(2024-01-16) - Why Berenson and Surowiecki Trip Over Data Illusions of Vaccine Effectiveness [Rounding the Earth]

v44rsk8 (2024-01-16) - Why Berenson and Surowiecki Trip Over Data Illusions of Vaccine Effectiveness [Rounding the Earth].vtt

WEBVTT

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Hello and welcome back to Routing the Earth.

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Today is Tuesday, the 17th of January, 2024.

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And I'm going to be doing something a little bit different.

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Sorry, I have the 16th, 18th pardon.

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I'm going to be doing something a little bit different today.

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We're going to be talking through some data and I've got my headphones on.

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I'm trying to juggle multiple things.

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This is playing on locals.

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I'm not going to be paying attention to locals if people who are on locals want to jump over to the rumble stream that may be better, especially if you have observations and questions or anything like that, but this is going to be a data conversation.

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I'm also open in a Twitter space.

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Wogpog is running a Twitter space where he is broadcasting this to Twitter or X Twitter, however you say it.

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And I'm listening to it on very low volume.

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So I'm kind of trying to do a little bit of a mental juggle here.

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Now, let's talk about what this is about.

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So.

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Yesterday or the yesterday date, two days ago, maybe.

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I don't know. Yesterday, I'll experience and posts about the military hospitalization data.

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And so this is where the story started.

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We're going to go a lot of places in the story and just to let you know, we're going to be taking a look at a lot of very plain straight forward data.

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I have data from every single county in the United States.

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I have data on county level, state level, and international level.

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But we're going to talk through it. We're going to talk about what it means to zoom in and out of that data and ultimately what that means about the vaccines in terms of their effectiveness.

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I personally believe that the vaccines have basically zero effectiveness, but it might even be calculated as negative effectiveness when you take into account people being harmed and the symptoms sometimes being covered.

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So, you know, where the story starts right now is out there.

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So the US military hospitalization database, I referred to on Tucker Carlson.

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I didn't watch his appearance on Tucker Carlson, but I don't think Alex Berenson is the person who should be discussing those database.

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I went in and went all over the military health database, the DMAT database.

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I went through hundreds and hundreds of queries to figure out what was going on.

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But here he is saying the hospitalization database, very reliable and it is.

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It was created long before COVID or the mRNA's. It contains 1.3 million people, mostly 20 to 40 years old, almost all tab.

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There is no signal. It's anything in the opposite.

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So what does he mean by that? There's no signal.

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What he's looking at is an overall trend.

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There was an overall downward trend going on over the past decade in the conversation.

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And he didn't ask, okay, well, why wasn't it all ready?

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Right.

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And can we unwind some of your causing that downward trend and then we see a signal.

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In other words, what he's doing is he's not correcting it for some variable that is obviously there in order to understand whether the vaccines themselves are creating a signal.

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But here's the thing.

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Over the past decade, we've brought some from the Middle East.

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It's a much less dangerous job.

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Right.

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Death rates went down in military hospitalization rates went down in military and that was all prior to COVID.

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All of those graphs went down with trends.

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So it doesn't really tell us anything about the vaccines.

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But I jumped in and said, okay, well, I was the statistic to take an outside.

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I think you're making a mistake looking at aggregate numbers more affected by duties and deployment.

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So the hospitalization.

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Shut up.

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During 2020.

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There are those responses.

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Unless they're unspoken.

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I'm happy to discuss the data.

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And, you know, every now and there could be.

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It's technically there could be some.

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Some entity went in and early.

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Made people more sick.

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You know, it could be some conspiracy where they literally decided to.

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You know, make true for a job.

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Suddenly have their still misses during.

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Of course, they're more reasonable.

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But, you know, let's look at this.

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This chart that I made by combining two of those.

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We met database.

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I took the hospitalizations and pieces and I divided.

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The first by the second.

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So that we get hospitalizations per chance.

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And you can begin to go up a weekly after vaccine rollout.

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And then we see a sharply.

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In the vaccine and date.

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Of 2021.

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Now that looks like a dose response signal to me.

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And you know, if you look at this.

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You actually get more of that signal.

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With the older group.

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I come back to.

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A little bit.

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People who jump in.

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You know, the realtor.

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Who he makes this argument that I feel like is obnoxious.

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He's like, Oh, that's, you know, we can blame them on the variant of the day.

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Right.

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Anything can be blamed on the variant of the.

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That's what he's doing without any other evidence.

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We saw this worldwide.

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And he presents.

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No evidence.

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We saw this worldwide worldwide.

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We saw his huge fatality.

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He's crashing, getting lower and lower.

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If I'm going to go to this period.

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You've been 21.

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It's almost, it's going to be down.

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You're almost there.

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I'm doing it.

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It's kind of 2022.

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The last time I want.

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So, you know, he's making this.

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I'm okay.

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You see, you got to competing variables.

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Right.

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At best, he's not doing this.

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He's not saying, well, we need to figure out which one it is.

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Cause a point.

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cause it be points to one that exonerates the vaccines.

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That's not going to do science, right?

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You need some other other things to look at.

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You know, illness is historically caused by viruses

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or microbes, you know, if there's debate over this,

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remember, I don't think it even really matters.

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Can you come up with any disease in history,

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whether it's supposed to be virus-commutating

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or back, you're getting something different.

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Come up with some illness ever,

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all of a sudden, started shitting younger people

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in the middle of a minute, not break or anything like that

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or even just a one-year-two another, right?

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Have we ever seen that with the flu?

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One year, suddenly, the proportion of younger people

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dying from the flu is just a fool.

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That proportion explodes, right?

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And I've asked this question to multiple people.

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Nobody's even gonna answer it.

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I can't find it.

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Nobody else has offered it.

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So it's a historical thing to say

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that's happened with COVID, right?

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COVID used it like this mythical demon

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that he shapes and explains everything it wants to.

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That's not science, right?

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That's creating a mythology and using it to explain it late.

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The more obvious variable,

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which the vaccine is something

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was introduced for the very first time.

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That's the thing which is brand new scenario,

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not respiratory illnesses, not viral illnesses.

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And Gabe just saw Dan Gaver, we good on audio or phone.

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The audio's coming through,

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but it's getting really garbled.

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You might want to switch whatever microphone you usually use.

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I think it's picking up.

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I think you're really curious, actually.

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I don't know if it's picking up your microphone

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from the headset, but you might want to change microphones.

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Hi, goodness.

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In the Twitter space,

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I don't know if you can jump into the studio.

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Let's make sure.

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He was doing that before, but I'm just saying,

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I think the audio's on your end

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because I'm having the problem too.

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What you want to make sure is that you're coming.

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If you've changed your audio device

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because I think it's maybe your headphone microphone,

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but I'm not 100% sure.

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So wait, I'm turning you up now so I can hear you,

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but I realize I turned my sound down.

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I'm saying you might want to switch

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in the window settings from your...

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I guess you have a desk microphone.

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Switch to that if you're using the headset one

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because I think the headset one is the problem.

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Okay.

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Sorry about that, everybody.

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I'll try again.

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Hear me better now.

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Yeah, I think it's starting to come in a bit better.

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You might be able to continue from here.

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Okay.

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Well, I hope that was at least good enough

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for people to follow the introduction.

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It may be that you had to turn up the volume

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or if you read the volume.

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But Gabe, do you think that I need to redo the intro

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of where we are with the inters?

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Should I just go from here?

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It was kind of garbled at particular points.

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It might be worth redoing,

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but I think the words did come through.

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It's just...

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Okay.

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It was a hard list.

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Okay.

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I'm going to try to do a quick summary of where we are

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and then hopefully the rest of this goes pretty smoothly.

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Alex Harrison, I guess, was talking with Elan.

