

44:40.360 --> 44:42.440
I apologize, I've done it myself.

44:42.440 --> 44:44.080
I think you did an excellent job.

44:44.080 --> 44:45.920
I think Peter's got some stuff to think about.

44:45.920 --> 44:47.680
And if he didn't have an interview light after this,

44:47.680 --> 44:49.320
I bet he'd still be talking to us.

44:51.080 --> 44:51.920
Yeah.

44:51.920 --> 44:52.760
Yeah.

44:52.760 --> 44:55.480
Yeah, I think it gave him things that he didn't,

44:55.480 --> 44:57.480
he didn't go to the place where,

44:57.480 --> 44:59.840
no, no, there was the COVID was a terrible thing.

44:59.840 --> 45:02.240
We could have saved people by treating it.

45:02.240 --> 45:03.120
He didn't go there.

45:03.120 --> 45:03.880
No, he didn't.

45:03.880 --> 45:07.840
And he did when I interviewed him a few months ago.

45:09.160 --> 45:11.240
And so I do think there's been movement

45:11.240 --> 45:13.240
and also you may not be aware,

45:13.240 --> 45:15.840
but he actually basically came out

45:15.840 --> 45:18.800
and questioned the vaccine schedule in America

45:18.800 --> 45:20.560
a couple of weeks ago,

45:20.560 --> 45:23.320
which was, he also admitted was a first for him.

45:24.840 --> 45:26.400
He might be shifting

45:26.400 --> 45:28.840
and this may have been a very big meeting.

45:28.840 --> 45:32.720
So let's make many people are evolving.

45:32.720 --> 45:34.400
I've met a lot of immunologists

45:34.400 --> 45:37.760
who are questioning fundamental immunology, you know,

45:37.760 --> 45:38.600
themselves.

45:38.600 --> 45:39.960
They've come to it there.

45:39.960 --> 45:42.680
Well, it's fundamental to fundamental immunology

45:42.680 --> 45:45.200
is that respiratory viruses are fought off

45:45.200 --> 45:47.960
by seroprevalence antibodies.

45:47.960 --> 45:50.800
And then they definitely need to revamp it.

45:50.800 --> 45:51.640
Goodness sakes.

45:53.160 --> 45:55.280
Okay, well, thank you very much for joining me, Danny.

45:55.280 --> 45:57.240
And I will, I'm just so pleased

45:57.240 --> 46:00.240
that we became friends and please stay in touch

46:00.240 --> 46:01.240
and I'll have you on again in time.

46:01.240 --> 46:02.080
I'm here.

46:02.080 --> 46:03.080
Okay, thank you.

46:03.080 --> 46:03.920
Thank you.

46:03.920 --> 46:04.760
Bye-bye now.

46:06.600 --> 46:10.080
Wow, I gotta tell you guys, I really,

46:11.440 --> 46:12.760
I really blew that time.

46:12.760 --> 46:16.520
So Peter is in central time

46:16.520 --> 46:19.280
and he said six o'clock,

46:19.280 --> 46:22.240
which is seven o'clock our time.

46:22.240 --> 46:25.400
And I for some reason thought for the,

46:25.400 --> 46:27.720
since I put it in my calendar,

46:27.720 --> 46:30.960
that it was seven o'clock my time, six o'clock Peter's time.

46:30.960 --> 46:33.680
And since Denny and I are in the same time zone,

46:33.680 --> 46:35.320
that made sense.

46:35.320 --> 46:40.320
But actually, which is super annoying in my calendar,

46:40.600 --> 46:42.320
it starts at six p.m.

46:42.320 --> 46:45.040
So all I had to do was look at my calendar

46:45.040 --> 46:47.840
and I would have known that it's not at seven at six.

46:47.840 --> 46:49.360
And lucky for me,

46:49.360 --> 46:51.240
Denny was flexible.

46:51.240 --> 46:54.120
Peter logged back in and we caught him at six 30

46:54.120 --> 46:57.280
and we got Denny to give him a 20 minute elevator pitch.

