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April 13, 2022 - RFK Jr. The Defender

26:56

Risk Info Management with Dr. Norman Fenton

Dr. Norman Fenton discusses risk information management with RFK Jr.

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	Hey everybody, our guest today is Dr.
	Norman Fenton who is a professor of risk information management at Queen Mary University of London.
	He's a mathematician by training with a current focus on critical decision making and in particular on quantifying uncertainty.
	Using causal probabilistic models that combine data and knowledge, and that system is known as the Bayesian networks.
	This approach can be summarized as smart data rather than big data.
	It has applications in law and forensic.
	He's been an expert witness in major criminal and civil cases, health.
	Security, software reliability, transport safety and reliability, and finance and support protection.
	His studies are available at NormanFenton.com.
	And I wanted to have him because he's done a lot of work on the data that is applicable to the COVID virus.
	And he has been very, very critical from an academic and stoic viewpoint of the government policies and the use of data, which is something that has been a preoccupation, I think, of all of us who've been following the countermeasures.
	So, Dr.
	Fenton, your group at Queen Mary University of London has applied itself to a range of challenges.
	When the COVID pandemic emerged, what kind of challenges did you specifically see?
	Well, first of all, I mean, it was clear, I think, from the start that most of the data that governments put out, not just the UK government, but most governments around the world, about COVID and then later about the vaccines was kind of misleading because it was based on very easily manipulated statistics.
	So initially, we saw there was an immediate rush to draw conclusions which were sort of based on oversimplistic data on case numbers and deaths.
	When instead there, we believe there was a need to look for causal explanations in the observed data, because that's the kind of work that we do.
	So the problem was that that data was very easily used by influencers and decision makers to fit particular narratives that exaggerated the scale of the crisis.
	So for example, People were concluding early on that neighbouring countries in Europe with lower COVID death rates must have more effective restrictions and better prepared hospitals.
	But actually it was just simple differences in how deaths were reported that had far more impact.
	So for example in the UK Anybody dying within 28 days of a positive PCR test was classified as a COVID death, irrespective of the cause of death.
	Whereas, say, for example, in Germany, they required a clinical cause of death.
	And the other thing that was revealing and really driving the narrative early on, I'm talking about when we first looked at the data, sort of March, April, was that At that time, the only people being tested for COVID and being confirmed as cases were those who were already hospitalised, or more rarely the sort of frontline medical staff.
	Now, what this did is that that meant that it was missing people who had mild or no symptoms, but it was massively underestimating the infection rate, i.e.
	how many people had the virus, and massively exaggerating the fatality rate, i.e.
	the proportion of people with the virus who actually were dying from it.
	Ours was amongst the first research published, which provided what turned out to be much more accurate estimates of the infection rate and the fatality rate.
	This already showed that the virus was more widespread than people assumed, but nowhere near as dangerous as was being claimed.
	Now, the other major challenge was that it was clear early on that proper understanding of the virus As far as risk and rational decision making was concerned, was that it critically depended on having accurate diagnostic tests for the virus.
	And we were initially told, we were led to believe that the PCR test was an accurate diagnostic test.
	But later, of course, we discovered that wasn't true.
	And the impact of that has been catastrophic.
	You've done over 20 studies, and a lot of them have focused on misclassification in the data, beginning with the initial modeling, which was done by Neil Ferguson, and what I would characterize as kind of a group of grifters who gave us just catastrophically inflated modeling projections for death and fatalities and infection rates.
	And then they followed up, as you say, the regulatory agencies by amplifying the, by using the PCR test, high amplifications, which created this kind of, this artificial epidemic.
	And I'm not saying the epidemic was artificial, but the magnitude of it was enormously and deceptively amplified by the misuse of the PCR test.
	And then the next thing they did, which was the classification of deaths, they also deliberately inflated by declaring that any death Whether it was a positive PCR test within the last 60 days would be classified as a COVID death.
	Even in our country, the coroners were given instructions to change the way that they filled out the death certificates.
	Yeah, I mean, this was a major problem, as you say.