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I was talking with...

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Sorry, Tucker Carlson.

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I referred to him with Elan.

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That's really the way that it was.

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And he brought up a military health database

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and he said, you see hospitalizations going down.

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Vaccination.

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And of course, it's already there.

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And so I jump in and say, look, you know,

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let's skip that trend,

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which is probably just bringing troops back from overseas,

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because that's what's been going on for a decade.

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Less combat.

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No new war started for a number of years now.

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But, you know, I said, look,

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let's talk about this because I have a dose response data

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from the hundreds and hundreds of hours

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going to require making charts.

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And I saw a number of...

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There's usually dose responses that match the things

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that go about for accident time.

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Myocarditis has a...

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It's a whole lot like this in terms of dose response.

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And maybe I'll show that later.

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So, yeah.

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They're going to be people like the real truth

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or who just, you know,

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believed this magical morphing demon called COVID

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just suddenly got worse.

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But just for the younger people,

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well, which younger people?

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Because it actually got worse more for, like,

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like the middle-aged people in the military.

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It got worse faster.

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So, you know, there's this,

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there's this, like, catch-all explanation.

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Oh, it was the variant of the day that caused this,

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not the vaccines, right?

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Well, you know what we're going to do?

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We're going to...

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Well, you know, let's keep going on here.

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So, you know, I wanted to be debating a little bit

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with a guy named James.

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Let me see if I can find him.

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Here he is.

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James, Sir Wieke.

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And he is an author who has written about the wisdom

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of the crowd.

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So, he's the best-selling author.

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And he's done this before.

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He's jumped in and said, you know,

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hospitalization for COVID rose when a more virulent,

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virulent variant hit the younger people emerge.

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What a shock.

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And he said, you know, look, it rose like things were worse

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in the area in the states where you had lower-vax rates.

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And I'm going to mention this 15 months ago.

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I remember specifically because it was the same month

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that I went to the CHD, so it was October of 2022.

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He and I were having this debate on Twitter.

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And I invited him into a conversation.

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I said, look, you know, I can show you that you're not

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correcting the data.

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And I can show you what this actually looks like.

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I'll come back to that because we're going to take a look

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at the actual data.

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But I showed him, look, you know, the international

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correlations between vaccine uptake and COVID deaths.

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Those were both positive, right?

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These are international correlations among all nations

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in our world and database, 219 nations.

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I skipped nothing.

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And this is what the correlations were always positive.

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And for mortality, they were positive and increasingly

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positive throughout 2021.

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I haven't rerun that data since I did the military health

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database work, which is funny because he comes back

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and he says the idea you can lump together data is silly,

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but that's exactly what he was doing on the state level, right?

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Like immediately you see the mental gymnastics come out.

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And I invited both him and Alex to discuss the actual data.

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Neither of them, once I made that invitation, neither of them

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responded, but, you know, I just want to point out that I

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showed James numerous graphs that cut against his argument

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and he just danced around it all, right?

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And I even told him, look, no, you know, I back tested.

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This is a predictive model and this is about healthy user bias

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and I'll come back and talk about what that is later on, right?

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But I just wanted to, we're just setting the table here.

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Here are these two guys who are avoiding the conversation.

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They don't want to have the conversation.

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So, you know, I invited them, I invited them both to the table.

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I was polite.

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I even showed them like a video of me having a polite conversation

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with someone who disagreed with me to show them.

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This is not meant to be a gotcha.

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Like, if you make false claims, I may debunk the funky or, you know,

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the way that I dunked on debunk the funk and he, you know,

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walked into the trap of showing that he didn't even know the

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definitions of the data that he was talking about, right?

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Now, if you try to, you try to BS me, you know, it will become

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your own trap.

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But look, this is Alex Behrenson.

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He comes back versus he grooves me with Steve Kirsch and Brent

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Weinstein, even though these guys don't even promote the data

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that I come up with, which is very weird because I've had, you

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know, some of the best data.

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I've unwound a lot of the data illusions that are out there.

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And I have the data on county, state and international level,

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right?

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But he says a top line first, right?

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But let's go down a little bit, right?

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This is an article that I wrote.

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See, when was this two years ago?

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Give or take, where I showed that he was using top line data to

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say that deaths were higher for people after the second dose.

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And I pointed out that this is a Simpsons paradox, that if you

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actually, you know, if you actually separate the demographics

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that they look almost exactly the same in terms of mortality,

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and I do that, and I show down here that the cumulative mortality

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pretty much mirrors each other in the groups after you separate

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out the demographics.

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It's just that one of those demographics is heavier in the

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vaccinated group and the other one's heavier in the unvaccinated

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group, which makes the appearance of a separation of the two.

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So he's absolutely wrong about the notion that you should be using

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top line data, right?

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Using aggregate data is where you run into something called the

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ecological fallacy or otherwise known as a Simpsons paradox.

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And I've even emailed with him about correcting his data

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before, you know, for that story in particular, but he doesn't

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want to have a conversation about it, right?

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He is invited here and it becomes, at any point in time, if he reaches

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out to me, I'm going to give him the link to the studio.

16:21.520 --> 16:23.520
He can come in and talk with me, same with James.

16:23.520 --> 16:24.520
They're both invited.

16:24.520 --> 16:28.520
But I think what's going on here is they literally do not want to

16:28.520 --> 16:32.520
have the conversation about what the actual data looks like.

16:32.520 --> 16:36.520
I've grown more and more suspicious that the entire, that

16:36.520 --> 16:40.520
we're just living in one giant clown show where nobody actually wants

16:40.520 --> 16:42.520
to get to the bottom of the data.

16:42.520 --> 16:45.520
Maybe people on all sides of the argument just want to keep the

16:45.520 --> 16:46.520
clown show going.

16:46.520 --> 16:47.520
That's what it feels like, at least.

16:47.520 --> 16:50.520
It's very hard not to feel that way at this point, but let's go

16:50.520 --> 16:52.520
through some of the data.

16:52.520 --> 16:56.520
And, you know, this is from an article that I wrote, you know,

16:56.520 --> 16:59.520
promoted by almost nobody in the medical freedom movement.

16:59.520 --> 17:01.520
But this is all the way back May 2022.

17:01.520 --> 17:04.520
I took a break from working on the military health database.

17:04.520 --> 17:08.520
Parsley, I think I saw a clear.

17:08.520 --> 17:12.520
What's her last name works with the long infant circle.

17:12.520 --> 17:18.520
She's good with her analysis, but pardon me, not knowing her name

17:18.520 --> 17:21.520
for a moment, but you guys probably knew what I'm talking about.

17:21.520 --> 17:25.520
Anyhow, I saw a post for her, which made me think about healthy

17:25.520 --> 17:27.520
user bias, right?

17:27.520 --> 17:32.520
And so I, you know, started to collect data, right?

17:32.520 --> 17:35.520
My wife and I between us, we found different databases.

17:35.520 --> 17:40.520
We got data on vaccination by county from the CDC and New York

17:40.520 --> 17:45.520
Times data, but we also went into census and we got census data.

17:45.520 --> 17:49.520
And I think I even have the notes file with data definitions that

17:49.520 --> 17:50.520
we used, right?

17:50.520 --> 17:53.520
Anybody can take a screenshot of that and go.

17:53.520 --> 17:56.520
If anybody wants to reproduce this work, we've got it, you know,

17:56.520 --> 17:58.520
on state and county data.

17:58.520 --> 18:04.520
But what we did was we looked at daily COVID deaths.

18:04.520 --> 18:10.520
Her, you know, vaccine percentage complete.

18:10.520 --> 18:13.520
Orlations and we did this.

