Dr. Denis Rancourt says scientists need to look at data that shows there was no evidence of a spread of a viral respiratory disease. He said, “That thinking has to be reset. Otherwise, we’re never getting out of this and they will keep doing this whenever they want. They will declare pandemics whenever they want. And they will assault the population in these kinds of ways anytime they want, if we don’t start resetting it. The way to reset it is to use hard data that cannot be disputed— And that is mortality data.”
I’m pleased to announce our first witness this morning, Dr. Denis Rancourt. Denis, can you state your full name for the record, spelling your first and last name?
Dr. Denis Rancourt
Yes, Denis Rancourt. Denis is D-E-N-I-S. Rancourt is R-A-N-C-O-U-R-T, and if you say Rancourt, that’s fine.
I’m so sorry. I mean no offence by getting your name wrong there. Denis, do you promise to tell the truth, the whole truth, and nothing but the truth, so help you God?
Dr. Denis Rancourt
Now, by way of introduction, you have a Bachelor of Science, a Master of Science, and a PhD from the University of Toronto. These are degrees in physics.
Dr. Denis Rancourt
You have been a Natural Sciences and Engineering Research Council of Canada [NSERC] international postdoctoral candidate in prestigious research laboratories in both France and the Netherlands.
Dr. Denis Rancourt
You became, and I’ll just use their anachronism [sic], a national NSERC university research fellow in Canada.
Dr. Denis Rancourt
You were a professor of physics at the University of Ottawa for 23 years, attaining the highest academic rank of tenured full professor.
Dr. Denis Rancourt
But more importantly, and I will ask you to explain this point, as a researcher at the university you were a researcher in interdisciplinary research. So you weren’t just tied to physics, and I’m wondering if you can please explain that for the audience. After that, you’ve been invited here today to speak about some things that flow from all-cause mortality data, and I’m going to ask you to launch into your presentation.
Dr. Denis Rancourt
Okay. It’s very common for physicists to be more interdisciplinary than some other areas of science. I ran a large laboratory that did interdisciplinary research, meaning that we use the methods of physics and mathematics to analyze problems in everything from environmental science to planetary science to theoretical physics to biogeochemistry, including interactions between bacteria and minerals, in the environment. Those kinds of things. So I was the head; I was the lead researcher in a laboratory that developed techniques to study these problems. We wrote more than a hundred articles, in scientific journals, about these questions.
Thank you. I’ll ask you to go into your presentation.
Dr. Denis Rancourt
Okay. Well, before I put the slides up, I’d like to say a few things. And I want to start by giving you my conclusions.
I’ve been working on all-cause mortality in its analysis for more than three years. I’ve written more than 30 reports about it, detailed scientific reports; some of them are more than 100 pages long with many figures and graphs and detailed interpretations. I’ve come to the following conclusions, and I will try to demonstrate how you must come to these conclusions by my material here that I brought today.
The conclusions are as follows: First of all, if governments had done nothing out of the ordinary, if they had not announced a pandemic, had not responded to a presumed pathogen, had done nothing other than what we normally do when we have a high season of mortality in the winter, then there would have been no excess mortality. Nothing special would have happened. That is a conclusion that I hold firmly from analyzing the data. So, in that sense, there was no pandemic that caused excess mortality. None at all. There was the usual ecology of pathogens: viral, bacterial, whatever you want to imagine. There’s a huge ecology of pathogens that we live with. They’re always there. We get sick. We recover. Sometimes we die. That’s all true. But there would have been no excess mortality beyond the historic trend if we had just left things alone. So there was no pandemic in that sense.
The second point I’m going to be making is that the measures that governments applied,
which I would think of as an assault: there were many different kinds of assaults against people. And those assaults definitely and quantitatively caused excess mortality in many jurisdictions and at various times during the pandemic period. Very significant deaths. In some jurisdictions, relatively little. And so on.
And the final point is that the vaccination campaign, the COVID-19 vaccination campaign itself, definitely caused excess mortality in definite peaks that are seen—that are directly associated with various vaccine rollouts of different doses to different age groups and in different jurisdictions. And you can see those excess mortalities immediately. There is no way to escape the conclusion that the vaccines definitely caused death in a significant number. And I’ll give you what those numbers are in my presentation.
Just before you start, I will advise the commissioners and the public that your CV has been entered as Exhibit OT-1a. I think it’s 50 pages long. And you spoke about papers that you wrote that we have entered as Exhibit OT-1. The papers that you provided to us: those are available to both the commissioners and the public.
I’m going to ask if you are adopting those papers as true, as part of your evidence today.
Dr. Denis Rancourt
Absolutely. Those are all papers that I authored and co-authored, and everything in them is from me and is true to the best of my knowledge.
Dr. Denis Rancourt
So I could move on to my slides now. That’s just the header. I want to say a little bit more about my background. There are five areas of science that I’m an expert in as a result of studying these various questions.
One is nanoparticles, small particles in nature and in the environment. Even the new vaccines, the mRNA vaccines, are nanoparticles, surrounded lipids, and so on. I’m an expert in nanoparticles: their stability, their chemical reactions, how they form, how they disperse in a fluid, and so on. I’ve written scientific papers about this.
I’m an expert in molecular science, and by that, I mean chemical reactions of molecules. I worked in a prestigious national chemistry laboratory when I was a postdoctoral fellow, in France. I have done theoretical work on molecular dynamics. I know how molecules bind to various surfaces, to each other, and so on. I know a lot about the intimate details of molecules and atoms.
