WEBVTT 00:00.000 --> 00:02.780 to unfortunately go on another show. 00:02.780 --> 00:05.580 Dr. Rancourt, it's so nice to meet you. 00:05.580 --> 00:08.860 Same here, same here, can I call you Peter? 00:08.860 --> 00:11.220 Sure, thank you, shall we do that? 00:12.720 --> 00:15.960 Sure, so I thought maybe this would be an opportunity 00:17.400 --> 00:20.560 to present your recent paper, 00:20.560 --> 00:23.520 particularly in light of the claim today 00:23.520 --> 00:27.280 that the Nobel Prize messenger RNA saved 00:27.280 --> 00:29.800 a minimum of 14 million lives. 00:29.800 --> 00:32.400 Right, yeah, no, I'd be happy to. 00:32.400 --> 00:34.880 Did they actually claim that number, Peter? 00:34.880 --> 00:38.160 14 million, no way. 00:38.160 --> 00:41.760 Oh my gosh, is that a Nobel announcement 00:41.760 --> 00:43.560 or some media report or? 00:43.560 --> 00:46.800 Media report, but they're all following the same script. 00:46.800 --> 00:48.320 Yeah, there was an article that claimed 00:48.320 --> 00:49.760 that kind of number, I guess. 00:49.760 --> 00:54.320 Yeah, it's a 14 to 25 million, if I recall correctly. 00:54.320 --> 00:55.080 Right, right. 00:55.080 --> 00:57.840 So I'll just very quickly say the result of our work 00:57.840 --> 01:01.440 in that regard and then we can interact about it. 01:01.440 --> 01:04.040 Okay, all right, let's go ahead and hit it. 01:04.040 --> 01:05.880 Yep. 01:05.880 --> 01:07.240 You'll go ahead, we're already on. 01:07.240 --> 01:09.080 I'm not fooling around here. 01:09.080 --> 01:10.960 You guys are too important for me, 01:10.960 --> 01:13.280 so I already, you didn't say anything crazy already, 01:13.280 --> 01:15.080 so go ahead and start, please. 01:15.080 --> 01:16.720 There you go, we're just starting, okay. 01:16.720 --> 01:19.520 I am thrilled to be on the program. 01:19.520 --> 01:22.280 Jay and Dr. Rancourt, thank you so much. 01:22.280 --> 01:24.360 It's an honor to meet you for the first time. 01:24.360 --> 01:26.960 Yeah, you know, the world has been very interested 01:26.960 --> 01:31.320 in your ecological analysis that involved countries 01:31.320 --> 01:33.400 in the Southern Hemisphere. 01:33.400 --> 01:35.280 Can you give us a capsule of that, 01:35.280 --> 01:37.680 of what that paper showed? 01:37.680 --> 01:40.640 I'd love to, Peter, if I may. 01:41.640 --> 01:44.520 I'm also thrilled to meet you for the first time, 01:44.520 --> 01:46.340 even though it's a virtual meeting. 01:46.340 --> 01:49.760 But here we go, I've been working on all cause mortality 01:49.760 --> 01:51.640 for a long time. 01:51.640 --> 01:54.400 Our first paper on the subject was put out 01:54.440 --> 01:57.120 in June of 2020. 01:57.120 --> 01:59.320 And in that paper, we said immediately 01:59.320 --> 02:02.760 that there were hotspots of immediate mortality 02:02.760 --> 02:05.120 that were synchronous with the announcement 02:05.120 --> 02:08.480 of the pandemic on the 11th of March, 2020. 02:08.480 --> 02:12.400 And we said these immediate surges of mortality 02:12.400 --> 02:16.000 that occur only in hotspots, New York, Northern Italy, 02:16.000 --> 02:18.760 Madrid, a few places like that, and nowhere else. 02:18.760 --> 02:22.600 There was nothing like it in 30 of the US states 02:23.360 --> 02:25.800 that that mortality was inconsistent 02:25.800 --> 02:28.120 with the spreading respiratory disease. 02:28.120 --> 02:30.760 It couldn't be that because it was synchronous 02:30.760 --> 02:32.160 around the world. 02:32.160 --> 02:37.040 And it was, so it was very granular and synchronous 02:37.040 --> 02:38.880 and it didn't spread. 