In this episode, Dr. Norman Fenton talks about his research on the co-infection pandemic, COVID, and how it was misclassified, misdiagnosed, misclassified, mismanaged, and misused by the government. He also discusses the role of data in shaping the narrative of the crisis, and the problems faced by decision-makers and influencers in the response to the crisis. Dr. Fenton is a Professor of Risk Information Management at the Queen Mary University of London, and a mathematician by training. He's an expert witness in major criminal and civil cases, and has worked in areas such as software reliability, software safety and reliability, and finance and support protection. His studies are available at NormanFenton.net, and his studies can be found at his website, which is a great resource for anyone interested in critical decision making and critical data. Dr Fenton has done a lot of work on the data that is applicable to the COVID virus, and he has been critical from an academic and stoic viewpoint of the government policies and the use of data, which has been a preoccupation of his work in recent years. This approach can be summarized as smart data, rather than big data. It has applications in law and forensic and forensic cases and forensic work, which can be applied in law, and forensic practice, and is a focus on quantifying uncertainty using a system known as the Bayesian network, or "smart data" (a system that can be summarising uncertainty using causal probabilistic models. (a mathematical framework). in the field of causal probabilities. He's work can be seen as a tool for critical decision-making, and evidence-based decision making. and forensic evidence, which he uses in criminal and forensic law and criminal law. His work can also be found in his work. at his blog, and in his book, . Dr. , which is available at his studies, , and his blog is available on his website and blog, which and blog posts, which are also available at the website, and blog post . His blog post, which you can find his blog post on the blog post and book which is on the book, which I wrote in this podcast, The Case of the Co-infectious Viral Virus: A Pandemic in the 21st Century, and blog post by .
00:00:02.000Norman Fenton who is a professor of risk information management at Queen Mary University of London.
00:00:08.000He's a mathematician by training with a current focus on critical decision making and in particular on quantifying uncertainty.
00:00:17.000Using causal probabilistic models that combine data and knowledge, and that system is known as the Bayesian networks.
00:00:26.000This approach can be summarized as smart data rather than big data.
00:00:30.000It has applications in law and forensic.
00:00:33.000He's been an expert witness in major criminal and civil cases, health.
00:00:37.000Security, software reliability, transport safety and reliability, and finance and support protection.
00:00:45.000His studies are available at NormanFenton.com.
00:00:49.000And I wanted to have him because he's done a lot of work on the data that is applicable to the COVID virus.
00:00:57.000And 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.
00:01:15.000Fenton, your group at Queen Mary University of London has applied itself to a range of challenges.
00:01:21.000When the COVID pandemic emerged, what kind of challenges did you specifically see?
00:01:28.000Well, 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.
00:01:45.000So initially, we saw there was an immediate rush to draw conclusions which were sort of based on oversimplistic data on case numbers and deaths.
00:01:55.000When 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.
00:02:02.000So 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.
00:02:12.000So 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.
00:02:23.000But actually it was just simple differences in how deaths were reported that had far more impact.
00:02:29.000So 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.
00:02:39.000Whereas, say, for example, in Germany, they required a clinical cause of death.
00:02:44.000And 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.
00:03:06.000Now, 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.
00:03:15.000how many people had the virus, and massively exaggerating the fatality rate, i.e.
00:03:20.000the proportion of people with the virus who actually were dying from it.
00:03:24.000Ours 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.
00:03:34.000This already showed that the virus was more widespread than people assumed, but nowhere near as dangerous as was being claimed.
00:03:42.000Now, 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.
00:03:58.000And we were initially told, we were led to believe that the PCR test was an accurate diagnostic test.
00:04:04.000But later, of course, we discovered that wasn't true.
00:04:07.000And the impact of that has been catastrophic.
00:04:09.000You'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.
00:04:33.000And 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.
00:04:49.000And 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.
00:05:00.000And 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.
00:05:20.000Even in our country, the coroners were given instructions to change the way that they filled out the death certificates.
00:05:30.000Yeah, I mean, this was a major problem, as you say.
00:05:33.000I 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.
00:05:42.000You also had, remember, this was affecting even sort of the hospitalisations.
00:05:46.000And of course, that was supposedly the thing that was driving the argument, you know, for lockdowns.
00:05:51.000You know, we've got to sort of manage the number of cases in hospital.
00:05:55.000But again, in the UK, they were defining...
00:05:58.000Anybody 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.
00:06:15.000I know I'm pretty sure the same thing happened in the States as well.
00:06:18.000But 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.
00:06:43.000In 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?
00:07:01.000And this is where I started to really challenge the narrative.
00:07:04.000And 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.
00:07:17.000But the problem came with the mass testing of asymptomatic people Around late summer, early autumn of 2020.
00:07:26.000That was when we were finding the real problems about the false positives, the scale of the false positives.
00:07:32.000People don't really understand how bad that is for asymptomatics, because they see something like a low...
00:07:40.000They 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.
00:07:52.000Funnily 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?
00:08:12.000And 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.
00:08:29.000So the key thing is, most people are asymptomatic, testing positive, don't have the virus.
00:08:35.000And 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.
00:08:45.000People going back to work after the initial lockdowns.
00:08:48.000And you've got this exponential increase in cases is inevitable simply because you've exponentially increased the number of the amount of testing.
00:08:57.000Again, it's a kind of a statistical artifact.
00:08:59.000But when you looked at real indicators, and of course that was all driving, that drove the second lockdown.
00:09:12.000And 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.
00:09:45.000I mean, an ambulance pickup or a call to the ambulance, sorry.
00:09:48.000And 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.
00:10:15.000And 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.