I know about statistical analysis. I’ve written scientific papers on advanced statistical analysis methods, such as Bayesian inference theory. I know about error propagation. I’ve written about that. I’ve taught it at the graduate level. All of these are areas of science I have taught to graduate students in every department I’m in. In science and engineering departments, I used to do a graduate course on scientific methodology which I had developed.
I’m an expert in modelling—meaning theoretical modelling. I’ve done modelling of the dynamics of environmental systems. And now I’m doing modelling with co-author Joseph Hickey on epidemiology: the classic theories of how things spread, the dynamics of that through a population. We have written two papers on that recently, and in both cases, the editors refused to even review them. We appealed one, and we won that appeal, and both have now been peer reviewed. And so I’m a modelling expert.
And finally, and not least, I’m an expert in measurement methods. I mean by that: How can we know things in science? There are a whole bunch of important measurement methods. They include diffraction; spectroscopies; microscopies, including electron microscopy; and various bulk property measurements. I have taught all of these methods at the graduate level. I had an electron microscope and several spectrometers in my laboratory when I was a lead researcher at the university.
That’s my background. That’s why I feel I can read a scientific paper and really understand what it’s about. I do this work with several collaborators; we work closely. I want to name them here:
Marine Baudin; Joseph Hickey; Jérémie Mercier; John Johnson, who is a professor at Harvard University; and Christian Linard, who joined us in our discussions and in our work, very recently.
I have written more than 30 articles about COVID-related matters, large reports and articles. They are on my website. My website is very complete. It’s organized by section: denisrancourt.ca. I prepared a book of exhibits for this testimony, which you have and is now entered as an exhibit. It contains almost 900 pages and many of the key articles for the conclusions of today. So this is just the index of that book of exhibits. The last one there, article number 87, is actually an article written in 2019 which is a very thorough analysis of geoeconomics and geopolitics since the Second World War. I think that gives the proper context to really understand, from a social point of view, what was going on here.
As I said earlier, these are my main points: There was no pandemic in the sense of causing excess mortality. It’s the measures and the assaults that caused mortality during the COVID period before vaccination. And then when they rolled out the vaccines, that caused definite excess mortality as well. All of this is based on all-cause mortality data, and I wanted to show you what that looks like.
This is all-cause mortality by month. You can do it by week, by day, and so on. But this is by month for the USA since the year 2000. Now, we’ve had this kind of data for more than 100 years in many Western countries. Because February has only 28 days, there’s a little dip in February that you can see there, and that allows you to see where February is. You can see that the mortality is seasonal. In the northern latitude countries, it’s always higher in the winter and then you come down to a trough of mortality in the summer. The y-scale here doesn’t start at zero. You have to notice that; it’s expanded. This goes right into the COVID period. So you can see that in the United States, that last one of a bluish colour there is the mortality in the entire COVID period, which is significantly higher than the mortality before, if you look at the historic mortality.
When the pandemic was announced on the 11th of March 2020, we start the COVID period there and we put that in a certain colour. Then you can add all the deaths per month for all the months of the COVID period, and you get the total deaths for the COVID period: that’s the black dot that’s higher than the others there. You can take the same duration period and move backwards in time and do that sum: that’s the other black dots.
So the black dots allow you to see the historic trend of the mortality on the timescale of a COVID period, if you like. You can see that it increases very gradually. That’s because the age structure of the population is changing. The baby boomers are coming of age to be older and are dying more, and you see those kinds of effects. But what you see also in the United States, this is for the entire U.S., is a stepwise dramatic increase right in the COVID period. That’s the kind of data that we analyze. We can look at it by state (50 different states), by city, and by age group. That’s the mortality.
This data cannot be biased. You’re simply counting deaths irrespective of what people died from. There’s no bias here. This is all-cause mortality. You’ve got that extra filter, which is by age, by sex, by jurisdiction, and as a function of time.
So it is very, very powerful data. This is the kind of data that allows you to spot heat waves, earthquakes, wars. Anything that will perturb the population to the degree that it will cause mortality is immediately seen in this kind of data. Just as a note, I want to make it clear that the various pandemics that were announced between the Second World War and before COVID, by the CDC, in Canada, where they estimated the number of deaths—none of those deaths are detected in all-cause mortality. In other words, there was no excess mortality related to the past so-called pandemics. That’s clearly described in our papers.
We’re still in the United States,
and now we’re going to blow it up on the time scale. We’re going to go from 2016 to the present. Now the data instead of being by month, is by week. There’s a higher resolution there. You can see in detail the evolution of the all-cause mortality as a function of time, there. You can do that quantification. Just to show that you can go higher resolution here.
One of the very strange things in the United States is that for the first time in the historic record, there were peaks of excess death in mid-summer in the United States. In the southern states where it’s very hot and where people’s lives were basically dissolved and they were dramatically perturbed in how they normally deal with poverty and heat, there were actual deaths—extra deaths—in the summer.
Now in the United States, when you integrate that all-cause mortality in the COVID period and then you look for social factors that correlate to that on a by state basis, this is the strongest correlation that we found for a single social factor. We looked at many, many. It shows a correlation of all-cause mortality integrated over the COVID period on the y-axis as a function of the fraction of the population that is living in poverty. This is what we call in science “technically a very strong correlation.” The Pearson correlation coefficient is plus 0.86, which is unheard of in the social sciences.