02:38.880 --> 02:41.640 So that was our immediate conclusion 02:41.640 --> 02:45.000 when we started looking at this all cause mortality back then. 02:45.000 --> 02:46.880 Well, let me respond to that. 02:46.880 --> 02:49.320 You know, I was in the thick of it early on 02:49.320 --> 02:51.880 in the pandemic, you know, trying to innovate 02:51.880 --> 02:55.040 with ways of helping people. 02:55.040 --> 02:57.160 But like every other person in America, 02:57.160 --> 03:00.120 I was watching CNN or any newscast 03:00.120 --> 03:02.960 and I was watching this mortality meter 03:02.960 --> 03:05.360 in the upper right-hand part of the screen. 03:05.360 --> 03:08.240 And let me tell you, when one of my patients dies, 03:09.440 --> 03:11.600 from the time they die to the time 03:11.600 --> 03:15.480 I completely finished the death certificate 03:15.480 --> 03:17.320 and have it registered in the system, 03:17.320 --> 03:18.920 it's about six weeks. 03:18.920 --> 03:23.920 So I was wondering how could these instantaneous deaths 03:25.080 --> 03:28.160 come up on a scoreboard because they each have a six week life? 03:28.160 --> 03:31.120 Oh, Peter, this is very important. 03:31.120 --> 03:33.000 We have to be very careful here. 03:33.000 --> 03:35.160 I'm talking about all cause mortality. 03:35.160 --> 03:38.680 So that means irrespective of any cause of death 03:38.680 --> 03:40.560 that anyone might assign. 03:40.560 --> 03:42.080 In other words, I'm talking about. 03:42.080 --> 03:46.520 Even when I'm saying the deaths don't get recorded 03:46.560 --> 03:49.600 until the death certificate is completed. 03:49.600 --> 03:50.440 Really? 03:50.440 --> 03:53.040 Yeah, so the idea is there's always a six week. 03:53.040 --> 03:56.080 No, the databases that I'm working from, 03:56.080 --> 03:58.920 they actually give you the date of death. 03:58.920 --> 04:03.440 Now, the certificate might go into the system late, 04:03.440 --> 04:08.440 but the data is, the actual data is by date of death. 04:09.920 --> 04:11.520 In other words, sure, there's a lag 04:11.520 --> 04:13.640 in terms of when you get the certificates in. 04:13.640 --> 04:17.880 Sometimes there's a lag of as much as a couple of months. 04:17.880 --> 04:22.880 And so you're updating the mortality data as we go. 04:23.600 --> 04:27.000 And it's typically a month or two late, you see, 04:27.000 --> 04:30.440 but then once it goes into the system, 04:30.440 --> 04:32.520 it's by date of death. 04:32.520 --> 04:35.880 Right, I know, but even the National Death Index, 04:35.880 --> 04:38.400 I think runs about six months behind. 04:38.400 --> 04:40.680 So when deaths occur, 04:40.680 --> 04:44.560 it's possible that if someone dies in the hospital, 04:44.560 --> 04:47.400 there may be a more immediate reporting system, 04:47.400 --> 04:50.120 but most of the time there's no, no, no, but let me explain. 04:50.120 --> 04:51.920 There's a misunderstanding here. 04:51.920 --> 04:54.480 See, what I'm talking about is, 04:54.480 --> 04:58.640 I mean, I'm sitting here in June doing my study, okay? 04:58.640 --> 05:02.400 And so this is after the 11th of March, 2020. 05:02.400 --> 05:03.320 No, I understand. 05:03.320 --> 05:05.160 I'm just telling you real world. 05:05.160 --> 05:07.160 I'm just wasting the question. 05:07.160 --> 05:11.240 Real world in March of 2020, 05:11.240 --> 05:13.840 we were seeing deaths go up every day. 05:14.720 --> 05:15.640 Yes. 05:15.640 --> 05:16.480 Okay. 05:16.480 --> 05:20.840 And it's, I was wondering how in the world 05:20.840 --> 05:25.360 are those data feeds that simultaneous 05:25.360 --> 05:30.040 with this lag of a month or two months afterwards? 