00:10:23.000There 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.
00:10:43.000And we don't like to think of our hospitals as doing something corrupt, but the temptation to do that was so enormous.
00:10:51.000To 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.
00:11:10.000I'm aware of that happening in the States.
00:11:12.000Now, 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...
00:11:25.000Exaggerating these numbers, the very definitions.
00:12:10.000That'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?
00:12:21.000I think I also saw a statistic, which was over 50% had at least four comorbidities, right?
00:12:26.000The 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.
00:12:39.000So 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.
00:12:52.000So it's not, you know, this is under 18s, right?
00:12:55.000The number of hospitalizations in the whole of 2020, because there was a detailed clinical analysis where they went through each individual clinical case.
00:13:04.000They actually read the case notes, right?
00:13:06.000So 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.
00:13:20.000So that's an interesting thing to know.
00:13:22.000And last year, they finally make this extraordinary concession that vaccines don't prevent infection and they don't prevent transmission.
00:13:34.000But 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.
00:13:49.000And none of that is explained statistically or with any kind of data whatsoever.
00:13:57.000That 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.
00:14:14.000So the key thing is, I mean, just to clarify...
00:14:17.000Just 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.
00:14:38.000And 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.
00:14:55.000It's supposed to show your chance of survival following that intervention are greater if you get the intervention than the placebo group.
00:15:05.000And with the vaccines, they have not been able to show that.
00:15:09.000Indeed, 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.
00:15:21.000So even in the randomized controlled trial, you didn't have the all-cause mortality evidence in favor of the vaccine.
00:15:28.000But what we've done is, of course, we've looked at all of the available observational data on this.
00:15:35.000And 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.
00:15:53.000And the problem was, in the first report that they produced, It wasn't properly age-categorised.
00:16:00.000In fact, it wasn't age-categorised at all.
00:16:02.000And 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.
00:16:26.000When you're grouping all of them together, that's kind of inevitable because you've got the age confounder.
00:16:30.000Most of that time, obviously, most of the people who die are generally in the older age categories.
00:16:37.000And at that time, most of the people, the much higher proportion of vaccinated was in the elderly.
00:16:43.000So, you know, you really have to go drill down into the different age categories.
00:16:47.000And 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.
00:16:56.000They 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.
00:17:09.000You had a much higher mortality rate in the vaccinated, but we don't know whether that's due to the age confounding.
00:17:15.000But with these older age categories, we seem to have all the data we needed, right?
00:17:21.000So this was the data we really studied, and we found some really, really strange things with this data.
00:17:27.000So 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.
00:17:36.000But at that point, it was up to September, whatever.
00:17:38.000And 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.
00:17:56.000What 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.
00:18:12.000When we looked at it for non-COVID mortality, you saw exactly the same thing.
00:18:16.000So 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.
00:18:34.000And what's more, these peaks are different for each age group because the rollout was different for each age group, right?
00:18:42.000In each age group, you're seeing this weird peak at exactly the time when the vaccine rolled out and it's non-COVID deaths.
00:18:49.000Well, 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.
00:19:08.000Because in our country, you are not classified as...
00:19:12.000As vaccinated until two weeks after the second vaccine.
00:19:17.000And most of these anomalies where immediately after the vaccine, there's these huge spikes in deaths.
00:19:24.000But 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.
00:19:43.000So you know when you see that, that there's misclassification, right, because it's happening in each age category.
00:19:49.000Now, 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.
00:19:59.000However, the Office for National Statistics, they argued, they told us, But actually, they weren't doing this misclassification.
00:20:09.000They 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?
00:20:17.000However, we have evidence that this was not the case.
00:20:22.000The 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.
00:20:44.000Now in the UK, that simply did not happen.
00:20:47.000We've got anecdotal evidence that didn't happen.
00:20:49.000It shouldn't have happened because those people who actually had the most critical illnesses were the ones who were prioritised.
00:20:55.000So those should have been the ones who were vaccinated early, right?
00:20:59.000We've also got data on seriously ill people, which shows that those spikes couldn't even occur if this healthy vaccine effect was true.
00:21:10.000What 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.
00:21:23.000And we've also, even since, having them having denied, first of all, that this vaccine You know, they were misclassifying in this way.
00:21:31.000Bear 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...
00:21:57.000And all of those analysis are based on this data which we've shown to be flawed.
00:22:02.000In 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
00:22:21.000Now, 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.
00:22:43.000That's what we believe is there in the data, but is, of course, being hidden.
00:22:46.000One 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.
00:23:09.000And 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.
00:23:19.000Their secondary claim it will prevent transmission.
00:23:33.000But 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.
00:23:55.000And it appears what's happening in all of these anecdotal reports from local hospitals and doctors, none of them are peer reviewed.
00:24:05.000And 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.
00:24:18.000And if they do not tell their vaccination status, the default is to classify everybody as unvaccinated.
00:24:27.000So 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.
00:25:06.000But 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.
00:25:24.000I mean, the UK, they've made these ridiculous claims.
00:25:29.000I got a text on my phone saying that it said over 8 out of 10 people currently hospitalised with COVID are not vaccinated.
00:25:38.000But 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.
00:25:50.000Anyone else is classified as unvaccinated.
00:25:53.000You know, right there, two miles from where I live, there's a...
00:25:57.000A 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.
00:26:12.000Yep, that's exactly what they're repeating.
00:26:13.000That's exactly the type of message that's being repeated ad nauseum.
00:26:18.000I 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.
00:26:33.000And I mean, it's just, you know, all the time.
00:26:35.000So, you know, the whole messaging exaggerating the statistics, it's just been a continual theme.