And it’s not just a correlation; it goes through the origin, which means it’s proportionality. Which means that in a state that would have had no poverty there would have been no excess deaths during the COVID period. So there’s a strong correlation to poverty, which is one of the pieces of evidence that allows you to say that this is not a virus. Because a virus, and COVID in particular, is said from clinical studies to kill mainly elderly people—and it’s even exponential with age. We find instead that we correlate the things like poverty. But if you did this kind of a map, which I didn’t bring, as a function of age—median age, or number of people living in the state, the fraction of the population that is over 80 or over 65, and so on—no matter how you slice it, there is absolutely no correlation with age, which is a definitive proof that this cannot be COVID as studied in clinical studies.
Can I just stop you, Denis? So you’re basically sharing with us that this chart is showing that people that had more poverty were more likely to die. And that’s not a function then of a virus, it’s a function of something else?
Dr. Denis Rancourt
That’s right. Not just “more likely,” as you would say, in a weak sense, if you were a scientist. This is the strongest correlation you’ll generally see between a socioeconomic factor and something happening in the population. This is an incredible graph. This shows an absolute, not just correlation, but proportionality to the size of the population living in poverty. This shows that the COVID period, on the scale of the nation in the United States, killed the poor in proportion to how many poor there were.
The other strong population correlation factors are, for example, how many people are living with disability: are certified disabled, cannot function in society, and need to be supported by the state. The number of people with those programs in the United States is also a very strong correlation to whether or not you died.
And so the United States is a very special jurisdiction that has large amounts of both disabled, poor, obese, and people suffering from diabetes. All of these things correlate to whether or not you were going to survive the COVID period. And that is why the United States, in proportion to its population, had a much higher mortality than Canada did. So even if you take the population into account, taking the population into account, Canada had five times less excess mortality than the United States. Five times less.
In other words: if this was a virus, it refused to cross the Canadian/American border. It was presumably causing death in the U.S. to this degree but would not cross the border into Canada. That virulent pathogen did not act in Canada. So that is impossible,
in terms of epidemiological theory. That is strictly impossible if you want to believe that theory. It’s thousands of kilometres of border, two of the biggest economic exchange partners in the world. That cannot happen. So that’s yet another line of evidence that this was not a viral respiratory disease pandemic.
Can I just ask you one more question, and I’m sorry. But do we know what factors of poverty might have played in? Like might it have been that the poor do not have as good nutrition or don’t access treatment or things like that? Like, are there any others?
Dr. Denis Rancourt
Yes. We tried to answer those questions in our large papers, and we concluded— Well, that’s a very interesting and deep question. What we found was that this death was occurring mainly in the poor states in the south of the United States, where it’s also very hot. And those are populations that normally get many, many prescriptions of antibiotics in the winter. So they have a high susceptibility to bacterial pneumonia infection, and they normally get treated. But during the COVID period, all Western countries cut antibiotic prescriptions by 50 per cent or more, including the United States. So they were not treating bacterial pneumonia. And these people always get them, always have this problem, and were not being treated.
And so we believe— And the CDC [Centers for Disease Control and Prevention] has agreed based on death certificates that a co-cause of death in the great majority of the so-called COVID-19 deaths is bacterial pneumonia. So we know that there was a massive epidemic of bacterial pneumonia. We know that it was not being treated up to standards whatsoever, and we believe that mechanistically, this is what killed the poor, obese, and so on. There were other factors as well, and we discuss them in detail in our papers.
Now, we’re still in the United States here, and this is the per cent increase in mortality. It’s the excess mortality expressed as a percentage of what the mortality would normally be by age group. This is now by age group, and this is before vaccination was implemented in the COVID period. So we’re starting at 11th of March 2020 and going up to the end of 2020 before we start vaccinating.
We can see that excess mortality expressed as a per cent for the 10 most populous states in the United States here—the different colours—goes from something like 5 or 10 per cent for these zero to 24-year-olds and up to something like 20, all the way up to 40 per cent for the other age groups. So it’s very, very high, and it’s high across the board in relative amounts, expressed this way for all the age groups of young adults all the way to the elderly.
Then, if we keep those 10 populous states and look at what happens in the period where you were vaccinating, because the rollout was very rapid, you get a very different pattern like this, where the 25- to 44-year-olds are affected up to 60 per cent excess mortality on a relative basis. So the age structure of the mortality has changed now as you move into the vaccination period. That’s the kind of analysis that you can do. This is just to illustrate.
So that’s the United States. So remember, in the United States, you have this massive increase of mortality in the COVID period, there at the end. Remember this mortality versus time. And remember that step where you have a regime of higher mortality at the end in the COVID period. Now let’s compare to Canada.
Here’s Canada in blue. Forget the red line for now. The blue is the all-cause mortality by week in Canada from 2010 to the present. What you notice is that there is no stepwise increase. There’s virtually—nothing happened relative to the summer trough baseline, if you like. There’s nothing special happening that’s visible in the mortality. This is in heavy contrast to what you would see if something real and important happened like a war or, for example, the Great Depression, the Dust Bowl in the United States. These give large mortality increases. There’s nothing like that in Canada.
So nothing special happened in terms of mortality in Canada. At first look, coarse-grain looking. But now if we look at the details, we will see things.
Now I want to contrast that with what Theresa Tam and her co-authors said in a scientific paper. They actually said from a modelling study, a bogus model, that if they had not applied all the measures—masking, distancing, vaccination, and so on—that there would have been approximately one million extra deaths in the COVID period in Canada. So I represented what that would look like in red here in the COVID period. I distributed those million deaths uniformly, just a simple model to show what it looks like. And that’s what the million extra deaths that they’re saying they prevented would have looked like.