18:13.520 --> 18:18.520
By all these different definitions of vaccine, like whether it was

18:18.520 --> 18:22.520
vaccine complete, dose one booster, vaccine complete, and over the

18:22.520 --> 18:23.520
age of 65.

18:23.520 --> 18:29.520
But we also did these COVID deaths per high school diploma or

18:29.520 --> 18:32.520
bachelor's degree educational status, right?

18:32.520 --> 18:39.520
And what you can see is all of these correlations of, you know,

18:39.520 --> 18:42.520
slightly negative correlations for counties that have been

18:42.520 --> 18:46.520
vaccinated more, slightly negative, but they perfectly track

18:46.520 --> 18:47.520
education status.

18:47.520 --> 18:50.520
In other words, you know, what happened in the county in terms of

18:50.520 --> 18:54.520
COVID deaths looks to be driven by education status.

18:54.520 --> 18:57.520
And this is really probably, there are these three variables that

18:57.520 --> 18:59.520
probably all represent the same thing.

18:59.520 --> 19:02.520
This is called healthy user bias, right?

19:02.520 --> 19:06.520
Is that people who are healthier tend to dot their eyes and cross

19:06.520 --> 19:08.520
their T's, they're more likely to go get a vaccine.

19:08.520 --> 19:10.520
They're more likely to have a ride.

19:10.520 --> 19:14.520
They're more likely to have access to a doctor, first of all, right?

19:14.520 --> 19:18.520
So they're more likely to go get vaccinated.

19:18.520 --> 19:21.520
And we can see in the data, we're going to see back testing too,

19:21.520 --> 19:22.520
right?

19:22.520 --> 19:25.520
Because, you know, I can make the claim and people might go,

19:25.520 --> 19:27.520
was that always true, right?

19:27.520 --> 19:30.520
Is the tracking the same as it would have been in your classroom?

19:30.520 --> 19:31.520
We're going to get into that.

19:31.520 --> 19:33.520
And the answer is yes.

19:33.520 --> 19:37.520
But all this is the healthy user bias in healthier counties where

19:37.520 --> 19:40.520
there is a lower mortality rate.

19:40.520 --> 19:44.520
You wind up with higher vaccination rates.

19:44.520 --> 19:46.520
And it's perfectly correlated.

19:46.520 --> 19:52.520
And that healthy user, that health status, it's the same as wealth

19:52.520 --> 19:55.520
status, and it's the same as education status.

19:55.520 --> 19:58.520
These three things, they track almost perfectly.

19:58.520 --> 19:59.520
And we'll see that.

19:59.520 --> 20:04.520
So this was, this was me beginning to recognize in the data that

20:04.520 --> 20:08.520
all of this vaccine data was basically just healthy user bias.

20:08.520 --> 20:10.520
Let's take a look at another chart.

20:10.520 --> 20:15.520
National correlates daily COVID deaths for 100,000.

20:15.520 --> 20:19.520
And we can see that when we compare this to what happened in

20:19.520 --> 20:22.520
2019, before there was now break, you know what?

20:22.520 --> 20:26.520
That there's pretty similar tracking.

20:26.520 --> 20:29.520
There's a little bit of separation a couple of times.

20:29.520 --> 20:32.520
But it's, it's, it's very highly similar.

20:32.520 --> 20:35.520
Let's take a look at one more.

20:35.520 --> 20:40.520
Daily COVID deaths for 100,000 versus county racial demographics.

20:40.520 --> 20:45.520
And you know what, because of the fact of segregation, this is

20:45.520 --> 20:49.520
really just another version of the healthy user bias to some degree

20:49.520 --> 20:53.520
because counties that have more whites and Asians on average

20:53.520 --> 20:55.520
are wealthier counties, right?

20:55.520 --> 20:58.520
And we can see the mirroring, right?

20:58.520 --> 21:04.520
When you have more vaccine completion, you have, sorry, less

21:04.520 --> 21:05.520
vaccine completion.

21:05.520 --> 21:10.520
You have more minority population, black population

21:10.520 --> 21:13.520
and Hispanic population.

21:13.520 --> 21:18.520
So, and let's take a look at one more because here we get, you know,

21:18.520 --> 21:21.520
in the very middle of this chart or little to the right of the

21:21.520 --> 21:24.520
middle, we have that yellow line that drops in where,

21:24.520 --> 21:26.520
where suddenly people start getting boosters.

21:26.520 --> 21:29.520
And very quickly, very quickly, that correlation winds up

21:29.520 --> 21:33.520
tracking with all of the other correlations, right?

21:33.520 --> 21:39.520
We have David, daily COVID deaths for 100,000 versus daily percentage

21:39.520 --> 21:43.520
of vaccine completion in that county at that point in time.

21:43.520 --> 21:46.520
And, you know, you do have a little bit of a negative correlation

21:46.520 --> 21:49.520
which would kind of look like vaccine effectiveness,

21:49.520 --> 21:51.520
except that it all still tracks here.

21:51.520 --> 21:53.520
We have education status.

21:53.520 --> 21:55.520
High school diplomas and bachelor's degrees.

21:55.520 --> 21:57.520
That's what's predictive.

21:57.520 --> 22:02.520
And again, I back tested all this and we're going to see some of that back testing.

22:03.520 --> 22:06.520
So, now let's talk about the actuarial analysis because this is

22:06.520 --> 22:07.520
one of the big stories.

22:07.520 --> 22:11.520
Ed Dowd took this story over, but he did so very incompletely.

22:11.520 --> 22:13.520
And I'm going to try to explain that.

22:13.520 --> 22:16.520
What Ed Dowd did is he looked at this graph and said,

22:16.520 --> 22:19.520
oh, you know, this 35 to 44 year old group.

22:19.520 --> 22:23.520
And again, why would, why would a virus, why would a viral illness

22:23.520 --> 22:27.520
affect people in this one age ban, 35 to 44, you know,

22:27.520 --> 22:29.520
much more than all of the others, right?

22:29.520 --> 22:31.520
You would think that if it affected younger people more,

22:31.520 --> 22:35.520
it would actually, you know, you need less people affected

22:35.520 --> 22:39.520
to affect a higher percentage of the really young people.

22:39.520 --> 22:44.520
So you would think that the youngest would have the highest percentage increases

22:44.520 --> 22:48.520
if there were such things as a virus that specifically just started

22:48.520 --> 22:51.520
to all of a sudden harm young people more.

22:51.520 --> 22:55.520
But when you look at this, there are these two columns near the right-hand side

22:55.520 --> 22:58.520
that say percentage code and percentage non-COVID.

22:58.520 --> 23:02.520
And what those are, you know, if you look at that column that says

23:02.520 --> 23:06.520
that combines the different quarters, that's a little bit to the right-of-center,

23:06.520 --> 23:08.520
you see like 118 percent.

23:08.520 --> 23:12.520
You take that 18 percent and you break it down into 2.7 percent and 15.2.

23:12.520 --> 23:15.520
And what you can see is for these younger populations,

23:15.520 --> 23:19.520
it's actually more non-COVID than COVID.

23:19.520 --> 23:22.520
The excess death is more non-COVID than COVID.

23:22.520 --> 23:25.520
And you know what part of this might be?

23:25.520 --> 23:27.520
Part of this might be the opioid epidemic.

23:27.520 --> 23:30.520
You go back to 2019, you have 80,000 deaths per year.

23:30.520 --> 23:35.520
You know, go forward a year or two and you have like 20,000 more deaths per year

23:35.520 --> 23:37.520
or a little bit more than that, I think.

23:37.520 --> 23:43.520
You know, nobody's doing the job of dividing out what those opioid deaths were.

23:43.520 --> 23:45.520
But I think you can see it.

