18 - Misuse & Misinterpretation of COVID Statistics with Professor Norman Fenton

In this episode, I interviewed Professor Norman Fenton,  Mathematician and Professor of Risk Management at Queen Mary University, London, about the data around COVID and the COVID injection program and wanted to know whether the data matches what we are being told by those in authority .

We discussed:

5:54

Professor Fenton's background and interest in COVID data

10:05

Were the infection fatality rates high in the Spring of 2020?

13:44

The wrong treatments given and the effective treatments banned

18:21

Relative Risk Reduction vs Absolute Risk Reduction

20:49

How many died with or due to COVID?

31:03

The Risk Factors for COVID death

36:38

Misleading information 

43:16

Why did the un-jabbed start dying of non-COVID causes?

49:45

Are the jabs effective according to the data?

53:00

All Cause Mortality rates 2020 v 2022 and cardiac related athlete deaths

58:04

Have the jabs saved millions of lives as reported in the media?

1:03:01

According to the data should pregnant women be taking the jab?

1:09:07

What happened to The AstraZeneca jab and what did the Pfizer trials tell us?

1:16:38

Why did the mainstream media fail to report any alternative views from the mainstream narrative?

You can find Professor Fenton @
www.normanfenton.com
https://twitter.com/profnfenton
https://www.youtube.com/@NormanFenton81
https://gettr.com/user/normanfenton

Chapters

• 5:54 Professor Fenton's background and interest in COVID data
• 10:05 Were the infection fatality rates high in the Spring of 2020?
• 13:44 The wrong treatments given and the effective treatments banned
• 18:21 Relative Risk Reduction vs Absolute Risk Reduction
• 20:49 How many died with or due to COVID?
• 31:03 The Risk Factors for COVID death
• 36:38 Misleading information
• 43:16 Why did the un-jabbed start dying of non-COVID causes?
• 49:45 Are the jabs effective according to the data?
• 53:00 All Cause Mortality rates 2020 v 2022 and cardiac related athlete deaths
• 58:04 Have the jabs saved millions of lives as reported in the media?
• 1:03:01 According to the data should pregnant women be taking the jab?
• 1:09:07 What happened to The AstraZeneca jab and what did the Pfizer trials tell us?
• 1:16:38 Why did the mainstream media fail to report any alternative views from the mainstream narrative?
• 1:21:05 How did Norman's work on COVID affect his career?

Transcript

[00:00] Professor Norman Fenton: What's data really telling us here, right? And what we found fairly early on, I mean, within the first few weeks of the crisis, the data wasn't really telling what the media and what the that type of so called experts were telling us. It makes sense in terms of what we were seeing with our own eyes that there was that, but also, we were told these incredibly high fatality rates, this was the thing that was driving it from the beginning. We were told we were given this impression that if you got this infection, there was a pretty good chance you were going to die. That was the thing. And so that's what we focused on. That was the first thing, from a statistical probabilistic perspective, that we focused on. The way, when you look at the data, it was impossible to get those peaks like that consistently in each of the age groups. And there was a much simpler explanation. And that simple explanation was that people who were dying shortly after vaccination were being classified as unvaccinated. Those who rely only on the mainstream right, whether it be BBC News, Sky News, or the mainstream newspapers, will be completely oblivious to almost everything that we've discussed today.

[01:23] Announcer: Welcome to the Radical Health Rebel podcast with your host, Leigh Brandon. If you enjoy the podcast, please leave a five star rating and the warm review. Your opinions are important, and your ratings help grow the podcast and help educate people to lead a healthier, more productive, fulfilling, and happy life. If video is your thing, please check out the Radical Health Rebel YouTube channel, where you'll find Fun Bitesize clips from each episode. And now, here is Leigh, the radical health rebel, with this week's podcast.

[02:05] Leigh Brandon: Professor Norman Fenton. Welcome to the Radical Health Rebel podcast. How are you?

[02:11] Professor Norman Fenton: Yes, thanks. I'm good. Thanks. Thanks for having me on.

[02:13] Leigh Brandon: Great to have you here. So the title of today's episode is Misuse and Misinterpretation of COVID Statistics with Professor Norman Fenton. In January 2020, we started seeing people in the streets of China nosediving to their doom. By March 2020, lockdowns around the world were being enforced based on computer modeling produced by Professor Il Ferguson of Imperial College, a man whose track record of previous computer model predictions were extremely inaccurate, to say the least. We were being told by a computer billionaire that the only way back to freedom was by the entire global population receiving a yet to be produced experimental injection with no efficacy or state safety data. We were told that there were no current medical approaches available, that lockdowns and masks were the only solution until the Untested soontobe mandated injection was ready, and that everyone was at risk, and that we had to assume that everyone had a disease to protect granny. The test used to detect the disease was designed by Carrie Malice, who shared the Nobel Prize of Chemistry in 1993 for the PCR test. Unfortunately, his untimely death was in 2019. But prior to that, he made it clear that the PCR test could not be used to diagnose viral diseases. These tests were used for diagnosis of COVID-19 and used as a key tool into the safety and efficacy of the injections in late 2020. We were then told that the injections were 90% to 95% effective, and the public were reassured by politicians, the media, and TV doctors that the injection, despite no medium or long term data, would stop you getting the honors, would prevent you from needing hospital treatment and stop you passing on to others, and it was perfectly safe. Many doctors also believed this to be true for much of 2020. I tried to warn that perhaps they shouldn't believe everything they hear and to not blindly follow what they were being told by authority figures. I ended many of my social posts with the hashtag Numbers easily manipulated, people easily fall. I was frequently banned from social media sites for sharing factual information as it went against their community guidelines. And in my book, released in May 2020, coronavirus Survival Guide, that's available for free, I quoted my musical hero Stevie Wonder when he said, when you believe in things you don't understand, then you're suffering. Superstition ain't the way. Now, I believe it's vitally important to be informed before making potentially life changing or life ending decisions, rather than just believing what you're being told by self proclaimed experts, especially when those experts suppress censor and ridicule opposing views and deny the opportunity to open public debate. So today I have one of the most qualified people in the world to discuss the reality of the data around COVID and the COVID injections, a man who has put his head above the parapet and risked much to share the truth of the data. So, Norman, I'm really excited to have you on the show today to discuss this very important topic and for you to drop a few truth bombs in the process. So to kick things off, can you share with the audience a little bit about you, your work and why you're qualified to talk about the data and the COVID data in particular?