Now I have to explain: that is absurd. Nothing known historically can cause that kind of mortality. And to affirm something like this is absolutely ludicrous. For example, if that is true, then why did the measures not reduce it by only half? Why did the measures not reduce it by only 80 per cent? Why did we come down to a mortality that happens to be approximately exactly what you would expect historically? It makes no sense. There’s one universe in a million where this could happen by accident. So this is absurd. This is the level of scientific propaganda that our government scientists are putting out these days.
Now we’ll take a closer look at the mortality in Canada. I have now shown this on a scale where Y starts at zero, and we’re seeing the all-cause mortality again. And now what I’m doing is I’m integrating over what we call a cycle year. So we go from summer trough to summer trough and we integrate the mortality. It’s total mortality per cycle year, if you like. The integral points are there. And you can see a small increase relative to the linear historic trend in the COVID period for those two cycle years. Very small, but you can quantify it. And there’s also details, of course. We’ll look at those.
This is the same, but now it’s on a different Y scale. So you blow it up a little more, and you can see it more clearly where the excess mortality is at the end. This is by cycle year again.
And now I’m showing it by calendar year. So actually, the last point there is the total number of deaths in 2022. This data for Canada just came out a few days ago and we made this graph. This shows that the excess mortality since the COVID period started in 2020, did not decrease whatsoever because of this huge vaccination campaign.
Remember, the vaccines were supposed to prevent serious illness—and that means prevent death. And there is no indication that this military-style vaccination of everyone reduced deaths whatsoever in Canada. In fact, 2022 has significantly higher deaths than the previous two years, one where you were vaccinating and one where you were basically not vaccinating. So the deaths are higher now in Canada. That’s the situation in Canada. They’ve created circumstances where the deaths are higher in 2022.
Now we can compare the all-cause mortality for Canada to the vaccine rollout in this graph. So the dark blue line is the cumulative number of vaccine doses administered to the population. You can see that when the rollout starts, you get an extra peak on the shoulder of that winter peak at the beginning of 2021. And that is a very strong peak, especially in Ontario, for people that are 59 years of age and older. And then you can see that the third dose rollout, which is this significant increase, gives you the highest winter peak we’ve seen in a long, long time. And there are other details.
So we can blow that region up and look at it again and label some of the peaks so that I can discuss them. That vertical line arrow pointing up is the start of the pandemic. That’s when it was announced, the 11th of March, 2020.
What I call peak A is a very important peak because it is a surge in mortality that occurred immediately after the pandemic was announced. But you have to understand this peak. We’re going to look at it in some detail. It is very heterogeneous from jurisdiction to jurisdiction. It did not occur in several Canadian provinces. It was very prominent in Quebec. And so it depended what you were doing in those jurisdictions to fragile people who were in hospital and ICUs and care homes, whether you were going to cause deaths during that peak. We’re going to look at that peak some more.
I can point some things out. That peak C is the one that arises because of doses one and two rollout. Peak E is the very high peak related to the third dose rollout. Peak F is a peak that occurs when they rolled out a booster to the elderly. And some of these peaks come out more significantly when you look at different age groups. But this is just to give you a broad view. And D is an example of a heatwave peak. This was a heatwave that occurred in southern British Columbia at that time.
Now we’re going to look at peak A, what we’ve been calling the COVID peak, that arises immediately after the pandemic was announced. And that peak is absolutely huge in the United States. We’re showing some of the states where it is the largest. And this time, all-cause mortality by time by week in fact is normalized by the population of the state. So you see that you get this complete overlap normally and the seasonal variations. So we’re looking at Connecticut, Maryland, Massachusetts, New Jersey, and New York. We see that that peak that fires up right after you announce the pandemic is massively different from state to state. There were about 30 states in the United States that did not have such a feature. It’s exactly at the same time whenever it occurs. And it’s very, very different in magnitude.
That same kind of peak happens at the same time in different parts of the world. So there are hot spots when, just after you announce the pandemic, you get these massive peaks. They’re shown here for Lombardy, Italy, and the region of Madrid, and an area in France, and so on. These peaks occur in very specific hot spots, but synchronously around the world.
Now, I want to insist on this: that—from an epidemiological standpoint—is strictly impossible. Because the time from seeding of an infection to the sudden and measurable rise of mortality is completely uncertain. It is a factor that is extremely sensitive to the details of the population, the institutional structure, and so on. It cannot be the same everywhere; even if you fly seeds out by airplanes at the same time to everyone on the same day, you will not get peaks of mortality that occur synchronously. It is impossible. That time between seeding—depending on the size of the seed—and the maximum in mortality varies by many, many months; it can even be years. So that’s impossible.
The first thing I said when I saw these peaks, as I said: this is not a viral respiratory pandemic. This has to be peaks that were caused in those jurisdictions that were hot spots. And in fact, in Lombardy, Italy, in that region in particular, they said, “Don’t stay home, come straight into the hospital, we’ll treat you.” And they were putting two people per mechanical ventilator when they were sick enough. And they were doing horrible things, and there was a massive killing of people, I believe, in that peak.
Now, we’re going to study that peak in some detail across Europe. I’m going to show you some maps. This first map is just to remind you where the countries are when you look at the other maps. But also, I put in blue here some borders. Those borders are interesting because you’ll notice in the maps I will show you of the magnitude of that mortality peak mapped on Europe, that the virus—if it was a virus—absolutely refused to cross these borders. Absolutely refused. There’s no crossing of these borders.