05:30.040 --> 05:34.400 Well, you know, in terms of reporting 05:34.400 --> 05:38.880 so-called COVID deaths in the media or on the TV screens, 05:38.880 --> 05:41.040 they can be reporting whatever they want, 05:41.040 --> 05:44.880 but actual official all-cause mortality data 05:44.880 --> 05:48.960 is the total of deaths for that day in a given jurisdiction. 05:48.960 --> 05:52.080 So what they might or might not be doing that, 05:52.080 --> 05:54.920 that pops up on your screen in terms of deaths 05:54.920 --> 05:57.080 and what those deaths mean, I don't know, 05:57.080 --> 05:59.600 but I'm working from robust data, 05:59.600 --> 06:03.480 which is actual all-cause mortality, 06:03.480 --> 06:07.080 deaths assigned to a given date and it's by day. 06:07.080 --> 06:09.520 And then some jurisdictions, when they report it, 06:09.520 --> 06:11.240 they'll give it to you by week. 06:11.240 --> 06:12.640 Some will give it to you by month 06:12.640 --> 06:14.240 if it's a smaller jurisdiction 06:14.240 --> 06:15.840 and they want better statistics, 06:15.840 --> 06:19.280 but you see it after the fact. 06:19.280 --> 06:21.680 You have to gather it, collate it, 06:21.680 --> 06:24.440 and you know up to when it is reliable 06:24.440 --> 06:27.880 according to the people that are providing the data, you see. 06:27.880 --> 06:30.240 So up to that date, you've got good data 06:30.240 --> 06:32.080 and that data never changes 06:32.080 --> 06:33.760 and that data has been reliable 06:33.760 --> 06:36.880 since they've been doing this for a hundred years now. 06:36.880 --> 06:39.480 It's very robust, very reliable data 06:39.480 --> 06:42.760 and it is collected irrespective of the cause of death. 06:42.760 --> 06:45.640 So this is just total deaths, okay? 06:45.640 --> 06:49.760 And then so what you do then is you look at the patterning time 06:49.760 --> 06:51.560 of those deaths in a given jurisdiction. 06:51.560 --> 06:53.240 It can be one state in the US, 06:53.240 --> 06:55.760 it can be the whole country or another country 06:55.760 --> 06:57.800 and you follow it as a function of time 06:57.800 --> 06:59.760 and what you will see immediately 06:59.760 --> 07:02.440 is that in Northern latitude countries, 07:02.440 --> 07:06.320 it has a seasonal pattern, a very clear seasonal pattern. 07:06.320 --> 07:09.120 There are always far more deaths in the winter 07:09.120 --> 07:10.480 than in the summer. 07:10.480 --> 07:13.000 So there's a winter peak in all cause mortality, 07:13.000 --> 07:14.760 then you go down to a summer trough 07:14.760 --> 07:17.520 and this pattern has been known for a hundred years. 07:17.520 --> 07:20.800 And what's interesting is in the Southern Hemisphere, 07:20.800 --> 07:22.280 that pattern is reversed 07:22.280 --> 07:24.800 because they're winters in our summer. 07:24.800 --> 07:27.480 So they get their maximum of deaths 07:27.480 --> 07:30.600 in that seasonal pattern during their winter, 07:30.600 --> 07:31.720 which is our summer. 07:32.840 --> 07:35.000 And this is a phenomenon that's well known, 07:35.000 --> 07:37.000 it's basic epidemiology, 07:37.000 --> 07:40.560 it's been known for a hundred years, it's very striking 07:40.560 --> 07:44.520 and it's not completely understood exactly why that is, okay? 07:44.520 --> 07:48.920 There are various models as to why the deaths 07:48.920 --> 07:50.520 are always higher in the winter, 07:50.520 --> 07:54.280 including deaths that are related to cardiac problems. 07:54.280 --> 07:56.440 The only deaths that don't follow that pattern 07:56.440 --> 07:59.880 are the main tumor type cancer deaths. 