	I mean, it's not just that anybody with the positive PCR test, you know, was classified as a COVID death, irrespective of the actual cause of death.
	You also had, remember, this was affecting even sort of the hospitalisations.
	And of course, that was supposedly the thing that was driving the argument, you know, for lockdowns.
	You know, we've got to sort of manage the number of cases in hospital.
	But again, in the UK, they were defining...
	Anybody who tested positive within 14 days before being admitted to a hospital, and anybody who tested positive while they were in hospital, again, irrespective of the reasons for them being admitted, they were all classified as COVID hospitalizations.
	I know I'm pretty sure the same thing happened in the States as well.
	But the key thing is, again, it comes back to this thing about the COVID or everything, everything, not just the issue with those classifications, but the fact that at the root of it, it also assumes, I mean, those are bad enough anyway, even if the PCR test was accurate, You know, you're defying the death from COVID even if the death wasn't caused by COVID just because they happen to have COVID and died from something else.
	In fact, we know that for most asymptomatic people, a positive PCR test, which is counted as a COVID case and hence a COVID hospitalisation, COVID death, if that happens to be what happens to those people, We're not actual COVID cases, right?
	I mean, the problem really struck.
	And this is where I started to really challenge the narrative.
	And when I kind of started, I guess, to come under attack, because I was really challenging earlier, we were simply doing what were considered to be mainstream analyses, which were not really challenging what the government was saying.
	But the problem came with the mass testing of asymptomatic people Around late summer, early autumn of 2020.
	That was when we were finding the real problems about the false positives, the scale of the false positives.
	People don't really understand how bad that is for asymptomatics, because they see something like a low...
	They say, ah, well, the false positive rate is actually quite low, less than half a percent, and therefore they think that somebody testing positive It's very likely that they really do have the virus, but no.
	Funnily enough, the Bayesian analysis comes in here because actually it's not the rate of the false positives which is driving how many false positives you actually get, but it's the prior rate, how many asymptomatics What's the rate of true virus amongst the asymptomatics?
	And that's incredibly low.
	And so even if you've got a low false positive rate, if somebody tests positive when they're asymptomatic, we know, and we've actually got the data, we've seen it with real studies, most of those people, over 80%, will not actually have the virus and will never go on to develop symptoms.
	Right?
	So the key thing is, most people are asymptomatic, testing positive, don't have the virus.
	And yet, you had this exponential increase in the number of people being tested, and a lot of those asymptomatics they were testing started to routinely test all school kids.
	People going back to work after the initial lockdowns.
	And you've got this exponential increase in cases is inevitable simply because you've exponentially increased the number of the amount of testing.
	Again, it's a kind of a statistical artifact.
	But when you looked at real indicators, and of course that was all driving, that drove the second lockdown.
	You know, that sort of thing.
	And you had absolutely ridiculous decisions.
	This was all driving these decisions.
	And indeed, that massive increase led to the decisions on the second big lockdown in the UK. And yet, when you look to other, funnily enough, other government indicators, for example, in the UK, there's a National Health Service dashboard, which actually shows The number of 999 triage calls specifically for COVID. And when you look at that, that's not case numbers, that's genuine 999 calls with COVID triages.
	What you actually saw...
	What is a 999?
	Is that an ambulance?
	Oh, that's an ambulance.
	It's either an ambulance...
	I mean, an ambulance pickup or a call to the ambulance, sorry.
	And what you actually saw, if you look at it, you can still see it now, you see this real peak when you had the original peak in March of 2020.
	Massive peak.
	Thereafter, you don't see much at all.
	You see the sort of traditional, the normal seasonal peaks for, you know, for the normal sort of seasonal viruses.
	Really, there was, again, a little bit of a peak, nothing like the original peak in January 2021.
	But it's just, there's not much there.
	There really is not much there.
	And then we had, I want to say a word about this, and I don't know whether you studied this at all or considered it.
	There was also, in the United States, We had these enormous and really grotesque financial incentives for hospitals to classify anybody who came in for any reason as COVID, where they would literally make tens, even hundreds of thousands of dollars by misclassifying them.