[05:55] Professor Norman Fenton: Yeah. Okay, well, thanks. That was a great introduction. You've covered a lot of the stuff that I've been looking at myself, and in fact, a lot of the work that we've been doing is kind of being geared towards a rigorous analysis and confirmation of some of those points. But anyway, I'm a mathematician by training, and my current focus is on what we call sort of critical decision making, and in particular, quantifying risk and uncertainty. And we use a type of mathematics and statistics called a Bayesian approach, which enables us to properly quantify uncertainty and risk when you don't necessarily have all of the necessary data available, where you've got to kind of like, combine bits of data, bits of expert judgment, that type of thing. And we're looking for. So we don't just do kind of like straight number crunching, which is a lot of statisticians, and sort of classical risk assessors do. We try and look for sort of calls and explanations for the data that we're seeing. We look at biases, uncertainty about its accuracy, and also sort of key information that's missing. And those are the sort of issues that you see in lots of the data that was coming out around the COVID era. So a lot of my work prior to the Pandemic, prior to that was actually involved in risk assessment, in sort of medical decision making. In fact, I was leading at the time a very large interdisciplinary project funded by the Engineering and Physical Sciences Research Council on improved decision making risk for chronic medical conditions that we were looking at. So this involved looking at this whole sort of risk of by taking account of data and expert judgment for things like rheumatoid arthritis, chronic heart failure, diabetes, that type of thing, and also even things like we also look at multiple sclerosis, pelvic floor syndrome. So a lot of these kind of like, chronic medical conditions and I was used to working with clinical experts in those fields. So we would combine we would basically get the data and where there was lots of uncertainty about the data and about how to in order to get that data into sort of accurate risk assessment, you need to get that medical, that clinical input. So it's kind of like inevitable that when the whole sort of COVID thing exploded, we would sort of be drawn into looking at doing the same kinds of things, trying to analyze the data, get together, get some clinical expertise involved, and then really trying to explain what's the data really telling us here. Right. And what we found fairly early on, I mean, within the first few weeks of the crisis, the data wasn't really telling what the media and what the at that time, the so called experts were telling us. It just didn't seem right. It didn't seem right. We were told these you mentioned about the people you're seeing all these scenes of people sort of dropping dead in China, and then they were showing things in Italy. Well, we weren't actually seeing that here. So already, even from a non statistical perspective, forget about the data, did it make sense in terms of what we were seeing with our own eyes? There was that, but also these incredibly we were told these incredibly high fatality rates. This was the thing that was driving it from the beginning. We were given this impression that if you got this infection, there was a pretty good chance you were going to die. And that was the thing. And so that's what we focused on. That was the first thing from a statistical probabilistic perspective that we focused on.

[10:20] Leigh Brandon: I myself was looking at the data probably not with as good an eye as you do, but it was obvious to me that they were underestimating the number of people that were infected in the first place. And I think I actually re read my book this morning, and initially they were saying that the fatality rate was 7%, and then I think Jay Batacharian was saying it was less than 1% at the time.

[10:46] Professor Norman Fenton: Yeah. And also, John IDs, we were one of the first I think we might have been first actually have a peer reviewed paper published on the infection rates and the infection fatality rates. Right. And what we did there was what we reported was indeed exactly what you just said, that the infection rate, the number of people who likely had this virus was, at the time much higher than was being reported in that sort of spring of 2020. Whereas the infection fatality rate, that's the probability you're going to die if you are infected with it was far lower. It was much lower. We got that by just looking at the publicly available data from all of the different sources that you could at the time. And we applied our kind of Bayesian analysis to that. In fact, the results that we came out with were consistent with what people like John Ion Edis yeah. That's probably predicting we were actually estimating even lower rates. And of course, even then it was clear that the risk of death, the risk of death of anybody really above the age of 80 was incredibly low. This whole, you know, that almost what can you call it? It's kind of a mania around it. The interesting thing here is another thing that was, of course, driving. You mentioned about the PCR test. Now, originally, in that early period, the reason why you had this kind of, like, part of that mass hysteria and the overestimation of the fatality rates was actually, at that point, the only people who were getting actually tested were getting the PCR tests were apart from sort of frontline health workers, but they were just a small, tiny proportion of those. The main people who get tested, of course, are people who are already severely ill hospitalized with the virus. Right. Now, if the only people you're testing are already severely ill with the virus, then you're going to get this exaggerated fatality rate. But remember, also, and this is something that we didn't know at the time, right, and I'm increasingly of the belief now, again, this is mainly coming from the very experienced clinical people I've worked with. And these are people who maybe have to still remain anonymous, actually, if they're clinicians is the evidence that much of that early death wave, the real death wave, and there was a real excess death wave of COVID deaths in March and April 2020, was due to inadequate poor medical procedures. They were given the wrong treatment. We know these people shouldn't have been they shouldn't have been put on ventilators. There were plenty of early and they shouldn't have been given things like renters. They were given essentially, drugs which were not the right treatment for people with that type of condition. And also they weren't given people who, in the early stages of the virus, in the early stages of the virus, were not given perfectly well treatments that we know are quite effective. I won't go into the details of that, but I think probably your listeners might be aware. But there were and are plenty of early treatments which have subsequently been proved to be very effective. And they were known at the time. They were known at the time. But as you said, I mentioned in.

[14:37] Leigh Brandon: My book, there was a study, albeit it was from China, and it was a relatively small study, but they did a study, people with Kobe, this was premch, so this was early 2020, and they did a study and they gave some of the patients hydroxychloroquine, azithromycin, zinc. And there was one other, I can't remember what it was 100%. No, heparin I think it was 100% of those were symptom free and PCR negative. Within five to six days, 100% of them, about 50% were symptom free and PCR negative that just had hydroxychlorine on its own. There's a lot of studies out there saying, oh, hydroxychloricine doesn't work well, it's not very good. Well, if you put it on its own, it's probably not that effective. But if you use it with the other treatments that were known to be very effective, then you're going to get much better results.

[15:36] Professor Norman Fenton: Yeah. And you know, that what really effectively killed the hydroxychloricine as a treatment throughout the world was this ridiculous, this fraudulent study that was published in Lancet Surgery study. Do you know about that one? Yeah, I mean, they were giving wrong dose, some people argue. Deliberately giving wrong doses. I don't know about certainly they were overdosed on. It was the wrong dosage. There was all kinds of flawed there's all kinds of flawed studies. And yet when they've been subsequent studies into things like Ivometin and other treatments, they've tried to claim that those studies don't live up to the high quality of the randomized controlled trials that we expect. And then there's been, again, the other more recent studies which have attempted to kind of delegitimize use of things like Iveomectin. Those are also proven to be highly politicalised and flawed. So we did look in we actually looked at all of the studies into Iveomectin. We did a metaanalysis. And even if you take out the studies which have been claimed to have been themselves fraudulent or implausible, even when you take those out, there is very solid evidence that Ibamectin, when applied early and in conjunction with some other drugs, is effective. This has been a major part of the scandal, really, that refusal to consider these early treatments, alternative treatments.

[17:23] Leigh Brandon: I've got an idea as to why they were suppressed. Have you got any idea as to why?

[17:29] Professor Norman Fenton: Yeah, I do believe now that they were suppressed. Because if it had been established that there were alternatives, there was an adequate treatment, then the injections would never have.

[17:44] Leigh Brandon: Got the emergency use authorization, emergency authorisation.

[17:49] Professor Norman Fenton: Use, that's the thing. So that was needed, that was a necessary requirement to get that authorization.

[17:58] Leigh Brandon: And one of the things as well, I think that was potentially misleading. And this is something perhaps you could explain the difference between an absolute and a relative risk.

[18:06] Professor Norman Fenton: Yes. So let's take an example. Let's take the example of colorectal cancer which affects let's say obese people and nonabees people slightly differently. So the figures here are not exact, but they're roughly what boys kind of like known. So basically, two out of every 100 nonabees people will die of colorectal cancer, and about three out of every 100 obese people will die of colorectal cancer. So we can ask the question, well, what's the risk of dying from colorectal cancer if you're obese? Well, the relative risk is 50%, because the way we calculate that is we say it's the we have to look at the percentage of obese people who die of corrective cancer, which is 3% divided by the percentage of none of these people who die of corrective cancer, which is 2%. So it's three over two. And then it's basically it's one minus three over 2%, which is your 50%. So you can say there's a 50% relative risk of dying of colorectal cancer if you're obese. But of course the absolute risk is just the difference. It's just 3% -2% it's just a 1% increase. So you've got a 1% increase of dying from colorectal cancer if you're obese. Now, if you think about that's, the one that's really important, it's that 1% it's the absolute risk. It's the one which is actually meaningful. The relative risk is generally, of course, massively exaggerates risk in most circumstances. There are times where we do want to use a relative risk. But in most of these cases, and particularly the cases where it's used in reference to COVID and stuff like that, and the vaccines, et cetera, it's much more reasonable and intuitive and meaningful to use the absolute risk rather than the relative risk.