07:59.880 --> 08:01.680 They don't have a seasonal pattern, 08:01.680 --> 08:05.800 but everything else, the infections, the heart attacks, 08:05.800 --> 08:08.520 everything that is sensitive to stress, I guess, 08:08.520 --> 08:13.000 stress induced, they all have a very clear seasonal pattern, 08:13.000 --> 08:15.040 okay, in terms of mortality. 08:15.040 --> 08:19.280 And so you know what to expect 08:19.280 --> 08:22.480 because you have a pattern that you can see for a hundred years 08:22.520 --> 08:25.000 and you can see it up and down and up and down 08:25.000 --> 08:26.280 is very regular. 08:26.280 --> 08:29.880 And then COVID hits and they announce a pandemic, 08:29.880 --> 08:33.120 they declare a pandemic on the 11th of March 2020 08:33.120 --> 08:36.640 and you get an immediate surge in that all-cause mortality 08:36.640 --> 08:38.920 in certain hotspots. 08:38.920 --> 08:43.640 So only occurring in New York, Northern Italy, Madrid, 08:43.640 --> 08:45.840 Stockholm, a few places like that, 08:45.840 --> 08:50.640 very intense, very sharp surges of all-cause mortality 08:50.720 --> 08:52.880 right after they announced the pandemic. 08:52.880 --> 08:57.120 So the fact that it is coordinated, 08:57.120 --> 08:59.640 the fact that the timing of the event 08:59.640 --> 09:01.480 is related to a political event, 09:01.480 --> 09:03.400 the announcement of a pandemic 09:03.400 --> 09:06.120 and that it is synchronous around the world 09:07.600 --> 09:10.040 and that it's only in those hotspots 09:11.480 --> 09:13.840 from our perspective, 09:13.840 --> 09:16.960 this cannot be the spread of a viral respiratory disease 09:16.960 --> 09:20.320 because it's well known that the time from seeding 09:20.320 --> 09:23.360 of a new pathogen in a population 09:23.360 --> 09:26.120 to when you get an actual surge in mortality, 09:26.120 --> 09:28.840 that time is extremely sensitive to the details 09:28.840 --> 09:31.800 of the population, of the society, 09:31.800 --> 09:33.600 of how they contact each other and so on 09:33.600 --> 09:36.560 and it can vary by months or years even. 09:36.560 --> 09:40.800 So to have synchronicity like that is impossible 09:40.800 --> 09:42.400 even with modern airplanes 09:42.400 --> 09:45.760 because even if you send out flights from the source 09:45.760 --> 09:47.040 all at the same time, 09:48.040 --> 09:52.400 then that's the seeding where they land 09:52.400 --> 09:55.960 but then the time between that original seeding 09:55.960 --> 09:57.960 to when you'll get a surge in mortality 09:57.960 --> 10:00.960 is highly dependent on the local circumstances. 10:00.960 --> 10:03.040 So you can't have synchronicity like that. 10:03.040 --> 10:08.040 So this was clearly not related to COVID like spread 10:08.200 --> 10:10.400 or anything like that at the beginning. 10:10.400 --> 10:12.280 So that was the first thing we noticed 10:12.280 --> 10:15.680 and then we kept studying all-cause mortality. 10:16.680 --> 10:19.040 I've written more than 30 papers 10:19.040 --> 10:22.160 on COVID related things analyzing data and so on. 10:22.160 --> 10:27.160 And what we find Dr. McCullough is that 10:28.920 --> 10:31.480 the excess all-cause mortality 10:33.960 --> 10:38.880 is inconsistent with a viral respiratory spread, 10:38.880 --> 10:40.920 absolutely inconsistent with it 10:40.920 --> 10:44.400 because it does not cross borders. 10:44.400 --> 10:46.440 If you look at European countries 10:46.440 --> 10:48.280 or states in the United States, 10:48.280 --> 10:50.400 you can have mortality in one jurisdiction 10:50.400 --> 10:54.280 and it stops at the border and is not in the other. 10:54.280 --> 10:56.720 So this mortality at the beginning 10:56.720 --> 11:00.880 was related to what was being done in those jurisdictions. 