Of course, that’s absurd. A viral respiratory disease is believed
to spread, and it does not need a passport, and it does not respect borders. So that’s yet another proof that this was not a viral respiratory disease pandemic.
Let’s look at these maps. We’re going to start in January 2020, before the pandemic was announced. What I’m representing here with the different colours is the intensity of the excess mortality integrated for January. So basically, January was an ordinary month and you’re around zero for all of Europe. February, same scenario, nothing special is happening. Now we hit March, which was when the pandemic was announced and when this peak arose. And there you go. Those are the hotspots.
So you can see: northern Italy, large regions around Madrid and Spain, and so on. And what you’ll notice is that you do not cross the border between Portugal and Spain. You do not cross the border between Spain and the south of France. You do not cross the border whatsoever into Germany: Germany was completely protected from this excess mortality at that time in the pandemic. Germany did not have these excess deaths whatsoever. And then as we go down off this peak, March into April, we’re still on the tail of that large peak. Those regions are the same, basically, and those borders are not crossed whatsoever. And then we get into May and June and the peak is over. And you have to ask yourself, what caused that peak?
Well, we’ve talked about it somewhat. We are now looking at everything that was done in that period specifically that could have caused this excess mortality. What we’re finding in many jurisdictions is that hydroxychloroquine, HCQ, had many, many prescriptions—a super-prescription of that drug in the jurisdictions that had this peak. Germany was not doing this and they did not have the peak. Spots that were doing it—counties, and so on, that had high prescriptions—had a lot of deaths. Now, this is not the only drug that had that peak.
So what basically was happening is: emergency MDs were told, “This is a pandemic, we don’t know what it is. It’s a new virus, do what you can.” At the same time, there had been the suggestion that hydroxychloroquine could be very effective. I’m not saying that’s wrong, but I think that some people were very reckless in prescribing it to their seriously ill patients. And this drug has the property that it has a very narrow and well-defined window of utility for treating people. And if you go in excess of that, it becomes a toxic chemical. It is quite possible that in the jurisdictions that did not know how to use this drug— And we know they overprescribed it as much as two grams, so we believe that that’s one of the drugs that would have caused this peak. Another one is various sedatives that were used often in combination with the mechanical ventilators. This is ongoing research that we’re looking at.
Hopefully by now, I’ve convinced you that at least there exists evidence that there was no pandemic, and that it was the measures that were killing people before vaccination.
Now we want to look at vaccination. The first thing I want to say is that there is absolutely no doubt that the vaccines cause significant death. There are now dozens and dozens of autopsy studies that show a causal relationship between the vaccination and its effect on the cells and organs of the body, and that that would have caused death. There are many autopsy studies that show this.
Adverse-effect monitoring is showing a peak of death immediately following vaccination, in the first few days: a very definite peak of death, followed by an exponential decay of death that lasts at least two months. We showed this in detail in one of our papers.
There was a survey study done by Professor Mark Skidmore that just asked people, “Do you know anyone close who died that would have been due to the vaccine, just immediately after vaccination?” And on the basis of that scientifically-performed survey, they found that in the United States about 300,000 people would have been killed by the vaccine. There are many, many studies now showing that there are induced pathologies: meaning sicknesses, disease,
related to having been injected. There are more than 1,250 peer-reviewed scientific studies analyzing and showing vaccine harm. And our work is to look at all-cause mortality, and we calculate what is called the “vaccine-dose fatality rate,” meaning: What is the risk that you will die from being injected on a per-injection basis? That’s what we’re going to look at now.
This is a list of some of the recent autopsy studies, and there’s some more there. Now we’re going to look at the vaccine period.
Doctor Rancourt, we will make your slides an exhibit [Exhibit OT-1b] so that the public and commissioners can access and actually see the references.
Dr. Denis Rancourt
Perfect. Thank you.
When we started looking at the vaccines and whether or not the rollouts could cause death, one of the first things that drew my attention to this is: four studies came out in various scientific journals about India. India is a difficult case because they don’t publish national high-quality all-cause mortality data. So you have to actually go on site and go into the various provinces and the various institutions and gather the data yourself to some degree. So there were four studies that did this, and they all found the same thing. They found that there was no excess mortality when the pandemic was announced. Absolutely everything was normal. And then all of a sudden, many, many months later, there was a huge surge, a massive surge of deaths. So they showed this and they explained that India had this huge peak in mortality. And none of the four groups of researchers mentioned that that surge in mortality was occurring exactly when the vaccine was rolled out, military style. And I said, how is this possible? How could they not even mention it? So I wrote an article critiquing them and pointing out that this is exactly when the rollout occurred. And therefore, it allowed me to calculate that those deaths were due to the vaccine.
I showed, I single-authored that one: 3.7 million people were killed in India by the vaccine, 3.7 million. And this is because they targeted elderly and sick people. India actually put out a list of 12 comorbidities of very sick people: if you had those, you should be vaccinated right away. They did something that was called a “vaccine festival.” That’s what the prime minister called it. And they said, “Go and get your sick and elderly everywhere and make sure everyone gets vaccinated.” And they killed 3.7 million people. The vaccine fatality rate that I calculated for India was 1 per cent, which means that one out of every hundred injections caused a death in India.