[20:11] Leigh Brandon: Yep. But yeah, we were being given the.

[20:13] Professor Norman Fenton: Relative risk risk almost always the relative risk, which always exaggerates the risk in people's minds.

[20:21] Leigh Brandon: Yeah. So the next thing, I guess that's quite important. On the subject of the data around COVID, I think in the first twelve months it was something like 170,000 reported deaths in the UK somewhere around there. But there's a difference, of course, between dying with COVID and dying because of COVID Can you give us a little explanation of that?

[20:50] Professor Norman Fenton: Yeah, so I can give you the numbers and also an explanation for it. So, first of all, we know that the classification of a person dying, those who are classified as COVID deaths were anybody who died within 28 days of a positive PCR test or who were clinically diagnosed with co. But the key thing is, almost all of them, it's within 28 days of a PCR test. And of course that means if somebody happens to be died in a road accident within some time after they tested positive a covered, then they'll be classified as a COVID death. And it doesn't matter what other mobility led to their death if they happen to have a positive PCR test that are classified as a COVID death. So that's now, I mean, at the time this wasn't well done. I think people now that's something that people are now at least are kind of well aware of. But how does that translate into this actual data? Well, there was this an interesting Freedom Information request made at the end of the most relevant one, which was the end of 2022, which looked at asking for the number of all COVID classified deaths and how many of those were in people who had no other serious comorbidities. And at the time you mentioned the figure 170. I think the figure I thought was maybe for England, which was 135,000 and less than five. I think it was less than four and a half percent of those less than four and a half percent of those deaths were people who did not have some other serious combility. Right? So it's a very low percentage. So in general, it's less than 5%. And of course it's particularly interesting when you look at the data for young people. So for people under the age of 20. We now know from another Freedom Information request that I believe again, up until certainly it might be even a later period, but certainly the first two years of the pandemic it's something like I believe it's only three people under the age of 20 without any other serious culmobility have died. So when you hear this, when you talk about the risk of death from COVID to young people, well, there's something confusing about that as well, because people don't realize that you've got to make the difference between what's the probability of dying if you've got the disease? As opposed to just what's the problem of you dying from it? So there's a difference there. But even so, whichever way you look at it, when you get these numbers of naught point, these are really incredibly tiny probabilities. Essentially, when we say there's essentially no risk of young, healthy people dying of this disease, it's certainly it's not nonexistent, but it's similar to, I think, probably less than flu for young people. I mean, more young people will die of flu than COVID if they're healthy.

[24:06] Leigh Brandon: So what I think a lot of people still believe is that well over a hundred thousand people have died from COVID when perhaps that's died with COVID.

[24:18] Professor Norman Fenton: Rather than rather than from COVID.

[24:24] Leigh Brandon: I've used the analogy you could die wearing underwear. But underwear, what did that cause you to die? Yeah, right. And a lot of people early on who didn't like the fact that I was suggesting they think for themselves and saying to me, well, they might have had other morbidities, but if they didn't get covered, they wouldn't have died.

[24:46] Professor Norman Fenton: Yeah, we don't know.

[24:47] Leigh Brandon: So how do you know that?

[24:50] Professor Norman Fenton: We know the kind of data and asset that we've applied suggests that wouldn't be the case. It's interesting that I noticed I was making a note in your introduction you were talking about you reminded you the analogy of something. It's Stevie Wonder song. There's another analogy of a famous song here with this whole point about COVID that once you've been positive PCR tested with COVID for quite a long time afterwards, for whatever reason, if you happen to get hospitalized, you're still going to be called a coat. You sort of a covered case for quite a long time. It reminds you of the Hotel California here. You can check out any time you want, but you can never leave.

[25:36] Leigh Brandon: Yeah, that's definitely a good one. Another analogy I used to try and suggest to people and say, look, if you look at the people that are getting ill and dying, they're either over the age of life expectancy anyway, or they've got other and often severe morbidities. And the analogy I would use and say how many people die of the wind blowing generally don't die of the wind blowing. And I said, what if you were walking along the edge of a cliff?

[26:04] Professor Norman Fenton: That's a good analogy.

[26:05] Leigh Brandon: Would you say if the wind blew you off, would you say the wind killed you? Well, no, hitting the ground at a high speed would kill you. It's just the wind that blew you off because you were right on the edge anyway. But a lot of people got offended by me saying that.

[26:21] Professor Norman Fenton: Well, I think the I mean, again, even the offset statistics kind of like concede that the large, a very large proportion of those deaths in that early period of early spring of 2020 would likely a lot of those people would likely have died within a kind of a six month period anyway.

[26:49] Leigh Brandon: I think the average age was 82 point something early on.

[26:52] Professor Norman Fenton: Yes, it was. And so when you combine that with the fact that they were given inappropriate treatment, which likely speeded the deaths anyway, as well as the virus itself, you've got that combination of effects which led to that very high number. Of. Now, incidentally, one of the other things that we've done in our analysis is that we believe that whereas there was a genuine, let's say, excess peak in death due to the virus in that early period, the so called massive second and third virus, which they talk about, has been far greater than the first wave. We don't believe that those were truly major waves. I mean, those were mainly due to the massive increase in testing of asymptomatics. You got these massive increase in case numbers, right? So people who many people who didn't even have the virus, that were classified as having the virus, that's the thing that was driving these you did get you did get this massive increase in number of COVID cases in that socalled second wave from sort of starting sort of october, November 2020. And you look at the case numbers, the case numbers went up to sort of much higher than in that first wave. That's because we were testing far many more people. And also, in contrast to that first wave, as I said, we were only testing people were sort of hospitalized, seriously ill with the virus. We were testing everybody. But it was when everybody was going back to everybody was going back to all the kids going back to school, people going back to work, when the lockdowns first when the first lockdown was being eased, everybody was having to go out and get tested, sort of every day or every other day and all that kind of thing. So this incredible increase in testing, mainly of Asymptomatic people, and we know, we know from part of the serious work that we've done, it's not just how inaccurate those tests are, it's the fact that a very large proportion of those who testymatics who test positive will not necessarily have the virus. So that's where you get these that's the false positive rate of interest. It's not how accurate the test is, but how many what proportion of those who are asymptomatic who test positive have the virus. And that's generally a lower percentage of those who are testing positive. And that was what was driving those numbers. And say, if you look at other indicators at the time, what I believe are more accurate indicators of the true scale of the true scale of the disease, you can look at the NHS dashboard. The dashboard. There's a COVID dashboard for nine nine nine calls, ambulance calls, and nine nine nine. So COVID triage course, right? And there you see this massive you do see a real peak in the spring of 2020, but when you look at that so called what was supposedly the massive peak later winter of 2021, actually, it's a ripple. It's much like you see in any other sort of flu type virus. It goes up a bit, but nothing like it was initially. Whereas you're getting the opposite view of a massive wave. When you just look at the when you just look at the NHS dashboard but when you look at the COVID dashboard that was used by all the TV stations and the media to show this massive wise.

[30:52] Leigh Brandon: And in terms of the known risk factors, is that something you've looked into in terms of the data?