11:00.880 --> 11:04.080 So for example, we wrote a paper with John Johnson 11:04.080 --> 11:06.880 at Harvard University, we co-authored a paper 11:06.880 --> 11:09.600 where we showed that when you compare U.S. states 11:09.600 --> 11:11.880 if you take states that share a border 11:11.880 --> 11:14.160 and one locked down and the other didn't 11:14.160 --> 11:16.160 they all cause mortality in the lockdown state 11:16.160 --> 11:17.160 even though they're very similar 11:17.160 --> 11:19.480 and they're sharing a border is always higher, 11:19.480 --> 11:23.040 significantly higher than in the non-lock down state. 11:23.040 --> 11:26.360 So we're able to, we have a lot of reason 11:26.360 --> 11:29.440 to come to the very firm conclusion 11:29.440 --> 11:34.160 that what I believe now is that all of the excess 11:34.160 --> 11:36.000 all-cause mortality that occurred 11:36.000 --> 11:38.320 before the vaccines were rolled out 11:38.320 --> 11:40.360 between when they announced to that time 11:40.360 --> 11:43.480 is all due to lack of treatment 11:43.480 --> 11:47.040 and aggressive medical protocols in big hospitals 11:47.040 --> 11:49.480 and aggressive government measures 11:49.480 --> 11:52.400 that isolated people and stressed them out 11:52.400 --> 11:54.760 and including very vulnerable people 11:54.760 --> 11:58.320 like the 11 million who are disabled 11:58.320 --> 12:01.200 by serious mental illness in the United States, 12:01.200 --> 12:02.080 that kind of thing. 12:02.080 --> 12:05.480 So when you look at the age structure of this mortality 12:05.480 --> 12:07.840 and its geographical distribution 12:07.840 --> 12:10.280 and its association with all these things 12:10.280 --> 12:13.280 that they know were being done in these jurisdictions 12:13.280 --> 12:16.440 we have concluded that there was, 12:16.440 --> 12:19.360 there is no evidence for a particularly virulent 12:19.360 --> 12:21.480 new pathogen that was spreading 12:22.440 --> 12:25.720 that in fact all of the excess mortality 12:25.720 --> 12:27.560 everywhere we've looked in the world 12:27.560 --> 12:29.760 can be understood in terms of 12:29.760 --> 12:32.400 this is what happens when you do this to people. 12:32.400 --> 12:34.920 This is what happens when you stop treating them 12:34.920 --> 12:36.840 for all the usual things that they have 12:36.880 --> 12:40.480 and when you destroy their lives and stress them out 12:40.480 --> 12:42.280 and force them to be isolated 12:42.280 --> 12:45.520 this is what you get, you get this kind of mortality. 12:45.520 --> 12:49.920 And so this mortality is very heterogeneous 12:49.920 --> 12:53.120 until you start roll out the vaccines. 12:53.120 --> 12:55.080 Then once you start rolling out the vaccines 12:55.080 --> 12:58.160 because that was done pretty much simultaneously 12:58.160 --> 13:01.800 around the world, you have everywhere 13:01.800 --> 13:04.320 an increase in all-cause mortality. 13:04.320 --> 13:09.040 You move into a regime of higher all-cause mortality 13:09.040 --> 13:12.200 and then you stay there while you're rolling out the vaccines 13:12.200 --> 13:14.560 and then every time you roll out a booster 13:14.560 --> 13:17.080 you get a peak, an extra peak in all-cause mortality 13:17.080 --> 13:20.960 associated in time with that booster. 13:20.960 --> 13:23.240 And this is stunning, we see this 13:23.240 --> 13:25.160 and you can do it by age group. 13:25.160 --> 13:27.880 So you can look at the 90 plus year olds 13:27.880 --> 13:30.520 or the 80 to 90 year olds and so on. 13:30.520 --> 13:33.640 And you see a very sharp booster rollout 13:33.640 --> 13:36.280 because they did it very quickly in a given jurisdiction 13:36.280 --> 13:38.200 and immediately follows it 13:38.200 --> 13:42.880 is a very sharp unprecedented peak in all-cause mortality. 