Then we said, “Let’s look at this for Western countries and for other jurisdictions.” We looked at all the data we could from the UN [United Nations] and tried to identify countries that would be easiest to study at first. Australia jumped out at us because Australia is a country that had no excess mortality whatsoever during the pre-vaccination period and then a huge increase in mortality, a new regime of mortality, when they rolled out the vaccine. So we said, “Let’s target Australia and see what’s happened there.” And you can see the integral value in the vaccination period jump up for Australia there on this graph.
And this is a blow up of it. You see mortality by week in Australia. You see the vaccine rollout. And you see that as a consequence of the vaccine rollout, there’s the higher regime of mortality right there. We also see a peak in their summer, our winter. Remember, mortality is higher in the experienced winter. So in the Southern Hemisphere, mortality is higher during the period that is our summer, but it’s their winter. And there’s seasonality like we normally have. But here in the middle of their summer, they have a sharp peak right there. You can see it. And that coincides exactly with the very sudden rollout of the third dose of the vaccine.
I’ll show that in detail now. Here’s the rollout of the third dose superimposed on that peak of mortality for all of Australia. It is the same thing for each of the states in Australia. This is Victoria, New South Wales, Queensland, and so on.
You get this rollout of the third dose and a peak in mortality that accompanies it. On that basis, we can calculate things—which I’ll show you in a minute. But first I want to do a little bit of around-the-world of these kinds of correlations.
So this is Mississippi. Something happened that was very unusual and very sad in the United States: They decided that they needed to have vaccine equity. So large financiers and companies and pharma-tied interests decided that people were not being vaccinated enough in certain states in the United States, so they will have a vaccine equity program, which was highly funded.
They hired thousands of people and they went and vaccinated the most vulnerable people living in various homes. So in the poor states, you can see that vaccine rollout. You can see that increase in the cumulative doses being given there. That is the vaccine equity program. And then you can see that huge peak that is coincident with that in mortality for the 25- to 64-year-olds. Now we see that large peak, which is bigger than anything else, that coincides with vaccine equity in basically all of the poor states in the United States—so Alabama, and so on. In those jurisdictions where you get that state, you have the same vaccine-dose fatality rate as they had in India, so 1 per cent. It’s massive for that peak.
This is a peak that occurred in Michigan coinciding with the initial rollout of doses one and two. And that same peak occurs, meaning the same properties occur, in Ontario, where it’s very important.
Now, this is a summary of all this data. I’m getting to the end now. This is vaccine-dose fatality rate expressed as a percentage by age group. You can see that for the most elderly, it goes up to almost 1 per cent—even in Western countries. This is Australia and Israel, where they have really good data on a “by age group” basis, of both doses and mortality. We were the first to do this. This is the first data that was produced that shows that the risk of dying from the injection goes exponentially with the age of the person being injected. And the doubling time of that exponential is about five years. So for every five years in age, your risk of dying from the injection doubles.
And this proves that it was absolutely the opposite of what you should do, from a public health basis, to go and vaccinate the most vulnerable in terms of being elderly. The risk-to-benefit ratio is completely out of whack. You are injecting people that are at high risk of dying from the injection when you inject the elderly. And this is the first quantitative demonstration of that for Australia and Israel, where we were able to do it. You can blow up the bottom of that exponential, and you can see that the young adults are above the exponential, that holds for the more elderly adults, starting at around age 40. There is a plateau of risk of dying from the injection for young adults that is maintained.
And you see it if you do a semi-log. For those of you used to looking at these graphs on a semi-log basis, you can see that plateau in the mortality risk from the injection for the young adults there relative to— The linear part is the exponential part on this kind of graph. So you can see what people are talking about in terms of sudden deaths of athletes and young people in this kind of data.
This is just to show you for Israel, the coincidence between the various dose rollouts and peaks in mortality. Now, this graph here is for all ages. But it is even more noticeable when you do it by age group, you see? Look at the relationship here for the 80-plus-year-olds between when you roll out the doses and when there is a peak in mortality.
And this is for 70- to 79-year-olds.
And this is 60- to 69-year-olds.
So you vaccinate and that starts a whole period of induced deaths as a result of that.
And this is 50- to 59-year-olds.
We summarized that data for Israel by calculating the vaccine-dose fatality rate by age group, by dose number. We showed that as you go to further and further doses, the risk of dying is higher and higher—even when you discriminate by age group like this. So again, that particular graph is not published yet, but that’s coming out in one of our next publications.
And that’s on a semi-log basis, what it looks like.
So this is my conclusion: Every jurisdiction that we’ve looked at—India, Australia, Canada, Chile, Germany, Israel, New Zealand, USA, and many others, including all the European countries that you saw—always gives us the same result. There is a risk from dying from the injection on a per-injection basis that is between 0.05 per cent—that’s for all ages in a Western country—all the way up to almost 3 per cent for the most elderly people that are fragile. So we always fall in that range in terms of the risk of dying from these injections. Consequently, we can calculate that that must be a property of the vaccines. And therefore, on the scale of the entire world, given the number of doses that were administered, the vaccine must have killed approximately 13 million people worldwide.
In India, we know for sure. We quantified it: 3.7 million people were killed in India. In the USA, we now have good reason to believe—and different methods giving the same results, the surveys that I mentioned, our method, and so on—about 300,000 deaths in the U.S., compared to 1.3 million deaths for the entire COVID period. So a large fraction of the COVID-period deaths in the U.S. can be directly associated with the injections.