23:45.520 --> 23:46.520
I think you see it right here.

23:46.520 --> 23:51.520
I think you see more non-COVID deaths among those younger cohorts than COVID deaths.

23:51.520 --> 23:56.520
So, you know, without better specific opioid data, I think that's the best that we can do.

23:57.520 --> 24:02.520
Going down, you know, I explained since paradox right here, this is a quick chart

24:02.520 --> 24:05.520
where you have somebody drink and then take an IQ test.

24:05.520 --> 24:12.520
And if you do that per individual, you know, with four people with separated out IQs,

24:12.520 --> 24:19.520
it looks like the more alcohol you take, the higher your IQ is, right?

24:19.520 --> 24:21.520
That's the overall trend line that goes up here.

24:21.520 --> 24:24.520
The more you drink, the higher your IQ is.

24:24.520 --> 24:30.520
But when you look at each subgroup, which is each individual, you see a clearly downward trend.

24:30.520 --> 24:32.520
But the overall trend is upward.

24:32.520 --> 24:35.520
So you have to go to subgroups.

24:35.520 --> 24:37.520
You have to in order to work this out.

24:37.520 --> 24:43.520
So, you know, this graph was published by the actuarial society itself.

24:43.520 --> 24:46.520
And they even made a video and they came out in this video and said,

24:46.520 --> 24:50.520
look, it looks like the vaccines are effective because, overall, nationally,

24:50.520 --> 24:52.520
we see a downward trend.

24:52.520 --> 24:58.520
In other words, when we graph the 50 states, the higher the vaccination rate,

24:58.520 --> 25:02.520
the lower the excess mortality.

25:02.520 --> 25:03.520
Okay.

25:03.520 --> 25:04.520
But you know what?

25:04.520 --> 25:06.520
They actually did the work for me.

25:06.520 --> 25:08.520
The moment I looked at this graph, I could see it.

25:08.520 --> 25:10.520
Look at each individual dot.

25:10.520 --> 25:11.520
Look at just the green dots.

25:11.520 --> 25:15.520
If you trend the green dots, is it going to look like such a downward slope?

25:15.520 --> 25:16.520
The answer is no.

25:16.520 --> 25:17.520
I did the work.

25:17.520 --> 25:21.520
I took the data myself and, um, and regrafted.

25:21.520 --> 25:26.520
And you can see, you know, right here, the four colored trend lines, you know,

25:26.520 --> 25:29.520
there's very little bit of slowing down.

25:29.520 --> 25:33.520
Well, maybe that's the vaccine effectiveness, but I think it can be explained way by healthy

25:33.520 --> 25:36.520
user bias, but even more than healthy user bias.

25:36.520 --> 25:42.520
It can actually be explained way by healthy user bias, but even more than healthy user bias.

25:43.520 --> 25:49.520
It can actually be explained way by the fact that in the places that had higher vaccine uptake,

25:49.520 --> 25:56.520
what you had was like during the breakout order, this is going to show it right here.

25:56.520 --> 26:02.520
During the breakout order, second quarter of 2020, you know, what you have is.

26:02.520 --> 26:04.520
Oh, this is it right here.

26:04.520 --> 26:07.520
The one on the left, what you have is an upward trend.

26:07.520 --> 26:12.520
In other words, those places that got more vaccinated add their weakest people.

26:12.520 --> 26:15.520
There are people, uh, people with like three and four comorbidities.

26:15.520 --> 26:17.520
Those people died more.

26:17.520 --> 26:18.520
They died more in New York.

26:18.520 --> 26:19.520
They died more in New Jersey.

26:19.520 --> 26:22.520
They died more in the States that wound up getting higher vaccination.

26:22.520 --> 26:26.520
And that's going to equal out at some point, right?

26:26.520 --> 26:33.520
You burn the, the dry tender off and then you should see a reversion in that.

26:33.520 --> 26:44.520
And so right now, you know, it looks at, you know, everything looks just to say, you know, quarter three of 2021 was sort of an equalizing quarter.

26:44.520 --> 26:47.520
Relative to quarter two of 2020.

26:47.520 --> 26:54.520
But if you, if you leave that fact out, if you leave out the fact that more of your week elderly people died in quarter to 2020.

26:54.520 --> 26:59.520
But in those Northeast States in particular, then you're going to get a false threat, right?

26:59.520 --> 27:00.520
You have to correct for that.

27:00.520 --> 27:05.520
How many people are within, you know, statistically the last year or two of life.

27:05.520 --> 27:09.520
So, um, moving forward.

27:09.520 --> 27:12.520
Let's talk a little bit more about healthy user bias.

27:12.520 --> 27:13.520
Right.

27:13.520 --> 27:15.520
Here's another one of those charts.

27:15.520 --> 27:19.520
This time I've got medium household income versus daily COVID deaths.

27:19.520 --> 27:23.520
And look, medium household income is just as predictive.

27:23.520 --> 27:29.520
It's, it's, it's absolutely predictive of where the vaccine correlation is going.

27:29.520 --> 27:31.520
Right.

27:31.520 --> 27:44.520
There is a slightly negative correlation that shows some vaccine effectiveness between getting more vaccines and people dying less, but you would already get that for median household income.

27:44.520 --> 27:48.520
And again, we'll see the back testing of that model later on.

27:48.520 --> 27:57.520
Then here, you know, this one is a really, really special moment in all of the data.

27:57.520 --> 27:59.520
This, then this should be talked about more.

27:59.520 --> 28:00.520
Right.

28:00.520 --> 28:12.520
This, this is one of the best moments we had in getting real actual data because this is one of the world's biggest health, sorry, defense contractors.

28:12.520 --> 28:16.520
It's called SAIC, one of the world's largest defense contractors.

28:16.520 --> 28:26.520
They were charting their own employees, particularly those, you know, at least 65 years of age, they were charting vaccine uptake.

28:26.520 --> 28:29.520
And they put this chart up online.

28:29.520 --> 28:38.520
And there were people like Meryl Dasky noticed and said, this doesn't look like, like there's, you know, this very high level of vaccine effectiveness.

28:38.520 --> 28:40.520
So what I did is I ran the numbers myself.

28:40.520 --> 28:42.520
I took the numbers and put them in a spreadsheet.

28:42.520 --> 28:47.520
And I've got that spreadsheet linked to this article, by the way, so everybody can look at the spreadsheet.

28:47.520 --> 28:49.520
But I put it in the spreadsheet.

28:49.520 --> 28:56.520
And what I get is after, first of all, in the first few weeks, you have negative effectiveness.

28:56.520 --> 29:00.520
But then it goes right towards zero, except this little bump right here.

29:00.520 --> 29:03.520
And I suspect that, and I'll explain that little bump.

29:03.520 --> 29:10.520
That little bump is because there are actually so few people, the numbers get so small.

29:10.520 --> 29:14.520
And I was actually like literally measuring the chart to estimate.

29:14.520 --> 29:25.520
And I was rounding to like 500 people at a time that I think that that's actually just a matter of the rounding of the numbers becoming a little bit too discreet.

29:26.520 --> 29:31.520
So I think that ultimately what you have is, you know, you can see it's kind of like made up here thereafter.

29:31.520 --> 29:38.520
I think what you have is basically zero effectiveness after the first couple of weeks.

29:38.520 --> 29:45.520
And I think that that's people literally suffering, you know, advertisements after taking the vaccine.

29:45.520 --> 29:46.520
That's the way that looks.

29:46.520 --> 29:49.520
It definitely, it definitely tells the story that I'm telling.

29:49.520 --> 29:52.520
It doesn't tell the story that the vaccines are effective, right?

29:52.520 --> 29:59.520
And it's very noteworthy that SAIC immediately took this data down, right?