46:58.400 --> 47:00.520
Actually Peter even started the show for me

47:00.520 --> 47:03.400
because I was so frazzled that I said,

47:03.400 --> 47:04.960
no, no, you guys are already live.

47:04.960 --> 47:06.320
Just start.

47:06.320 --> 47:08.120
And so then Peter actually opened the show.

47:08.120 --> 47:10.080
There was no intro.

47:10.080 --> 47:12.880
One of the sloppiest shows of my life,

47:12.880 --> 47:17.880
but at least we had a nice backdrop for our council fire.

47:18.880 --> 47:21.880
I thought that worked really, really well.

47:21.880 --> 47:26.120
I was actually super pleased with the council fire.

47:26.120 --> 47:27.280
I don't know if I can do this.

47:27.280 --> 47:29.120
Oh, I wanted to do it like that.

47:30.040 --> 47:31.000
This was pretty good, right?

47:31.000 --> 47:32.120
I mean, it looks nice.

47:33.520 --> 47:36.080
And so the next time I have a couple of guests on,

47:37.920 --> 47:39.760
I will go with the council fire again.

47:39.760 --> 47:40.600
I like that.

47:41.760 --> 47:43.920
I have to recruit my boy to get a little better fire

47:43.920 --> 47:44.880
going in the beginning there,

47:44.880 --> 47:47.360
but they almost started the rest of the,

47:47.360 --> 47:49.120
the rest of the, the yard on fire.

47:49.120 --> 47:51.160
That was kind of funny when they were thrown in the cardboard.

47:51.160 --> 47:52.360
I don't know if you noticed that.

47:52.360 --> 47:54.320
Anyway, I'm going to cut it shorty here

47:54.320 --> 47:57.560
because I think it's a beautiful show as it is.

47:57.560 --> 47:59.200
And I don't want to ruin something

47:59.200 --> 48:01.320
that turned out much better than it should have given

48:01.320 --> 48:02.200
that I dropped the ball.

48:02.200 --> 48:04.240
Thank you very much for joining me.

48:04.240 --> 48:06.440
This has been GIGO Biological High Resistance

48:06.440 --> 48:08.560
Illinois Information Brief.

48:08.560 --> 48:10.720
Brought to you by a biologist.

48:11.720 --> 48:15.440
This was a council fire with,

48:18.200 --> 48:19.800
this is what I would have had.

48:23.320 --> 48:26.360
And I've been not dropping the ball.

48:30.160 --> 48:31.800
This is what I would have had.

48:31.800 --> 48:33.240
Oh, whoops.

48:40.720 --> 48:45.720
This is what we did tonight.

48:46.000 --> 48:51.000
It was a council fire with Denny Rancor and Peter McCullough.

48:51.240 --> 48:54.680
And it was an excellent, excellent show.

48:54.680 --> 48:55.680
I really enjoyed it.

48:58.040 --> 49:00.960
I'm still trying to figure out how to use this.

49:02.680 --> 49:05.200
I guess I got, I switched my cameras around.

49:06.480 --> 49:10.000
I was, I was scrambling for my cameras

49:10.000 --> 49:11.840
like at the last minute.

49:11.840 --> 49:14.520
And then I got the mail at 10 after six.

49:14.520 --> 49:16.800
We thought it was at six o'clock.

49:16.800 --> 49:21.800
And so I had to like plug in four HDMI cables

49:22.800 --> 49:26.160
in their right place, turn on three star two computers

49:26.160 --> 49:28.840
and set up the stream in less than a minute

49:28.840 --> 49:32.080
and then get zoom going anyway.

49:32.080 --> 49:34.560
I blew it, but it didn't go bad.

49:34.560 --> 49:39.560
And that's because Denny and Peter really needed to meet.

49:39.560 --> 49:42.360
And so I'm humbled and honored that they got to meet

49:42.360 --> 49:43.480
on this show.

49:43.480 --> 49:44.320
Thanks for joining me.

49:44.320 --> 49:45.320
I'll see you tomorrow.