In Canada, we’re still quantifying, but the number’s going to fall between about 10,000 and 35,000 deaths that were directly induced by the vaccine. And remember, I showed you the graph for that. Those deaths are larger [in number], they’re not smaller, when you vaccinate. That concludes my presentation.
Now, this is looking at deaths. I’m just curious. So it doesn’t show whether there’s any benefits, it just shows that we have excess deaths. I know that sounds like an odd question, but it’s just that the public messaging has been: the purpose of the vaccine, the benefits, were that it would reduce our symptoms or make the experience of having COVID less severe. I’m wondering if you can give us your thoughts on that public messaging.
Dr. Denis Rancourt
Well, I have been concentrating on data that is robust, that is bias-free— and that data is mortality, all-cause mortality. So I confine myself to saying everything I can say about mortality. However, I note that they are claiming many times that the vaccination would have caused less severe illness. Well, if that’s not related to death, I don’t know what is.
Right, death follows severe illness, generally speaking. I understand that this is just speculation, but your speculation is that the public claim about reducing severity of illness is likely not—
Dr. Denis Rancourt
Well, as a scientist who reads the literature extensively, I can tell you that the trials, the pharmaceutical industry trials that would have looked at safety and efficiency of the vaccines, are highly flawed. They’re completely rigged. I think rigged is not an exaggeration. They exclude vulnerable groups, which are exactly the groups that are being killed by these injections. They exclude them from the trials. So you cannot know if particularly fragile, very elderly people would have been killed by the injection because it was never tested
My other question is, and I appreciate you have to wait for the data to be published.
My understanding is vaccination intake has dropped, so fewer people, for example, are taking the boosters. Are you seeing any reflection in all-cause mortality with a less robust uptake of boosters?
Dr. Denis Rancourt
In most countries, excess mortality is now dropping and is coming back to normal. There’s a subgroup of countries like Canada where, in 2022, the mortality is higher than it was even before. Those are countries of concern for us that we’re studying now in detail.
Boosters, generally most of the population is refusing the more advanced boosters. Therefore, they’re targeting the elderly more, supposedly to protect them. So that’s part of the reason that we measure on average that the booster doses are more lethal—because they’re also being given predominantly more to elderly people. But they appear to be even more lethal when you take age into account, as I showed today with these graphs, where there’s somewhat higher risk of mortality with the higher doses. But all of this is occurring together, and sometimes we can’t unpack the data enough to really answer some of these detailed questions.
Thank you. Those are the questions I have. I’ll see if the commissioners have any questions for you. And they do.
Thank you very much, Dr. Rancourt, for this very comprehensive analysis. You did point to a number of conclusions that I think are pretty well-founded based on your analysis. But there are still more questions to be examined. For example, without getting into too much speculation, I was wondering whether you’ve examined causes for the targeted population of disabled, especially in the south of the United States, like Mississippi or other places, that also happen to be areas where the industrial agriculture is very, very active.
We know from many studies that industrial agriculture extensively uses glyphosate, which is in theory helping the yield, but that’s something we can debate. But what we’ve learned from many studies that are coming in the last decade or so is that glyphosate is a very toxic component for the microbiota. We had a scientist presenting in Quebec City who was mentioning that there was a pretty good correlation with respect to sensitivity to all kinds of infection, including COVID, as well as a propensity for people that had a bad microbiota to be more susceptible to vaccine toxicity.
I don’t know whether you’ve explored that in terms of big numbers, and if you could at this point draw some sort of correlation between the exposure to glyphosate making the population much more susceptible to infection that if you don’t treat, as you pointed out, will result in death.
Dr. Denis Rancourt
We did look in detail into glyphosate use on the U.S. territory. And we looked at maps of that, and we compared those maps to excess mortality maps and to poverty maps, and so on. So we did examine that in some detail. It’s difficult because we’re talking about correlations. We don’t really know. We don’t have specific patients where the presence of that toxic substance was analyzed in their blood, and we know that they died, and we know that they were injected. We don’t have those things that you would have in clinical studies. So we’re looking at correlations.
And what I can say is that it’s difficult because there is heavy glyphosate use in some states that are very poor. So the two are together. But there is also heavy glyphosate use in agricultural regions which are not particularly poor and don’t have a
high prescription of antibiotics, for example. So it’s very difficult to unravel. But I would say that we did not find clear evidence of a glyphosate effect. Let’s put it that way. On the scale of the nation, looking for correlations, we did not find that.
My other question has to do with the number of, I would say, disabled population in the States that, according to your analysis, seems to be much higher than what we have in Canada.
Dr. Denis Rancourt
And that could explain one of the differences we see between the two countries. But do we have in Canada a population that, even though low in number, could be in that category and that suffer more from the measures?
Dr. Denis Rancourt
Absolutely, absolutely. Oh, absolutely. I didn’t say everything we’ve done. There are 30 articles. But in Canada, it’s clear, just to give you a few examples. Even though the mortality is much lower than the U.S., still, quantitatively, there is excess mortality.
So for example, in Alberta, young men at the beginning of the pandemic, when the energy sector was closed down, have a very high excess mortality—above anything else. So there was huge suffering among young men in Alberta that is directly seen in the excess mortality. This correlates with an increase in homicides, suicides, drug deaths, and so on. So there’s tremendous suffering that can be induced from the all-cause mortality in Canada, young men.