29:59.520 --> 30:01.520
Why remove data?

30:01.520 --> 30:02.520
That's very suspicious.

30:02.520 --> 30:07.520
You should always want all data, all data is valuable.

30:07.520 --> 30:10.520
So, well, that's the chart that we've already seen.

30:10.520 --> 30:14.520
So anyway, the SAIC data, that's very important.

30:14.520 --> 30:17.520
And then we get to Norman Fittons.

30:17.520 --> 30:19.520
That's who I give first credit for.

30:19.520 --> 30:21.520
I don't know if I'm giving credit to the right person first.

30:21.520 --> 30:24.520
Maybe it was somebody in his circle, maybe came up.

30:24.520 --> 30:29.520
I think this was his observation first, though, which is that there was

30:29.520 --> 30:35.520
miss categorization that was causing the appearance of vaccine effectiveness.

30:35.520 --> 30:39.520
And I went through his argument and built my own spreadsheet.

30:39.520 --> 30:42.520
And I think I haven't linked in here.

30:42.520 --> 30:44.520
I've done a video on it anyhow.

30:44.520 --> 30:48.520
You know, when you look at his argument, though, he did a video.

30:48.520 --> 30:51.520
There are 19 columns in his spreadsheet.

30:51.520 --> 30:55.520
And I think that that makes his video hard for a lot of people to follow.

30:55.520 --> 30:58.520
In fact, just watching it myself as a data mind.

30:58.520 --> 31:02.520
You know, I was looking, trying to get ahold of what all these columns were

31:02.520 --> 31:03.520
while he was speaking.

31:03.520 --> 31:07.520
And it's very difficult to do both, right, to look at that much data

31:07.520 --> 31:10.520
and to follow someone's conceptualization as it was going.

31:10.520 --> 31:13.520
So I knew that there was a need to do something a little bit different.

31:13.520 --> 31:16.520
So I created my own spreadsheet with fewer columns.

31:16.520 --> 31:18.520
And broke it.

31:18.520 --> 31:22.520
I started with just the fewest number of columns possible and gave everybody

31:22.520 --> 31:27.520
an infection rate of 2%, everybody vaccinated or not.

31:27.520 --> 31:31.520
Just to see how the model would work.

31:31.520 --> 31:40.520
And if what you do is you don't have people get called vaccinated for two weeks.

31:41.520 --> 31:50.520
Then, yes, you wind up with this illusion where ultimately you have a splitting of the

31:50.520 --> 31:53.520
numerator versus the denominator, right?

31:53.520 --> 31:58.520
You should be dividing the same numerator in each category with the same denominator

31:58.520 --> 32:01.520
that represents that category, but you don't.

32:01.520 --> 32:07.520
You have, you know, one category in the numerator divided by two and two categories

32:07.520 --> 32:09.520
in the numerator divided by one.

32:09.520 --> 32:13.520
And I color coded the columns to show that happening, right?

32:13.520 --> 32:16.520
If you don't call these people vaccinated for two weeks,

32:16.520 --> 32:22.520
then you wind up with exactly the data illusion that he is talking about.

32:22.520 --> 32:29.520
But this is predicated on both groups having exactly 2% infection rate each week, right?

32:29.520 --> 32:35.520
So, you know, it's very, it's, and, you know, the curves, the curves look exactly

32:35.520 --> 32:39.520
like what we've seen, which is they go, oh, well, there was high effectiveness.

32:39.520 --> 32:45.520
There was high vaccine effectiveness, but then it waned, waning vaccine effectiveness.

32:45.520 --> 32:47.520
And these are the only vaccines that have been like this.

32:47.520 --> 32:52.520
And they talk about antibody titers and, you know, they make this sort of mystical assumption

32:52.520 --> 32:55.520
that these antibody titers are what is causing the immunity.

32:55.520 --> 33:00.520
Even though, even though antibodies are only, you know, just one portion of our vast immune system, right?

33:00.520 --> 33:07.520
But they talk about antibody titers, but they can explain away waning efficacy this way.

33:07.520 --> 33:10.520
But it's complete nonsense.

33:10.520 --> 33:15.520
You know, the waning efficacy is a waning illusion only, right?

33:15.520 --> 33:20.520
Otherwise, otherwise we wouldn't have seen what we saw in the SAIC data, right?

33:20.520 --> 33:26.520
The SAIC data totally flat-lined, totally flat-lined weeks into the vaccination program.

33:26.520 --> 33:32.520
You don't have this, this changing curve of vaccine effectiveness.

33:32.520 --> 33:39.520
So, you know, it certainly, certainly looks like Norman Fitt was right.

33:39.520 --> 33:43.520
Now, I have, like I said, I've got hundreds, I've got thousands, literally thousands of charts.

33:43.520 --> 33:45.520
Many of them I've never written up.

33:45.520 --> 33:47.520
I've never talked with anybody about them.

33:47.520 --> 33:52.520
Some of them were just me coming to grips with what, with, with the data, right?

33:52.520 --> 33:55.520
I'm going to be coming up with my, with my own analyses so that I can talk about it.

33:55.520 --> 33:57.520
But I'm going to show some of my spreadsheets.

33:57.520 --> 34:00.520
I'm going to show everybody how much work went into this.

34:00.520 --> 34:05.520
This one doesn't have like a lot of the initial data that we pulled, but we pull certain columns of data.

34:05.520 --> 34:09.520
When I say we, my wife and I went through a lot of this together.

34:09.520 --> 34:15.520
But, you know, one of the things that I wanted to see was the healthy user bias back tested.

34:15.520 --> 34:21.520
So, we take a look back in 2018 and, and what are the columns that are being compared here.

34:21.520 --> 34:25.520
This is median household income versus mortality, right?

34:25.520 --> 34:34.520
So, in 2018, we can see that if we put every, all the median household income on like a, a, a 0 to 1 continuum.

34:34.520 --> 34:36.520
In other words, percentile, right?

34:36.520 --> 34:39.520
This is all 3100 something counties in the United States.

34:39.520 --> 34:47.520
And then we, we, we, we are graphed at percentile versus the mortality in that, in that county.

34:47.520 --> 34:54.520
And we can see that in 2018, we have the slope, which is negative 575.

34:54.520 --> 35:03.520
That means you have 575 more deaths per million in the poorest counties that you do in the wealthiest counties.

35:03.520 --> 35:05.520
Give or take, right?

35:05.520 --> 35:07.520
It's a pretty smooth progression, right?

35:07.520 --> 35:10.520
Which, which tells you that you're looking at the right variable, I think.

35:10.520 --> 35:12.520
Do we see the same thing in 2019?

35:12.520 --> 35:13.520
Yeah.

35:13.520 --> 35:15.520
The slope is almost exactly the same.

35:15.520 --> 35:16.520
Negative 554.

35:16.520 --> 35:19.520
So, we have about an average slope of negative 565.

35:19.520 --> 35:22.520
Let's see what happens over the next few years during the pandemic.

35:22.520 --> 35:25.520
2020, suddenly the slope is more severe.

35:25.520 --> 35:30.520
Things were worse in poorer counties, right?

35:30.520 --> 35:33.520
Now, on the state level, they weren't, right?

35:33.520 --> 35:35.520
This should make you scratch your head.

35:35.520 --> 35:38.520
Like, suddenly we have these death spikes in New York and New Jersey.

35:38.520 --> 35:41.520
Oh, but it's just those poor people.

35:41.520 --> 35:44.520
Did somebody push those people off a cliff?

35:44.520 --> 35:45.520
Right?

35:45.520 --> 35:52.520
You've got to wonder, is this hospital protocols, you know, made to create this or, or if there's any illusion?