[30:58] Professor Norman Fenton: Yeah. So, interesting enough, looking at the risk factors early on, that wasn't considered controversial. So we had some stuff published about this, we looked at things like the differences in ethnicity, which again, I think we found were a bit exaggerated in terms of there was a lot of stuff on, for example, Bayme people being especially at risk. And it turns out actually it's not the ethnicity, but it's the sort of the socioeconomic factors and things like the size of the household, number of people in households and that which are the root causes of that type of thing. We looked at, interesting enough, the smoking risk fact that was of interest because it was being claimed that smoking was actually an inhibitor that actually smokers were less likely to get coverage. And it turns out that analysis that was kind of like there's a sort of a classical probabilistic issue there, which was maybe creating something of an illusion on that. I think that there may be a little bit in it. But again, that was exaggerated any effect there. So that was the type of stuff certainly things like obesity, vitamin D deficiency did seem to be important. But yeah. So there are these bursts of but of course, by far away the most important one is age. I mean, age compared to the rest. It blows more out of the water.

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[34:51] Leigh Brandon: Yeah, one of the things I saw early on was that smoking reduced your risk of getting COVID. But if you got COVID, it increased your risk of dying compared to a nonsmoker.

[35:02] Professor Norman Fenton: Yes, but this is again, it's all to do with these complex calls or relationships that you've got going on which lead to counterintuitive results. The reason why you'd see maybe apparently be less likely that smokers get the affected early on. This is again all connected with who what type of people were being tested early on for it. And other than people who already seriously are in hospital, right? The only others who were being tested early on were frontline hospital staff. They tend to not be smokers. Okay? And so there was all kinds of interesting confounding and complex cause interactions around that.

[35:50] Leigh Brandon: So again, that's another example of numbers easily manipulated and me being really fooled.

[35:58] Professor Norman Fenton: Yeah, I mean these kind of like statistical illusions. I mean early on you've got this very early on. To be fair, there was a statistical illusion, a wellknown statistical illusion which was claiming that right from the very beginning that the vaccines were very unsafe. But it was actually now because got a lot more to say about what we know now about the truth or safety in efficacy. But very early on there was misleading information put out that oh look, the vaccines are really unsafe because they're killing the vaccinator dying at much higher numbers than the unvaccinated. But at that point people were simply looking at the overall numbers which without taking account of the fact that most of the people who were at that time most likely to have been vaccinated with a very old of course they've got a much higher in the and some far more of the old people getting vaccinated. So virtually all of the deaths in that older age group and in the younger age group, very few were getting vaccinated, relatively few deaths. But when you aggregate it together, you get this misleading result that it looks like it's the vaccinated dine a much higher rate. Now of course, since then that's an example of where the statistics were being wrongly used to claim something kind of like an argument against the safety and efficacy of vaccine. But since then there's been lots of arguments the other way. Lots of statistical errors have been made to paint the vaccines as safer and more effective than they truly are. Of course, that's something that we've been summoned to death in the last year.

[37:53] Leigh Brandon: Actually, just on your previous point about more older people were injected, but then more older people die anyway and that's why it's always important that when you're looking at statistics, you need to do it in age groups. You can't just look at the whole population as a whole and say well, it's this or it's that because as we know, the chance of a ten year old getting in and dying from COVID is very different from a 90 year old. Yeah, that's an extreme example but it's a valid example.

[38:27] Professor Norman Fenton: Yeah. And one of the problems when we first started looking at the vaccine alternative surveillance data was being put out by the Office for National Statistics, because we decided that the only fair way to determine whether the vaccines were safe and effective was to look at this allcause mortality and to look at it in the different age groups. These different age groups. And one of the problems we had first four is they weren't properly they only had these incredibly crude age categorizations. I think initially they were doing things like under fifty s and sort of been over 50s in ten year age brackets and again, that wasn't sufficiently refined to be able to make any definitive conclusions you need to have these fairly refined age categories. Certainly five year age groups is certainly what you need. And if you haven't got that, then they use this so called age standardized mortality metric, which I'm never really comfortable with that again, I don't want to go into statistical there's some complex statistical issues around that, which I'm going to here. But that's where they try and combine it. Instead of saying, Right, instead of going through each of the different age groups and saying, here's what the risk is, here's what the benefit is, which is what you should be doing, they'll give it overall. They'll say, right, we've got this age standardized metric which enables us to give an overall judgment of the risk and benefit. Well that can be highly misleading as well and that's problematic. So we very much prefer to go for these age, very specific age categorized.

[40:16] Leigh Brandon: Distinctions and obviously we've got, I guess almost two years worth of data now from people that have been jabbed so far. What's that looking like in terms of mortality of the jabbed versus the unjabbed?