	And we don't like to think of our hospitals as doing something corrupt, but the temptation to do that was so enormous.
	To classify, for example, an automobile death, An accident death, a motorcycle, or a drowning as a COVID death because the amount of money that would come to your system was so disproportional.
	And did you look at any of that?
	Yeah, I looked at that.
	And I know that was...
	I'm aware of that happening in the States.
	Now, it's not clear whether there were similar financial incentives in the UK. But my view is they didn't need them in order for this massive...
	Exaggerating these numbers, the very definitions.
	They were just forced.
	We don't know about, I'm not sure about financial incentives.
	They were forced by definition to classify these people as COVID deaths.
	Everything driven by the faulty PCR test.
	But the most...
	The CDC in our country has made the extraordinary admission.
	And 94% of the reported classified COVID deaths were among people who had at least 3.8.
	Yeah.
	Potentially fatal comorbidities like cancer, diabetes, heart disease, et cetera, that may have, in fact, killed them.
	So it really makes this kind of amorphous...
	Yeah, and that's the same.
	That's definitely the same, because that figure, that I said, it was actually, yeah, over 95% had at least, most of them, most of them had at least two or three comorbidities, right?
	I think I also saw a statistic, which was over 50% had at least four comorbidities, right?
	The thing that's really concerned me about the whole, because we're into this, you know, we're all about risk assessment and risk benefits, especially when it came on to, of course, the vaccines, is the situation for children under 18.
	So I can give you a very interesting piece of data about this, again, from the UK, because this was a study of all of the child vaccines.
	Hospital admissions.
	By children, I mean under 18.
	So it's not, you know, this is under 18s, right?
	The number of hospitalizations in the whole of 2020, because there was a detailed clinical analysis where they went through each individual clinical case.
	They actually read the case notes, right?
	So in the whole of 2020, there were 5,830 hospitalizations of children under the age of 18 with COVID, classified as COVID, which incidentally was fewer than the previous year with influenza.
	So that's an interesting thing to know.
	And last year, they finally make this extraordinary concession that vaccines don't prevent infection and they don't prevent transmission.
	But they continue to say, without any kind of citation or scientific evidence, that the risk of the vaccines is worth it because the benefits are so enormous.
	And none of that is explained statistically or with any kind of data whatsoever.
	So you did the first task.
	That every statistician and every medical mathematician or scientist ought to do the gold standard of assessing a medical intervention, which is to look for the key metric all-cause mortality and tell us what happened.
	So the key thing is, I mean, just to clarify...
	Just so people know, what all-cause mortality is, is you look at the intervention, which in this case would be vaccines, and you compare a group that got the vaccines with a group that is similarly situated that did not receive the vaccine.
	And then you look down the road at how many of them are alive after a certain time period, how many of them died from all causes during the following time period, and for intervention to get an FDA license in this country.
	It's supposed to show your chance of survival following that intervention are greater if you get the intervention than the placebo group.
	And with the vaccines, they have not been able to show that.
	Indeed, even in the Pfizer randomized controlled trial, the small number, there was a slightly higher number in the vaccine arm than the placebo arm who died in the first six months.
	So even in the randomized controlled trial, you didn't have the all-cause mortality evidence in favor of the vaccine.
	But what we've done is, of course, we've looked at all of the available observational data on this.
	And to be fair, in the UK, the Office for National Statistics Unlike actually many countries, it started last September to give what superficially seemed to be very good data to enable us to look at this all-cause mortality comparison, the vaccinated and the unvaccinated.
	And the problem was, in the first report that they produced, It wasn't properly age-categorised.
	In fact, it wasn't age-categorised at all.
	And people were actually making, even people who were sceptical, people who were sceptical about the vaccination were making incorrect conclusions, which didn't help the argument, because they were looking at the whole data and saying, ah, look, on the base of all the data, if you just compare all of the unvaccinated with the vaccinated, there's a much higher mortality rate in the vaccinated or caused in the unvaccinated.