35:52.520 --> 35:57.520
I know that there's someone named Jessica Hockett who believes that New York may have futs with the death records.

35:57.520 --> 36:04.520
And I haven't evaluated that argument at all, but if you did that and you did it in counties that were poor,

36:04.520 --> 36:07.520
there would be fewer people looking to see, right?

36:07.520 --> 36:13.520
So, you know, and then in 2021, vaccines roll out.

36:13.520 --> 36:17.520
Things get a little bit, just a little bit worse, but pretty much the same as 2020, right?

36:17.520 --> 36:20.520
It's not, it's much closer to 2020 than it was in prior years.

36:20.520 --> 36:22.520
But what happens in 2022?

36:22.520 --> 36:26.520
Suddenly you have a much shallower slope.

36:26.520 --> 36:28.520
In other words, you have equalization.

36:28.520 --> 36:36.520
If you average 2021 and 2022, you get, see if I can do this in my head.

36:44.520 --> 36:50.520
594.

36:50.520 --> 36:55.520
And when I say it was up here, negative 565, or negative 594.

36:55.520 --> 37:02.520
In other words, overall, during the pandemic, you had the exact same slope.

37:02.520 --> 37:11.520
It's just that early on during the pandemic, you had more people dying in poor counties overall.

37:12.520 --> 37:16.520
People in wealthier counties were a little bit more protected protected.

37:16.520 --> 37:19.520
And you know what? I'm going to throw this out there. We all know that it's true.

37:19.520 --> 37:26.520
We all know that there were wealthy people out there who were getting ahold of fake vaccine cards.

37:26.520 --> 37:32.520
We all know that that's true. We also all know that that in the halls of someplace like Congress,

37:32.520 --> 37:39.520
those people in the CDC, those people that have to take the vaccine, they didn't have mandates, right?

37:39.520 --> 37:44.520
You have mandates for wealthy corporate workers.

37:44.520 --> 37:50.520
Those people are a little bit more likely to slide a few hundred dollars to a doctor who's going to write on a card got vaccinated.

37:50.520 --> 37:55.520
I've had numerous people tell me they had fake vaccine cards. We all know that it's true, right?

37:55.520 --> 38:00.520
And it's going to happen amongst your wealthier, more educated groups.

38:00.520 --> 38:05.520
So this is one of the spreadsheets I wanted to talk about. I've got so many more, right?

38:05.520 --> 38:12.520
And like I said, a lot of this stuff is stuff that hasn't been published, but it just informed me of what was going on.

38:12.520 --> 38:17.520
At some point, I may have written about it, but you know, 2022, I got buried on the health database.

38:17.520 --> 38:24.520
I got buried almost just a giant scyop. I think.

38:24.520 --> 38:25.520
Let's see.

38:25.520 --> 38:28.520
Where do I have?

38:28.520 --> 38:32.520
Maybe move some things around. Where's 2020 quarter three?

38:32.520 --> 38:37.520
Uh oh.

38:37.520 --> 38:40.520
Ah, there's 20, 20, quarter three.

38:40.520 --> 38:41.520
Right.

38:41.520 --> 38:47.520
Oh, yeah, this is okay. So let's think about what data this is. This is state level data.

38:47.520 --> 38:52.520
And what are we charting on the spreadsheet? We are charting.

38:53.520 --> 38:56.520
Media and household income.

38:56.520 --> 39:02.520
Versus all cause mortality, median household income versus all cause mortality.

39:02.520 --> 39:09.520
And you can see, you know, you can see the negative downward slope on a state level and then in quarter to it inverts.

39:09.520 --> 39:15.520
It inverse in quarter to, but then it goes back. It goes back to being a negative downward sloping trend.

39:15.520 --> 39:21.520
But I separated these out. For what reason did I separate these out? Let's see.

39:21.520 --> 39:28.520
Because it, you know, from 2020 quarter three to 2021 quarter three, you have really close to the same curve.

39:28.520 --> 39:33.520
It's just a little bit more severely down, but that's just make up for the fact that.

39:33.520 --> 39:41.520
Um, 2022 quarter two was, was upward just during that five weeks span of the big spike.

39:41.520 --> 39:46.520
The big, the big scare spike in, in COVID in April.

39:47.520 --> 39:48.520
2020.

39:48.520 --> 39:52.520
Um, but I think there was another tab here that I wanted to talk about.

39:52.520 --> 39:54.520
That's why I brought the spreadsheet up.

39:54.520 --> 39:59.520
What do we have here? We have median household income.

39:59.520 --> 40:02.520
Versus.

40:02.520 --> 40:09.520
Uh, percent excess mortality. So it's just, it's just another way of looking at the same thing, but we can see.

40:09.520 --> 40:13.520
Um, on a state level, we can see the general downward sloping trend.

40:13.520 --> 40:18.520
And we see it invert quarter to, and then it kind of makes up somewhat in quarter three.

40:18.520 --> 40:24.520
Of 2020, but, but then quarter three and 2021, right? That's like the two of those together just make up for this.

40:24.520 --> 40:29.520
And we've seen ultimately, ultimately these lines average, these trend lines.

40:29.520 --> 40:37.520
They're, they're usually negative and they average out to the regular negative story. It's just that people in the Northeast died earlier on during the pandemic.

40:37.520 --> 40:39.520
And that's it.

40:39.520 --> 40:42.520
Um, let's see.

40:42.520 --> 40:50.520
Uh, that's something else that somebody else's graph.

40:50.520 --> 40:54.520
Uh, this is an interesting one.

40:54.520 --> 40:57.520
Okay.

40:57.520 --> 41:01.520
What are we looking at here? We're looking at.

41:02.520 --> 41:10.520
Baseline. We've, we've turned baseline into 2018 and this is by.

41:10.520 --> 41:15.520
Uh, county again, you can see we've got all.

41:15.520 --> 41:20.520
3100 counties represented in this data.

41:20.520 --> 41:24.520
Every single county and we're using the CDC's.

41:24.520 --> 41:27.520
Numbers, right? So what do we see?

41:27.520 --> 41:33.520
When we compare. So what's going on in this graph is we are comparing.

41:33.520 --> 41:38.520
Um, baseline mortality.

41:38.520 --> 41:42.520
Um, we're, we're, we're, we're correlating is what we're doing.

41:42.520 --> 41:46.520
Right. We're taking, um, like week one.

41:46.520 --> 41:51.520
Twenty nineteen, uh, versus week one, 2018.

41:51.520 --> 41:58.520
By U S county and then correlating all 3100 calories counties and you get a very,

41:58.520 --> 42:06.520
very high correlation, which is what you would expect counties that have more deaths one year are going to have more deaths the next year.

42:06.520 --> 42:07.520
Right.

42:07.520 --> 42:14.520
And, and you can see this correlation is just a little bit under point seven on average and then you have that five week dip.

42:14.520 --> 42:18.520
But then things pretty much return back to normal.

42:18.520 --> 42:22.520
Right. The counties, you know, by late 2022.

42:22.520 --> 42:27.520
Um, there's really no change in this comparison by counties with vaccination rate.

42:27.520 --> 42:31.520
But even if you want to judge this by vaccine uptake.

42:31.520 --> 42:32.520
Right.

42:32.520 --> 42:36.520
All you get is that the slightly negative correlation.

42:36.520 --> 42:42.520
For who got vaccinated, it is exactly the filler.

42:42.520 --> 42:47.520
For that upward correlation that would have happened.

42:47.520 --> 42:53.520
Like I said, the counties that had more deaths are ones that had more vaccine uptake later on.

42:53.520 --> 43:01.520
And so all that's happening is this, this near zero correlation is getting moved.

43:01.520 --> 43:06.520
Shifted to the right. That is all we see on average. It's still just grand sum zero.