[40:34] Professor Norman Fenton: Well, that's a good question because it is all caused mortality is the metric we should be using to determine the safety and effectiveness of the Jab. Because think about it this way if COVID is as deadly, as serious as claimed. And if the vaccine, the Jab, is as safe and effective as claimed, then what we should be seeing is more people over a given period in each age group dying of any cause. Forget about whether it's COVID or non COVID because we've got a lot of problems about the classification. Should be more people dying from any cause if they're unvaccinated than if they're vaccinated for the following reason. First of all, if it's deadly and the Jab is safe, then those who are unjabbed are going to be getting supposedly getting covered in dying compared with those who are Jabbed. So you should be seeing this big difference in mortality due to the covered deaths. But on the other hand, if the jab is safe, it doesn't have any serious it isn't killing people through adverse events, then you shouldn't see much. You shouldn't be seeing any additional excess mortality in the jab compared to the unjab. So when you put those together, what you should be seeing is fewer people dying in the group than the amjad group in each ape group over, let's say, some reasonable period of time that you want to look at this. We've been looking at six month periods. Well, we're not seeing that. What we've done is we've looked at the Office of National Statistics stage on this, and we've kind of had problems with that. Because superficially, when this data was first being released, it did look like it did look like it apparently was showing that the unjabbed were dying at a much higher rate than the Jabbed. And it was showing in each age group as well. And when we looked at this, we actually found that there were flaws in the data. What you were seeing was unnatural data. What we were finding was that when you looked at the noncovid deaths, you were finding these massive spikes in mortality of those who were unjad at the very time that the vaccine rollout was happening for that age group. Now, why would the people who are not getting jabbed at the time when all the others are getting jabbed in that age group suddenly start dying of non COVID causes? Well, of course, there is an explanation for this. Well, there's a number of explanations for this. One that the ons tried to say was, well, actually, it's something to do with it. They call it the sort of the healthy vaccine effect, that it's only the healthy people who are getting vaccinated. So the unhealthy ones, the ones who are sort of about to die or close to death, we're not getting vaccinated, so they're sort of dying at that time when the rest are getting vaccinated. We showed both we showed empirically and theoretically this wasn't true, because we know, we know that empirically, that actually, people in even people close to death was up in care homes. They were actually people the highest risk people. Most critically, all people were prioritized for the job in those early in 2021. We know that. And even people who were in care homes and were even those close to death were given it because it was felt that to avoid a cause, kobe could be a horrible death. If you've got it to avoid that sort of horrible death, it's better to be jabbed. So that was nonsense, because theoretically, we showed when you looked at the data, it wasn't possible to get those peaks like that consistently in each of the age groups. And there was a much simpler explanation. And that simple explanation was that people who were dying shortly after vaccination were being classified as unvaccinated deaths. And that was it. That's the explanation. Now, this shouldn't be a surprise for various reasons. For a start, this whole it comes back to this definition of who is a COVID case? What is a vaccinated person? Right? So officially, for all of the studies on effectiveness of the vaccine, somebody who gets that gets COVID within 14 days of being vaccinated is officially classified as unvaccinated, because they say, well, the vaccine hasn't had a chance to take effect yet. Now, there is some you can sort of see some justifications for it, but then you need to make allowances for the way you do the analysis of effectiveness, and they don't properly do that to take account of this. But the fact that all these people in fact just imagine that you had a vaccine which was just a placebo which had no effect whatsoever on those who got it or didn't. If you do that type of and if there was some consistent rate of people getting some virus, then statistically, using that approach, that placebo will always be seen to be very highly effective because you're just not counting all the people. You're taking out all the people who've been vaccinated who get the Jab and moving them into the unvaccinated category for the first two weeks after vaccination. Now, when it comes to death and vaccination, officially, of course, if you die shortly after a vaccination, that should be it should be classified. You have had the Jab, right? But for exactly the same reasons. For the same reasons why they're classifying a person as unvaccinated if they get COVID within that period, if they get the virus in that period. Similarly they classify them as unvaccinated if they die. Now, the ons, what the ons say, and incidentally, this is routine, and we know that that is routine in many parts of the world. Now, to be fair, the Office of National Statistics, they say no. They say no, we do it from day to vaccination and date of death. So it doesn't matter if they died even just 12 hours after vaccination. That will be counted as that death and counts as a vaccinated death. Well, it's all very well time for them to start, but they get the data from various different bits of the NHS and various different bits of the recording system of NHS, and we know and we know that the mistakes are happening elsewhere. They don't get the correct data. If someone dies shortly after vaccination, their death is going to certainly be recorded. But is their vaccination date going to be recorded accurately? Almost certainly not. So that's where these kinds of problems come in. We've looked over the whole period when they've been releasing these reports and it's clear that in that first, in 2021 especially, this was a big, big problem of misclassification those deaths. If you take out if you adjust for that, if you make adjustments for that, then you find that there is no real difference in all calls mortality. In fact, we looked at the recent data. We don't trust any of it. It's so flawed. There are so many flaws in this data, not least because overall, the overall mortality rates are just wrong compared with historical data. When you take out when you just look at noncovered deaths and compare them with the previous periods of noncovered deaths, it's not right. They're lower in all age categories, which doesn't make sense, which suggests that they're missing we know they're missing a whole load of deaths. We know that they were missing a whole load of vaccinated deaths in their data. And so once you take account for this, not only is there not this drop in all cause mortality in the jab, but if anything, in certain age groups there might be an increase. We found that in some of the younger age groups, the allcause mortality actually may be higher in the jab than the unjab. So interesting enough, it's also in the 90 plus age group. But anyway but the thing is, the data isn't good enough to make any definitive conclusions, right? We should definitely have expected to see these drops in all cause mortality in the vaccinated, the unvaccinated, but we're definitely not seeing them. I'm not claiming that the jab is killing lots of people. We haven't got the evidence for that. What we have got is the evidence that they're not doing much in terms it's not really just doing anything in terms of stopping hospitalizations and death and that we know for certain. They're doing less than good. They're doing terrible in terms of COVID infections. Because you just have to look around you to know that anybody who says that there's data which claims that the vaccines are leading to decrease infection rates, we know that's not true. It's almost certainly almost all only, I mean, do you know of any person who was unvaccinated who had covered naturally and then has been continually getting COVID again? No, but we know that's happening with the Jab people, they're getting it over and over again, but it's not happening with the unvaccinated. So the jabs appear to be creating increased COVID cases and yeah, that's another issue.

[50:40] Leigh Brandon: So the data is suggesting that the efficacy just isn't there, but the data isn't suggesting that they're safe, but it's not also suggesting that they're unsafe.

[50:53] Professor Norman Fenton: It's a bit money. I mean, the problem is where there's a lot of stuff, of course, a lot of analysis has been done in sort of excess deaths. And there's a claim a lot of my colleagues indeed are working on looking at excess deaths as being a way of determining whether there is potential if there are significant excess deaths, what the cause? Is it possible that the vaccinations or it's possible long covenant and stuff like that. So that might be a way of seeing it. The fact that we are seeing excess deaths overall is worrying, of course, because if these excess deaths are happening after this mass vaccination period, then why is it how much of it is down to the lockdowns? How much is it to do with lack of access to at least less access to the NHS services and all that sort of stuff? There's all of that stuff in there, right. But you can look at there are other indicators. There are other indicators. When you just look at compare the different countries, the rates of excess deaths, or all cause mortality in different areas or different countries, different regions. And that, again, there what you see is, again, there's no evidence of a sort of a mass kill going on, but there is definitely no evidence of any reduction in deaths, which you should have expected if COVID was what it was supposed to be and if the vaccines were what they were supposed to be. So the whole thing points to this being, or something of a I wouldn't say a fuss over nothing, but certainly the world has been changed for the worse in many ways, for something that was maybe not necessary.

[52:51] Leigh Brandon: So my understanding is that all cause mortality did go up in 2020, as you'd expect, because it was a pandemic. But my understanding is that all cause mortality rate this year is even higher. But you'd expect if there's been a pandemic and lots of death, you naturally see a drop in on course mortality. Would that be a fair comment?

[53:15] Professor Norman Fenton: That's a fair comment. That's a fair comment. Incidentally, even the 2020 mortality rate was not that high anywhere. And I think that if you look back over the period of the last 20 years, it certainly wasn't the highest.

[53:31] Leigh Brandon: I remember looking at the stats in 2020, if you look back 30 years, it had the 15th highest level of mortality.

[53:39] Professor Norman Fenton: Yeah, exactly. It wasn't massive, but 2022 is looking to be far worse. And as you say, a lot of the very vulnerable people were kind of like taken out in 2000 and 22,021. Then you would expect, given that we now have this great vaccination mass vaccination program, to avoid this, to stop people dying from this virus, you would expect the numbers to have come down, not gone up.

[54:12] Leigh Brandon: Yeah. And the other thing I'm seeing as well is that it's the younger age groups that are tending to make up quite a lot of the excess mortality. Is that correct?

[54:23] Professor Norman Fenton: That is correct, yes. Now, of course, there are, again, the numbers, because in those age groups, the overall numbers of deaths are relatively small. Okay, so these are not like in the UK. They're not massive numbers. You'll tend to find that you don't get massively significant differences, at least not yet. So, again, we're still looking at I'm a bit more sceptical about the excess, about looking excess the way excess deaths are being looked at than others, let's say, who are on the skeptic side of this album, because I don't think we need to see some more data there, especially in the arm.

[55:07] Leigh Brandon: The other thing, again, correct me if I'm wrong, but a lot of the excess all cause mortality, a lot of it's heart related as well.

[55:17] Professor Norman Fenton: Yes.

[55:18] Leigh Brandon: And then we do know, I think it's fair to say we do know that these injections do cause myocarditis and pericarditis. It's almost like is it such a massive leap to suggest that that might be linked?

[55:33] Professor Norman Fenton: Well, I think the evidence is there. I mean, I try to avoid the sort of the clinical issues about explaining the link between these. Again, my esteemed clinical colleagues mostly have to be anonymous because otherwise they lose their jobs. Certainly convince me about that link. And the data suggests that there is that link, especially in young males, of course. And again, we're seeing plenty of anecdotal evidence of that in sort of young athletes especially, because that's, of course, a special problem when the heart's under stress, as in intense sports activities. And these are not things we've been seeing before that we didn't see these on anything like this on this scale before. Yeah.