13:42.880 --> 13:46.560 So this is extremely clear, it cannot be an accident 13:49.280 --> 13:52.120 and therefore you can quantify it. 13:52.120 --> 13:54.160 You can say, well, how many deaths occurred 13:54.160 --> 13:56.840 given how many injections you gave? 13:56.840 --> 13:59.520 So that's what we do, we've been quantifying it. 13:59.520 --> 14:02.080 And what's surprisingly is what we find 14:02.080 --> 14:04.800 is that around the world in every jurisdiction 14:04.800 --> 14:07.600 we've now looked at over 100 countries, 14:08.560 --> 14:11.920 the mortality risk per injection 14:11.920 --> 14:13.520 is pretty much the same everywhere. 14:14.480 --> 14:18.800 So all ages, it's about 0.1%. 14:18.800 --> 14:23.080 So one, actually we refined it recently 14:23.080 --> 14:27.080 is 0.126% with an error bar on it. 14:27.080 --> 14:31.440 And so that means that for every 800 injections, 14:31.440 --> 14:32.800 one person will die. 14:34.320 --> 14:37.440 So it's one person per 800 injections. 14:38.520 --> 14:43.680 Now the important thing is that that risk of death 14:43.680 --> 14:47.400 per injection is not uniform with age. 14:47.400 --> 14:51.240 It increases exponentially with age 14:51.240 --> 14:54.360 and it is dramatically higher the older you are. 14:54.360 --> 14:59.360 The doubling time by age is four to five years of age. 15:00.040 --> 15:03.240 Every four to five years of extra age that you have, 15:03.240 --> 15:06.560 your risk of dying per injection doubles. 15:08.040 --> 15:12.360 So we hear about the deaths in young athletes and others, 15:12.360 --> 15:15.640 but I've always been struck by the McLachlan analysis 15:15.640 --> 15:18.640 from Queens University very early on using the VAERS. 15:18.640 --> 15:20.160 It was the only VAERS analysis 15:20.160 --> 15:22.840 that read every single vignette 15:22.840 --> 15:25.760 and then adjudicated the two different adjudicators 15:25.760 --> 15:28.480 and they had an agreement process 15:28.480 --> 15:31.000 to finally adjudicate the death. 15:31.000 --> 15:32.560 And they only had about 1,200 deaths 15:32.560 --> 15:34.840 at that point in time in VAERS. 15:34.840 --> 15:36.440 And what was striking is, 15:36.440 --> 15:38.800 that was when it was being rolled out in the nursing homes 15:38.800 --> 15:40.920 in January, February, March. 15:40.920 --> 15:44.680 It was the seniors, just as you said, that were dying. 15:44.680 --> 15:46.960 And in the McLachlan analysis, 15:46.960 --> 15:48.960 it was striking how quick it was. 15:49.840 --> 15:54.840 There was something like 16% or so died within a few hours. 15:55.360 --> 15:58.840 Of those who died died within a few hours of the shot. 15:58.840 --> 16:01.800 A large fraction was within 24 hours. 16:03.000 --> 16:07.360 And so it was almost as if the reactor genicity of the shot 16:07.360 --> 16:09.640 or the early production of the spike protein 16:09.640 --> 16:12.600 from the genetic material we know this occurs within an hour. 16:12.600 --> 16:15.000 We know it's circulatory in the bloodstream. 16:15.000 --> 16:17.960 It's simply not this lethal protein 16:17.960 --> 16:19.880 just is not tolerated by the elderly. 16:19.880 --> 16:21.040 It makes sense. 16:21.040 --> 16:23.560 In the McLachlan analysis, 16:23.560 --> 16:28.560 86% of the time there was no other explanation. 16:29.680 --> 16:31.120 They were in their usual state of health. 16:31.120 --> 16:33.920 They took the vaccine and then they succumbed to death. 16:33.920 --> 16:34.760 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.