43:06.520 --> 43:08.520
And that's it.

43:08.520 --> 43:13.520
And that just it screams vaccines didn't matter.

43:13.520 --> 43:15.520
They did not matter.

43:15.520 --> 43:18.520
Um, let's add adverse events.

43:18.520 --> 43:19.520
Then they mattered.

43:19.520 --> 43:21.520
If you had myocarditis, that's what it mattered.

43:25.520 --> 43:27.520
Um, let's see.

43:27.520 --> 43:29.520
I think I have one or two more of these spreadsheets.

43:29.520 --> 43:32.520
And you know, I've got dozens of these spreadsheets.

43:32.520 --> 43:33.520
Uh, is this one?

43:33.520 --> 43:35.520
This is one that already went through.

43:35.520 --> 43:40.520
Uh, I've got one where I did all the states and, you know, maybe I'm going to go find this right now.

43:40.520 --> 43:42.520
Um, yeah.

43:42.520 --> 43:44.520
Uh, hang with me for just a moment.

43:44.520 --> 43:49.520
I'm going to go into, you know, I did so many spreadsheets.

43:49.520 --> 43:50.520
I had no idea.

43:50.520 --> 43:52.520
We would be talking about this for years.

43:52.520 --> 43:54.520
To me, it was just so simple.

43:54.520 --> 43:59.520
I had no idea that, um, okay, here's some international data.

43:59.520 --> 44:00.520
Right.

44:01.520 --> 44:06.520
Here's international data where, um, you had more.

44:06.520 --> 44:08.520
COVID deaths.

44:08.520 --> 44:10.520
Or you had more objections.

44:10.520 --> 44:12.520
That's the international data.

44:12.520 --> 44:14.520
Yes, there are outliers.

44:14.520 --> 44:16.520
But the trend is pretty clear.

44:16.520 --> 44:18.520
You know, maybe I should bring that up for a second.

44:18.520 --> 44:21.520
What's interesting is that the slopes that you see.

44:21.520 --> 44:28.520
On these things actually do kind of match the, uh, the access mortality estimates that I'd.

44:29.520 --> 44:33.520
Um, that I computed in all the way back in August of 2021.

44:33.520 --> 44:40.520
I was the first person to, uh, to say, look, the data says we have excess deaths, but, um.

44:40.520 --> 44:41.520
Apologies.

44:41.520 --> 44:43.520
I don't know where all my spreadsheets are.

44:43.520 --> 44:44.520
Right.

44:44.520 --> 44:47.520
I did know that I would be looking some of these up.

44:47.520 --> 44:49.520
You know, so much later.

44:49.520 --> 44:52.520
So it may take me a moment to find some of these.

44:52.520 --> 44:57.520
I'm going to pull my files over to another screen while we're doing this.

44:57.520 --> 45:01.520
But in the meantime, let me leave up one of these.

45:01.520 --> 45:07.520
That just so clearly shows the healthy user bias so that people can soak this up.

45:07.520 --> 45:09.520
This is due to the vaccine.

45:09.520 --> 45:10.520
Take a look.

45:10.520 --> 45:15.520
Take a look at the purple line, which is percent bachelor's degree and the gray line, which is present.

45:15.520 --> 45:24.520
Um, less than a high school diploma, which those are, those are anti correlated, but it's, it's just a perfect shape match.

45:25.520 --> 45:27.520
For the vaccine correlations.

45:27.520 --> 45:30.520
It's all just health and wealth.

45:30.520 --> 45:31.520
That's all it is.

45:37.520 --> 45:40.520
They actually, you know, I'm going to go, um, I'm going to go look for comments.

45:40.520 --> 45:45.520
Uh, I, um, you know, while I do this, I'm going to look through the comments here on rumble.

45:45.520 --> 45:49.520
Uh, somebody says I'm heading over to X because the audio is awful.

45:49.520 --> 45:51.520
Sorry about that at the beginning.

45:51.520 --> 45:54.520
Uh, audio is better on locals than rumble interesting.

45:54.520 --> 45:57.520
I remember that in the future, but I'll, I'll have things set up.

45:57.520 --> 46:01.520
We, I just had more wheels, you know, turning more gears going on today.

46:01.520 --> 46:11.520
Um, any comments, any questions in here or war three is taking place right now.

46:11.520 --> 46:13.520
It's a real war with real deaths.

46:13.520 --> 46:15.520
And I think, I, I kind of think that that is true.

46:15.520 --> 46:20.520
In fact, you know, in the middle of the pandemic, Warren Buffett comes out and says,

46:20.520 --> 46:24.520
yeah, class warfare is going on and we're winning.

46:24.520 --> 46:25.520
Right.

46:25.520 --> 46:28.520
He literally said that in the middle of the pandemic.

46:28.520 --> 46:29.520
Right.

46:29.520 --> 46:31.520
That's not conspiracy theory.

46:31.520 --> 46:36.520
That is, uh, some old rich man who happens to spend a whole lot of time, you know,

46:36.520 --> 46:40.520
with Bill Gates and pulling his wealth together with Bill Gates ventures.

46:40.520 --> 46:45.520
Um, really does feel like there's something like that going on.

46:45.520 --> 46:54.520
And I think that this is to, um, you know, push people into a place where they can't stop whatever changes are intended.

46:54.520 --> 47:01.520
I, that's my opinion as to what's going on at this point is that we are probably being brought under global governance.

47:01.520 --> 47:04.520
I don't know if we'll succeed in fighting it off or not.

47:04.520 --> 47:05.520
I hope that we do.

47:05.520 --> 47:11.520
I think that we have been saddled with a clown show in the meantime and people who are staying on a schedule.

47:11.520 --> 47:14.520
And they're televising the revolution.

47:14.520 --> 47:18.520
I think that's what's going on that they have bootstrapped together.

47:18.520 --> 47:20.520
Kind of a new world order.

47:20.520 --> 47:27.520
I mean, that's, you know, I called it World War E. It's World War economics.

47:27.520 --> 47:29.520
It is a global civil war.

47:31.520 --> 47:32.520
So apologies.

47:32.520 --> 47:34.520
I'm going to look through my spreadsheets now.

47:34.520 --> 47:35.520
So I'll be silent for a moment.

47:44.520 --> 48:00.520
Some of these spreadsheets are very large and I hope that they opened.

48:00.520 --> 48:07.520
I actually got more RAM for my computer while I was in the middle of doing this just because the data got to be so gruesome.

48:08.520 --> 48:15.520
I wish I'd stopped and taken the time to learn Tableau.

48:15.520 --> 48:16.520
Okay.

48:16.520 --> 48:19.520
I think this is one of the ones that I was hoping to file.

48:19.520 --> 48:24.520
Let me zoom out here and unfortunately, stream yard jumps over some of the tabs.

48:24.520 --> 48:27.520
I have to take a moment to figure out what's going on here.

48:27.520 --> 48:29.520
Yeah, this is state by state.

48:29.520 --> 48:31.520
This is state by state.

48:31.520 --> 48:35.520
It takes some of these time to load.

48:35.520 --> 48:39.520
And, you know, there are going to be some states that we probably shouldn't even look at like Alaska.

48:39.520 --> 48:44.520
But, you know, do we see anything different that's going on here?

48:44.520 --> 48:46.520
State by state.

48:46.520 --> 48:48.520
I think the answer is yes.

48:48.520 --> 48:51.520
Maybe I put these somewhere else.

48:51.520 --> 48:52.520
Let's see.

48:52.520 --> 48:54.520
Where did I do with all this data?

48:58.520 --> 48:59.520
It says don't touch.

48:59.520 --> 49:00.520
I shouldn't touch it.