[56:22] Leigh Brandon: I mean, I'm an avid sports fan and I can remember maybe three heart attacks or heart problems during a match. I'm talking over 50. Well, I probably been watching football maybe 45 years, and I can remember three. And then there was more than three in a month.

[56:42] Professor Norman Fenton: Yeah, exactly.

[56:45] Leigh Brandon: Over a period of time. So there's definitely.

[56:50] Professor Norman Fenton: Those are all general. They always quite say, you mustn't say anything about their vaccination status. I mean, it's interesting, isn't it? We were told we had to give our vaccination status to go to enter sort of a restaurant and stuff like that, but you're not allowed to ask a vaccination status if somebody has just sort of dropped dead on a football pitch. It's a bit weird. It's one of those weird idiosyncrasies which has become sort of commonplace in the sort of the COVID era. But, yeah, most of those we know that most of those, certainly professional sportsmen, would have had to have had the jab in order to go abroad and play in the tournament they were playing, et cetera.

[57:39] Leigh Brandon: Yeah. There were some Manchester City players that didn't go on the American tour this year. It was quite obvious why.

[57:47] Professor Norman Fenton: This is interesting. I didn't know that, actually.

[57:51] Leigh Brandon: That was interesting. So something you touched on earlier. I want to just ask you, kind of just to COVID it in a bit more detail. So I've heard a lot on fire media and politicians. It's kind of a throwaway comment. Well, this vaccine has saved millions of lives. Do you know where they go?

[58:12] Professor Norman Fenton: Yeah, the interesting thing about that is, again, some of the paper that was published on that, which claims to save worldwide over 20 million lives, that also came from people at Imperial College, not Ferguson himself, but again, similar to, again, based on these very, very similar models. Right? And they are assuming they were assuming, exactly as they were assuming these massive exponential increases in COVID cases without lockdowns, they were doing exactly the same kind of like assumptions about what these increases were going to be without the vaccine program, without taking any account of what the reality was in terms of the trends anyway. And again, it's making exactly the same mistakes that they made in their projections. I mean, you got to remember, they were projected. People say, oh, well, their projections weren't that bad. They were initially projecting half a million they were projecting half a million deaths in the UK about lockdown in the first six months, or something like that. Right. If it wasn't lockdown. So these similar types of completely ludicrous projections, without taking account of what was really happening in that case, they weren't even taking account of things like what proportion of the population would get infected and stuff like that and how many people would die. They were, of course, massively overestimated how many people would die if they didn't get infected. They're making the same mistakes, exactly the same mistakes on their estimating, their parameters in those models. They're completely they're completely meaningless. They're just garbage. And then it's ridiculous. It gives mathematical modeling a very, very bad name.

[01:00:05] Leigh Brandon: So it sounds to me like they've taken the original model and said, right, x number are likely to die. This is how many have exactly and.

[01:00:15] Professor Norman Fenton: Taken you got exactly. That's exactly what they've done, yeah.

[01:00:21] Leigh Brandon: And they've said that the vaccine is 100% of the reason why.

[01:00:24] Professor Norman Fenton: Exactly, yeah.

[01:00:26] Leigh Brandon: Crazy. So do you have any data on the safety of the Jabs with pregnancy and fertility still bursts, that kind of thing?

[01:00:37] Professor Norman Fenton: Yes, we do. There's a lot of controversy around this, and again, I can't go into too many details because it involves particular people promoting particular messages, but we've seen quite a lot of data which suggests that there is a problem, that there is an increased risk of poor outcomes for pregnant women who are vaccinated. But the problem is that a message has gone out, the the narrative has gone out of the exact opposite that now they're actually recommending that pregnant women are those who should be prioritized for the booster. For example, there's been quite a big government push on this and it's all to do again with misinterpretation of the statistical results that you get from this. So the claim which is being made by those people who've created this narrative that pregnant women need to get especially, is that their outcomes will be better in terms of them less likely to have stillbirth, for example, than the unvaccinated. Now, how does this come about? It's a simple misinterpretation. It's classic misinterpretation of data. It's called not taking account of what we call survivor bias. Most pregnancies, most termination of pregnancy happened very early in the pregnancy, not late on, right? And so if, for example, you get your vaccination shortly, sometime into your pregnancy, you've already survived, if you're only counting that you've already survived the most dangerous part of the pregnancy. So it's things like this, very simple things like this, this survivor bias not taken account of, which are leading to again, you can reverse you get these reverse results. You reverse these results as soon as you take account of survivor bias.

[01:02:47] Leigh Brandon: Just as an example there. So most problems happen in the first trimester and what you're saying is if someone is vaccinated in the second or the third trimester, it's not taking that survivor bias into account.

[01:03:02] Professor Norman Fenton: Exactly. If you don't take that into account, it will look like those who are vaccinated have more likely to have a successful outcome for pregnancy than those who aren't okay, because you've got rid of all those early terminated pregnancies which occurred there. So you've so that's an example. That's one of the main examples of the problems you get here. There are also other factors like socioeconomic factors in here that we know, for example, that there is a link, of course, between things like smoking and lower socioeconomic class in leading to higher silver outcomes. And we also know that in those people are less likely also to be vaccinated. So you've got a small amount of confounding there, but most of the confounding is in this survivor bias. Right. And once you take out that, the evidence suggests that, again, if you look at the number of we're seeing decreasing fertility, we're seeing decreasing number of birth. So again, it's all about when you look at the big picture, there is no evidence that if anything, the evidence is the other way that the vaccination isn't safe for pregnant women. Which is why I'm appalled at this, especially appalled at this push that the government is making for pregnant women to be prioritized for the booster.

[01:04:37] Leigh Brandon: Yeah.

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[01:08:38] Leigh Brandon: I was just going to say it's when they did studies into the nanoparticles that. Are used in these injections, the site that they tend to go to the most in a female. Surely they've got to affect fertility somehow.

[01:08:54] Professor Norman Fenton: Yeah. And of course, there's also something about the breast milk. Again, I'm not sure if I'm the best person to comment on that. Yeah.

[01:09:04] Leigh Brandon: Do you know what's happened to the AstraZeneca Jab? Yes.

[01:09:07] Professor Norman Fenton: So this is great, isn't it? So the AstraZeneca Jab, that was, of course, the great British invention. Of course, at the beginning, when they rolled out the vesting, most people were getting that rather than Pfizer Jab. So they rolled this out, the great fanfare, and the woman in charge of it got her knighthood and sort of stand ovation at Wimble and all that stuff, in June of July, whatever it was, of 2020. Well, the interesting thing was, even as early, of course, in fact, before July 2020, there were already known problems with that, which had led to it's been basically banned in seven countries in Europe. So there were already problems with it then. And in fact, what's interesting is that GPS in the UK, and I know this for a fact, were no longer getting no longer been provided with Gas and Zenika that summer. Summer. It was already from that's actually early summer of 2020. Right. They were suddenly only being provided with Pfizer. Now, I've spoken to quite a lot of GPS about this, and most of them, they're asking as well, we were just not provided with AstraZeneca anymore. Okay. But of course, there was no announcement. No announcement. This has been withdrawn. In fact, there still hasn't to this day, there has not been any formal announcements yet, to all intents and purposes, it was withdrawn in late summer of 2021. And think about this is quite an incredible thing they got around doing a formal it would have been very embarrassing to have a formal notification of withdrawal. My understanding is that they got around this by saying that, I think even now, certainly a few months ago, so about four months ago, when I last tested this, apparently, they said if you specifically asked for Astro Zenica, it could be provided. You specifically asked for the Enoch jam, it could be provided. And that gets around this idea that there's a withdrawal. So there's a sort of a de facto there was a de facto withdraw. That's basically what happened. And no talk about this has not been discussed. There's been no absolutely nothing about it. It just vanished from the UK narrative, our great success story, which perhaps wasn't such a great success story after all. And of course, most of the there's been only a very few number of confirmed deaths caused by vaccine. And in the UK, I believe that almost all of those were AstraZeneca.