	When you're grouping all of them together, that's kind of inevitable because you've got the age confounder.
	Most of that time, obviously, most of the people who die are generally in the older age categories.
	And at that time, most of the people, the much higher proportion of vaccinated was in the elderly.
	So, you know, you really have to go drill down into the different age categories.
	And after we put in some Freedom of Information requests, and also we spoke directly with these people, they eventually gave us some age categorised data.
	They gave us, it was categorised as 80 +, 70-79, 60-69, and then this big one of 10-59, which was basically useless, because in that one, again, you saw the same thing.
	You had a much higher mortality rate in the vaccinated, but we don't know whether that's due to the age confounding.
	But with these older age categories, we seem to have all the data we needed, right?
	So this was the data we really studied, and we found some really, really strange things with this data.
	So we plotted the weekly mortality rates over the whole of, well, for all of the time period we had, which eventually we got for the whole of 2021.
	But at that point, it was up to September, whatever.
	And we found obvious anomalies in each of the age groups, because what you saw was a pattern whereby superficially it looked like the unvaccinated had a much higher mortality rate than the vaccinated.
	But it wasn't consistent, right?
	What was happening was that you saw these weird peaks In very, very high peaks in mortality of the unvaccinated, which happened to coincide with the rollout peak of the vaccine for that age group.
	And what more?
	When we looked at it for non-COVID mortality, you saw exactly the same thing.
	So what was happening is something which looks like a statistical anomaly, which is that how is it possible that the unvaccinated are somehow suddenly dying of non-COVID causes just at the time when the vaccine rollout reaches its peak for that age group.
	And what's more, these peaks are different for each age group because the rollout was different for each age group, right?
	In each age group, you're seeing this weird peak at exactly the time when the vaccine rolled out and it's non-COVID deaths.
	Well, it turns out that is an absolutely, if you can absolutely repeat that, you can construct that statistical anomaly simply by a simple misclassification of misclassifying those who die shortly after vaccination, putting them, classifying them as unvaccinated.
	Because in our country, you are not classified as...
	As vaccinated until two weeks after the second vaccine.
	Exactly.
	And most of these anomalies where immediately after the vaccine, there's these huge spikes in deaths.
	But those among vaccinated people, recently vaccinated, but those people, those deaths are all classified as unvaccinated because you're not classified as vaccinated until two weeks after you've received your second vaccine.
	Exactly.
	So you can construct that.
	So you know when you see that, that there's misclassification, right, because it's happening in each age category.
	Now, we know, same as in the UK, people who are vaccinated within 14 days, they're not counted as vaccinated for the purpose of all of the studies where you're doing vaccine effectiveness.
	However, the Office for National Statistics, they argued, they told us, But actually, they weren't doing this misclassification.
	They were claiming if a person, even if a person died within 24 hours of a vaccination, that person was classified as a vaccinated death, right?
	However, we have evidence that this was not the case.
	We've actually...
	The thing was, it's quite an amusing thing because they kept changing their minds about this because they said the reason for these weird peaks then is not because of misclassification, It's because of what they call the healthy vaccinee effect, that somehow people who were very, very ill and should have been vaccinated didn't get the vaccination because they were going to die anyway.
	Now in the UK, that simply did not happen.
	We've got anecdotal evidence that didn't happen.
	It shouldn't have happened because those people who actually had the most critical illnesses were the ones who were prioritised.
	So those should have been the ones who were vaccinated early, right?
	We've also got data on seriously ill people, which shows that those spikes couldn't even occur if this healthy vaccine effect was true.
	And even if it was, even if it was...
	What it would mean, all of their conclusions about vaccine safety and effectiveness would be fundamentally biased because they hadn't adjusted for this so-called healthy vaccine effect.
	So the data was just so flawed.
	And we've also, even since, having them having denied, first of all, that this vaccine You know, they were misclassifying in this way.