49:00.520 --> 49:01.520
Can't.

49:01.520 --> 49:02.520
There we go.

49:02.520 --> 49:04.520
I can move this around a little bit.

49:05.520 --> 49:08.520
Maybe this is the best that I can do.

49:08.520 --> 49:09.520
Yeah.

49:09.520 --> 49:18.520
I'm going to take a look at a couple of like your more ordinary state states of moderate size.

49:18.520 --> 49:20.520
There's Massachusetts.

49:20.520 --> 49:25.520
Massachusetts didn't go through the chaos that everybody else did.

49:25.520 --> 49:32.520
What's interesting is that the degree of like healthy user bias just mostly got sorted out pretty early.

49:33.520 --> 49:36.520
But you do have vaccination come in.

49:36.520 --> 49:42.520
You can see right when vaccination starts, some of these correlations begin to change.

49:42.520 --> 49:43.520
Right.

49:43.520 --> 49:46.520
And what are these correlations to?

49:46.520 --> 49:50.520
These are correlations between.

49:50.520 --> 49:56.520
Between debts and all of these other factors by county.

49:57.520 --> 50:05.520
Such as education status or median household income wealth status unemployment rate.

50:05.520 --> 50:08.520
And you can see the vaccines come in and boom.

50:08.520 --> 50:09.520
That's the variable.

50:09.520 --> 50:11.520
That's the variable that changes things.

50:11.520 --> 50:13.520
Oh, that one variant changed things.

50:13.520 --> 50:16.520
Oh, but there was supposed to be another variant that changed things later on.

50:16.520 --> 50:17.520
Right.

50:17.520 --> 50:20.520
We don't see any change in this going on.

50:21.520 --> 50:24.520
Moving up to the vaccine mandates in 2021.

50:24.520 --> 50:31.520
Not like we saw the hospitalization rates or COVID case skyrocket in the military.

50:31.520 --> 50:32.520
Right.

50:32.520 --> 50:35.520
You don't see you don't see a variant coming and change things.

50:35.520 --> 50:36.520
Right.

50:36.520 --> 50:38.520
So choose between these two variables.

50:38.520 --> 50:39.520
Real truth.

50:39.520 --> 50:43.520
You know, do you see it in any other state?

50:43.520 --> 50:44.520
Nope.

50:44.520 --> 50:45.520
We don't see it in Michigan.

50:45.520 --> 50:46.520
Nope.

50:46.520 --> 50:47.520
We don't see it in Minnesota.

50:48.520 --> 50:49.520
Right.

50:49.520 --> 50:50.520
Look late summer.

50:50.520 --> 50:52.520
2021 vaccine mandates approaching.

50:52.520 --> 50:55.520
We do see something weird happened in Mississippi.

50:55.520 --> 51:00.520
But you know, what where we see things happen is when vaccines roll out.

51:00.520 --> 51:07.520
That's when we see the correlations change in very few of any states do the to the correlations change.

51:07.520 --> 51:10.520
Late summer going into the mandates.

51:10.520 --> 51:16.520
Not like they did in the military, not like you saw for for hospitalizations per case.

51:17.520 --> 51:21.520
Those some sort of discrete data shift that happened in Nebraska.

51:21.520 --> 51:22.520
That's not.

51:22.520 --> 51:23.520
Yeah.

51:23.520 --> 51:27.520
And some of these states, you know, you do have discrete data shifts that happen at some point in time.

51:27.520 --> 51:30.520
But, you know, going into the mandates.

51:30.520 --> 51:32.520
You know, where do you see it?

51:32.520 --> 51:34.520
Some of the states have very small populations.

51:34.520 --> 51:39.520
So, you know, things happen weirdly as it goes, but New York late summer.

51:39.520 --> 51:43.520
You just don't see it, but you see it when the vaccines roll out.

51:43.520 --> 51:52.520
You see suddenly all the correlations freak out before they before they become relatively stable again.

51:52.520 --> 52:01.520
There are a couple of states where things change a little bit at different points of time, but it's different from state to state.

52:01.520 --> 52:06.520
And it would be very hard to figure out what that is without, you know, look on each state basis.

52:06.520 --> 52:12.520
But the majority of the states, the vast majority of them, you don't see any very serious change happen.

52:12.520 --> 52:16.520
That you can blame on variants.

52:16.520 --> 52:20.520
You can blame on variants of concern.

52:20.520 --> 52:31.520
So, if we're the very good of concern, we would see a change in these correlations in every single state uniformly.

52:31.520 --> 52:39.520
Whereas, you know, late summer, we see it happening, you know, we see slight changes happening like a third of the states.

52:39.520 --> 52:44.520
But mostly we see big changes happen in every single state right at the time of vaccine rollout.

52:44.520 --> 52:48.520
We see a reshuffling of these correlations.

52:48.520 --> 52:55.520
Almost every single one, California, oddly, we do not.

52:55.520 --> 53:03.520
And scratching my head as to why that is, but California is so different than other states in so many ways.

53:04.520 --> 53:13.520
But nearly every state, we see reshuffling of correlations going into vaccine rollout.

53:13.520 --> 53:25.520
So,

53:25.520 --> 53:27.520
I'm going to do one by counties again.

53:27.520 --> 53:42.520
I'm going to bring another one up.

53:42.520 --> 53:49.520
So, any sign of Alex Berenson or James Sir wiki?

53:49.520 --> 53:53.520
Any sign that they have, has anybody come in and tweeted?

53:53.520 --> 54:00.520
Anybody said anything?

54:00.520 --> 54:12.520
Yeah, they just do not want to discuss it.

54:12.520 --> 54:17.520
Okay, correlation demographic factors versus cumulative deaths.

54:18.520 --> 54:27.520
Now, this is a really good one, right? Cumulative takes into account that you may have had something very weird go on in April.

54:27.520 --> 54:31.520
Whatever that is, whatever you think that might be.

54:31.520 --> 54:36.520
But ultimately, you see it sort of come apart, come back together in.

54:36.520 --> 54:43.520
Again, and then you see the usual correlations that you would expect of healthy user biases, the dominant variable.

54:48.520 --> 54:51.520
Yeah, and maybe I'm just going to stop there.

54:51.520 --> 55:00.520
The point is, I have dozens of spreadsheets, hundreds of tabs, hundreds of graphs.

55:00.520 --> 55:13.520
And if these guys want to test a variable, they need to show me that they are downloading the data, that they are putting it together, that they have any idea what they're talking about, any idea how to separate out demographics.

55:14.520 --> 55:17.520
But they don't want to show up, do they?

55:17.520 --> 55:21.520
They haven't taken the challenge.

55:21.520 --> 55:28.520
There's no veracity in their opinions of what they're saying here, and that's that.

55:28.520 --> 55:33.520
So, I guess I'll cut this short.

55:33.520 --> 55:37.520
I thought maybe somebody would show up to have a conversation.

55:38.520 --> 55:40.520
So, I'm going to look at the conversation again.

55:40.520 --> 55:43.520
Are there any questions here in the rumble chat?

55:43.520 --> 55:47.520
Mark it who's a tonic live on YouTube and run rumble.

55:47.520 --> 55:55.520
I think who we reported on the last podcast, he was formally employed by CHD.

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The blog says brush your teeth, actually did, just before the podcast.

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All right.

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Okay, I guess I'm going to get off here and I'm going to go into the quarter chat.

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I don't know if there's a way to include that in the stream.

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I guess I'll just stop recording here, but maybe I'll drop a link from the live stream

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from Twitter into rumble so that anybody who ever watches this rumble video can know where

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to go listen to the rest of this conversation.

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But for now, I'll play my outro music and then I'll go talk with Wogpog.

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You