[01:12:09] Leigh Brandon: Yeah. So big fanfare when it was launched and then swept under the carpets.

[01:12:16] Professor Norman Fenton: Just typical again, this is just typical of this. Craziness things that you couldn't have imagined being happening, sort of precovid something like this happening, where you basically invested this, you've got massive chunks of the population to take this injection, this great British success injection, and within months it's discovered actually there were serious problems with it. But let's just forget about it, forget that any of that happened.

[01:12:55] Leigh Brandon: The other thing I guess, that's important is that how could anyone have ever given informed consent?

[01:13:01] Professor Norman Fenton: Yeah, exactly. There was never informed. There was never informed consent because people were never told. We didn't know. Of course we don't know. We never knew the long term. There was no long-term safety data in early 2021. I mean, you know that even now. I mean, I recently got hold of the Slovenian contract with the Slovenian government contract with Pfizer, and it was in three, I think we had four stages of contracts. The initial one, which was, I think in something like November, December 2020 for the initial batch. Then there was another contract for January, and then one in April, and then one in November. I think we got November of 2021, so bear that mind. November 2021, when there certainly were already known, there were many, it was already a big, the safety was already a talking point about the FISA jam. And even in that 2021 contract, they've still got the words, they've still got the statement that Pfizer cannot provide any to worse the effect of there is no evidence of efficacy or safety. So they're not again, it's all about their liability. But it was stated, you can understand it being in there in November 2020, but the fact that we're still in there, the exactly the same wording in the contract in 2021, I have no evidence of efficacy of safety. That kind of tells you everything. So we haven't got, of course, the safety data from the trial, from the formal trial that we were talking about before, is, of course, abysmal. Right? I mean, in many respects, it's abyssal. On the one hand, if you just look at the 40,000 people who are actually part of the formal randomized controlled trial and who's actually died, the numbers aren't large because these were largely sort of healthy people. But we do know that it's something around 21. Last time I saw the figures, it's like 21 of the vaccinated have died, compared to about 15 or 16 of the unvaccinated. So again, it doesn't tell you much at all. It certainly doesn't give you evidence in favor of safety of the vaccine. But then you look at the work of people like Doshian. I mean, he was one of the co authors on a detailed paper which looked at all of the adverse, all of the serious adverse reactions from people on the actual FISA trial, not talking about synth, but were actually on the FISA trial itself and on the Mederna trial, and of course, the risk of serious adverse reaction. When you looked at all of those people in those trials was, of course, much higher in the vaccinated than the Unvaccinated. So any safety data there was from the randomized controlled trials doesn't really support the safety of the vaccine, then we know, I mean, I've spoken to people who run the FISA trial, who suffered serious adverse reactions and who were simply then not their condition. They were taken off. They were no longer counted as part of the trial.

[01:16:30] Leigh Brandon: So why do you think that the mainstream media fails to report any sort of alternative narrative?

[01:16:37] Professor Norman Fenton: Well, that's a very good question. I mean, right from early on, I always felt that there was a bigger there was something behind this. When I remember being on a podcast, so it was basically a panel, a live zoom panel with a number of other sort of experts from different disciplines. I think in early, maybe sort of June of 2020, maybe even before that, when the first lockdown was sort of sort of rigorous, and they were talking all the other people on the panel were talking about the great recess. I hadn't never heard that expression. I generally hadn't heard that expression until I was on this panel. And these others were talking about it in very, very positive terms. They were saying the same words that Clash Schwarber, the word economic forum said about it. This is the opportunity for a great reset. And they were saying that they were very positive about that. They were saying things like this the lockdowns massively improved air quality in London, and that we needed to keep this up because of the this would benefit, this would help us fight climate change. And that a lot of these people, of course, were academics who this is a joke, isn't it? Academics. They were the ones who were pushing for hardest and the most harsh lockdowns, and they absolutely were the ones who were least affected by the negative aspects of lockdowns. They still expected their deliveries to come to their door from people who were expected to work and come into contact advice, which they didn't have to do by being sitting there with their laptops, their gardens and nice sunny weather and all that sort of stuff. Not just academics, but of course, the politicians as well. But that's an elite class of people were the ones who were most pushing the harshest lockdowns, had the least to lose from it. They didn't lose any salary, these people, and stuff like that. So you've got all that. But they were talking about it. They were talking in a very positive terms that this was a great opportunity. And I think that that's always been I think that's this opportunity to reshape the world greater control, move towards we're seeing all of the I mean, I said at the time after that, but it seems to me that the COVID lockdowns were a precursor for the client lockdowns, because that's what all these people are asking for. They were saying, this is how it needs to be. We need to have more people working from home. We need to have less people traveling, cut down on all types of travel. It was just leading to what was effectively that WF 2030 agenda kind of like stuff. And I think that the vaccination program was always now I understand it was all about getting necessary control to move towards this sort of digital ideas and eventually international digital currency, that type of thing. Having it as a precursor, having it as a necessary requirement to travel. For example, you've got to have your proof of your vaccine status to travel and having that on a digital device on your iPhone or whatever on your mobile phone. And that then gets people kind of like into the state of mind whereby it's moving towards the the of sort Chinese digital control system there. It just seems to me that it's all about that inevitable movement. And I think that that has been true. I can't think of any other reason why there has been. That refusal to anybody challenging the coded narrative is a threat to that longer term, that longer term narrative of kind of like digital control.

[01:20:46] Leigh Brandon: It's interesting that, as you say, that today G 20 that's going on at.

[01:20:52] Professor Norman Fenton: The cloud shelves wearing outfits.

[01:20:57] Leigh Brandon: Interesting. So, final question for you, Norman. How did your work on COVID and COVID data affect your career negatively?

[01:21:09] Professor Norman Fenton: And it's interesting I changed because at the beginning, we were okay, we were getting stuff. I've got over 400 published papers. I've done seven or eight books, and I'm at the stage where close to retirement, in fact, I'm actually retiring very shortly now anyway. So I felt that maybe I could speak out more about this than others. But actually, at the beginning of the pandemic, I wasn't speaking out against the narrative at all. I was simply looking at the data with the data. And at the time, we were getting stuff published in peer reviewed papers because it wasn't especially challenging, the narrative, that stuff. Actually, when we first I mentioned the paper about where the covered infection rates were higher than being reported, but the infection fatality rates were lower. One of our conclusions in that paper was there needed to be much greater random testing. And of course, at that point there wasn't. But of course, that was what was implemented very shortly afterwards. I'm not saying it was as a result of what we were recommending. Of course, I didn't know at the time how inaccurate the PCR testing was, and I regret ever calling for more random I think it was one of the most serious mistakes ever made, was calling for more random testing at that point. At that point, it wasn't challenging the stuff we were doing, and also stuff on the as you mentioned before, the risk factors. That stuff wasn't challenging in any way. The narrative they saw that as being supportive of the narrative. But as soon as I started to note the problems with the inaccuracy of the testing, the fact that that was ramping up the number of cases in a way which was not representative of the true state of the virus, as soon as I pointed that out and had the audacity to simply say, hang on a SEC. If you can look at the increase in case numbers we've got allstate count increasing testing numbers and just pointing that out, suddenly I became a covered denier spread of misinformation almost overnight. The work we did on this is that the very serious work we did on exposing the problems with the proportion of the people of Asymptomatics who are generally false positives. So we did that work that was very intensive research, very, you know, comprehensive piece of research. That was when suddenly that paper was getting rejected without review. It was just saying we couldn't even get it on preprint servers which is supposed to take anything which is not plagiarized or out scope. We couldn't get our work, we couldn't publish our work anywhere. There was only sort of one thing there's a cycle of research grade where we were the only place we were able to put our stuff and also put it out on the blog, my work and that people in my group was simply being censored. That's what was happening because it wasn't even getting reviewed. This was sort of counter narrative and I got those abusive attacks. People were sort of calling for me to be sacked from Queen Mary and lots of abuse on Twitter and all of that stuff. I was getting I had invitations to speak which were being canceled, getting the full weight of the censorship and that hostility and it's very tiring. I mean, to be fair, at the same time, people who I respect.