	Bear in mind, all of the claims in the UK about the effectiveness, the safety of the vaccine, you know, the narrative, repeated ad nauseum in the UK is, ah, but the vaccine is the only thing that stops you from being seriously ill and dying, a serious hospitalisation of death from COVID. And yet, all of that data is based, all of those conclusions...
	And all of those analysis are based on this data which we've shown to be flawed.
	In fact, once you do the adjustments to take account of the misclassification, what we believe is happening is indeed what you what you said yourself just before, that there actually is a peak in the vaccinated mortality shortly after vaccination and not the other way around. that there actually is a peak in the vaccinated mortality
	Now, of course, it could well be that these people are sort of because that you're vaccinated in priority of the most critically in need, these are people who are indeed, you know, immunosuppressed seriously also might just be the vaccination might just be bringing forward, you know, the death which would have occurred shortly afterwards anyway. you know, the death which would have occurred shortly afterwards But nevertheless, you know, that's what we believe.
	That's what we believe is there in the data, but is, of course, being hidden.
	One of the things we're seeing in the United States recently is a state, a state of newspaper articles with doctors, local hospitals who are saying all the people who are in my COVID ICU ward who are dying now.
	90% of them are unvaccinated.
	And there's article after article.
	And of course, this is the final readout of the vaccine orthodoxy because they've had to retreat from their initial claim that it would prevent infection.
	Their secondary claim it will prevent transmission.
	We now know none of those are true.
	Everybody admits it.
	Oh, now what they're saying is, well, it will prevent you from going to the hospital and die.
	It'll reduce your chance at that.
	So that's what they're saying.
	But it seems that what's really happening, if you look at the data from Israel, if you look at the data we now have from New York State, if you look at the data from the UK that you've developed and teased out, it's clear that Vaccinated people have at least as good a chance of ending up hospitalized or dead as unvaccinated.
	And it appears what's happening in all of these anecdotal reports from local hospitals and doctors, none of them are peer reviewed.
	None of them are published.
	And it appears what's happening is that CDC has told hospitals that when somebody checks in the hospital, if they're not two weeks beyond their second vaccine, they classify them as unvaccinated.
	And if they do not tell their vaccination status, the default is to classify everybody as unvaccinated.
	So if you have somebody who comes in who's unconscious or incoherent or not able to fill out a form because they're in bad shape, which is true for many people who come into the hospital COVID wards, the doctors who are on those wards, the ward itself is a locked ward.
	The family can't go on the ward.
	The doctors have no access to contextual information from families and friends, etc.
	So they're reading the chart, and the chart says unvaccinated.
	So when they walk down the ward...
	And they look at all of the beds and all the charts.
	They're thinking that all these patients are unvaccinated.
	It's striking.
	But if you don't understand the data collection system and all of these artifacts from essentially this falsification of data, you will get the impression that it's only unvaccinated people dying in hospitals.
	Yeah, absolutely.
	I mean, the UK, they've made these ridiculous claims.
	I got a text on my phone saying that it said over 8 out of 10 people currently hospitalised with COVID are not vaccinated.
	But it turned out, and we challenged them on this, they said, yeah, it actually means not fully vaccinated, which at the moment means you've got to be at least two weeks after your booster jab.
	Anyone else is classified as unvaccinated.
	You know, right there, two miles from where I live, there's a...
	A giant billboard on the 405, which is a major thoroughfare going through Los Angeles, and that billboard says if you're unvaccinated, you're 16 times more likely to die.
	It's just a lie.
	Yep, that's exactly what they're repeating.
	That's exactly the type of message that's being repeated ad nauseum.
	I mean, you know, we've seen that sort of messaging that if It's been going on throughout, you know, not just with the vaccination, but with the infection rates, you know, one in three people, they kept saying, message about one in three people who have COVID had no symptoms.
	And I mean, it's just, you know, all the time.
	So, you know, the whole messaging exaggerating the statistics, it's just been a continual theme.
	Dr.
	Norman Fenton, thank you for joining us today.
	For those who are interested in viewing details of Dr.
	Fenton's findings and the publications we have referenced today, you can see them on NormanFenton.com.
	Thank you so much for joining us.

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