[01:24:46] Professor Norman Fenton: Was getting very positive feedback from them. So it wasn't stopping me from doing it. I mean, I felt that obviously everybody feels that they're on the right side of history, but I did feel that the attacks were coming from people who weren't well informed or had their own agenda. And the problem is that I was able to carry on through that simply because I guess I always felt that I had the option to retire sooner rather than later and had I been, let's say, 1015 years younger, it would have been much more difficult. And that is a problem. I think that is part of that's why that's why the narrative despite I mean, of course there's people who've made a much bigger impact in the counter narrative than I have. But nevertheless, the overall impact of those people, including myself has been relatively small because I mean, let's face it, as we've been discussing, a lot of the things we've discussed today are known within kind of like people who are watching the sort of alternative who have access to alternative sources of information. But those who rely only on the mainstream, whether it be BBC News, Sky News, or the mainstream newspapers, will be completely oblivious to almost everything that we've discussed today. They will simply not have a clue about it. They will not be aware. If you mention some of the problems about the if you mention about the AstraZeneca problem with that, people will be completely unaware of that. They won't even know that it's not available. They won't know about the issues of safety. They certainly won't know about things like the great if you mention the great reset to sort of most normal, as it were, and it's not intelligent people don't say, well, that's obviously a conspiracy theory. If you mention that they don't never heard of a guy called Claustro, they've never heard that. They will absolutely deny that this is possible. It's completely whatever we've done, we still haven't broken through that. We've got to leave. Maybe, say, 15, almost 20% of the population, but 80% simply have not been simply not aware of any of this stuff. That's a problem.

[01:27:15] Leigh Brandon: Hence, we're doing this. Norman, do you want to quickly talk about your Wikipedia page?

[01:27:19] Professor Norman Fenton: Yeah, well, this is just an example of the kind of nonsense that's put up with. So I had a Wikipedia page up there. It's not something that people can't, I don't think, putting up their own Wikipedia page, but people put it up. People put up Wikipedia page. And there's one of me which has been there for a few years, hasn't been changed. Actually, it needed to I was hoping someone might update it with some new stuff. It's not really anything I thought about, but it was up there. Right, but the first time I was aware that there was a problem with it was that it was a week before I was due to give evidence in a case in Indonesia brought against the government for their vaccine mandates. So that was being challenged. That was actually a case against the president of Indonesia and the head of their health service. So this case being brought by some people over there, and I agreed to give expert evidence on both the data on the seriousness of code and the vaccine safety. So a week before I was due to give that one of the people on the one of the lawyers who were making the case, the plaintiffs alerted me to the fact that my Wikipedia page now had a big section on it saying coded misinformation and cited a it said that I had called for the government, the British government, in early 2021, to stop the vaccination program because it was killing people, more or less. It was more or less more or less what I said. Now it turns out that actually that was a complete lie. I never made any claim like that whatsoever. Right. All it was was that it was based on a they'd been a Sunday Times article which had been published, which had misinterpreted a report that was published by the Heart group, which I'm one of many members I'm a name as a member of the Heart group. And there was one article out of, I think about 15 in a set of articles in a Heart report which had raised this early issues, early issues, sorry, had raised this issue about the spike in mortality after the first wave of the vaccines. Now, that paper which was in that report, I wasn't an author or co author of any of the papers in that report. My neighbor simply listed as a member of Heart on the inside cover. Right? But even that paper didn't actually make the claim that was, that the Sunday Times said was being claimed. But nevertheless, then because I was made, there were sort of three, I think, prominent professors in Hart, they picked out the three prominent professors in the Sunday Times report and said these professors could Professor North end of Queen Mary have said this? When we of course didn't. So it was based on a misinterpretation and a completely flawed Sunday Times report, which actually got the headline of which and some of the details online did get changed afterwards. And that was the basis for this Wikipedia entry. And the Wikipedia entry only linked to of the Sunday Times for it didn't link to anything else. And of course what happened was that what happened was that I basically got in contact with somebody who did editing of Wikipedia pages and got him to point out that I was denying this. Okay. And this is the whole reason to cut long story short, it turned out that every time anybody, of course I put this out on my blog, so other people started to chip in on the editing as well. And every time somebody tried to change that correct, that was misinformation about me being supplied misinformation, one of these sort of super editors just reversed. He just took it all out. He just had the power to simply delete everything that they didn't like. And it turns out we found out that there's one guy who's responsible for all of these ridiculous Wikipedia entries for lots of people. You look at people like Robert Malone, for example, Peter McCullough and all of them, it's got a similar thing. It's got this thing about COVID misinformation and that dominates the entire Wikipedia page. All the stuff they've done before is considered irrelevant. They're labelled as purveyors of COVID misinformation above all. Now, fortunately, actually, unlike the others I appear to be one of, because of the fuss I made about this and because I spoke about it on podcast with Brett Weinstein, it got quite a lot of publicity. Eventually, eventually that whole misinformation code misinformation thing on my entry was actually removed. So it was a bit of a success story in the end.

[01:32:22] Leigh Brandon: So Norman, where can the audience find you online?

[01:32:25] Professor Norman Fenton: Yes, I've got a website which is just normanfensen.com and on Twitter at prophn. Fenton excellent.

[01:32:33] Leigh Brandon: What I'll do is I'll put all your details in the show notes so people can contact you. And by the way, I would highly recommend people check out the links on your website to other interviews you've done and also to your YouTube channel, which is absolutely excellent.

[01:32:49] Professor Norman Fenton: Thanks.

[01:32:50] Leigh Brandon: Norman, thank you so much for taking your time out today. I know it's been a tough day for you as well, but really big thanks for sharing your wisdom with the Radical Health Rebel listeners and viewers.

[01:33:02] Professor Norman Fenton: It's been a pleasure.

[01:33:02] Leigh Brandon: And to all the Radical Health Rebel tribe, if you know someone who would benefit from watching or hearing this episode, and I would suggest everyone you know needs to watch or listen to this episode, please make sure to share the love and forward it on to them. After all, the mission of this show is to help people lead a more funfilled, healthy, productive, fulfilling and happy life. And if you'd like to support the podcast, you can@patreon.com, where you can also receive lots of other exclusive premium content, including unedited fulllength ad, free videos, ask me anything, Q and A sessions, and even free Radical Health Rebel merchandise. So that's all from Norman and me for this week, but don't forget, you can join me same time, same place, next week on the Radical Health Rebel podcast.

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