Now, there were also many Aboriginal working in that sector. And so we looked at Aboriginals. We found that the highest mortalities among young men were in provinces where there were many Aboriginal people. And not just young men. So there is definitely a correlation in Canada between the fraction of the population that is Aboriginal and the excess mortality. We definitely see that. It’s going to be in our next paper about Canada. So that is certainly a fragile group in the same way that disabled people in the United States are a fragile group. Absolutely. I’m sure it’s co-correlated with things like diabetes, obesity, and so on. But yes, that’s an identifiable fragile group in Canada. So young men whose lives were devastated by losing their jobs in the energy sector and Aboriginal are the two in Canada that we found that were most striking, let’s say.
Another question I’d like to try to put in perspective: the magnitude of deaths that are correlating with the vaccines rolled out in the States. And you and other people are coming with numbers in the range of 300,000 over a fairly short period of time. If you put that in perspective with the significant excess deaths measured in the States from the opioid crisis that took place over a much longer period of time— But clearly, that was so important, according to some analyses, that it did decrease the life expectancy in the United States.
So what would be your assessment of the death toll deriving from the vaccine in terms of life expectancy? And how would you compare that to the opioid crisis in terms of death toll?
Dr. Denis Rancourt
When we identify mortality that we conclude is due to injections, due to the vaccine, we actually see peaks that are synchronous with vaccine rollouts, whether it’s different doses, and so on. Like I showed you. And that includes in the United States. We see peaks that are synchronous with the rollouts. That’s one thing.
Now in order to see that, it has to be strong enough. So there have to be enough elderly people or fragile people that are injected, and so on. The equity program was just shocking in the United States where you really see the peak associated with that. So that’s one proof, if you like—even in Canada and in the United States—that the vaccines are definitely killing people.
But the other argument is we always see the same death risk by injection. So we tend to believe that even though the United States is very complex in terms of its mortality—peaks in the summer, all kinds of things,
and certainly, these other factors that you mentioned—that we expect that the risk of death by injection would be the same in the U.S. That’s how we obtain our global number, our global estimate.
Now to be clear, we studied the U.S. in great detail before we first quantified in so many countries this vaccine-death fatality rate. So now, we’re more certain than ever that this is a real number and that it is a given for these types of vaccines. So that’s how we estimate our 300,000.
So when I say that the vaccine has caused death, I mean that the death occurred soon after the injection and gave rise to a statistical feature that cannot be there by accident. But that doesn’t mean there were not comorbidity conditions. Of course there were. There’s age and many different illnesses and if someone is highly exposed to glyphosate, and so on, a clinician would be able to tell you.
So you’re basically challenging, I think, the number for vaccine deaths in the U.S. on the basis that there are other cofactors. Yeah, it’s true, there are other cofactors. But I have become convinced that this vaccine-dose fatality rate between 0.05 per cent, all ages combined, all the way to 3 per cent for the most elderly, including in Western nations, is a hard number. Every time we do it. And remember: we’re doing it for peaks that should never occur there, that are exactly coincident with vaccine rollouts.
Just to give you an example of how much resistance we’re getting in this work: The four articles on India that didn’t even mention the vaccine rollout. There was a fifth article on India that looked at Bombay, I believe, in more detail, the large urban centre. And they saw the same peak again, but they argued that it was due to the Delta variant.
Now, we looked at that. We were critical of it because when you look at what they actually did, they adjusted the virulence of the so-called presumed Delta variant in order to get the mortality that they were observing. So it was not an ab initio determination of the virulence of the pathogen they were proposing; it was adjusted virulence in order to explain the death. That’s the level that they are pushing these variants. When I read the literature on variants, I think, wow, this is incredible because they’re very small populations that are being analyzed and not selected at random whatsoever. They go to clinics. They’re analyzed. And from this they use computer models to, they claim, tell us what fraction of the infections are due to a particular variant. And they make these wonderful graphs with computers. It’s completely unreliable.
When you read the methodology that they’re using, they admit in all the footnotes how unreliable it is. I am shocked to see scientists reproducing those graphs of how many variants there are and which variants are arising and the proportion of— Making these beautiful graphs straight from these websites that are funded by pharma. I’m shocked to see scientists using them and believing them and interpreting their own studies in terms of the prevalence of these variants. I think it is garbage science. That’s my impression. It’s not my direct area of expertise. You asked me a question, so I’m overflowing a bit. But I think that the variants stories are garbage. That’s me. And I have direct experience of that with India.
Now, there were peaks in Australia. At the same time— If you believe the scientific literature, at the same time that the Delta variant was causing this peak in India, it was the Omicron variant that was causing something in Australia. But really, the peaks in Australia that they were concerned about were exactly coincident with the rollouts of the vaccine. So I did a deep dive into how they determined these variants and decided for myself that it was garbage, and that every time I needed to challenge it, I would: I would look at what they did, and I would point out the errors and the incorrect assumptions.
So we did that for India. And I could go on about— Every time I read scientific articles claiming things about COVID-19, I find huge errors. This is bias. It’s not science. It’s bias. Sorry, I’m going overboard here. I’m stepping outside of my all-cause mortality expertise, but I’m shocked at the degradation of science in general.
Maybe a last question. I mean, your analysis is very thorough, but you’re using methods that are pretty standard methods, right? So how is it that no other team has done similar analysis and generated data that would either confirm or challenge your data in a meaningful way?
Dr. Denis Rancourt
Well, there’s not a lot in the peer-reviewed scientific literature. A lot of our work is also not peer-reviewed. But there are people looking. There are more and more ad hoc scientists, if you like, looking at all-cause mortality data. And some of them are making very useful comments.
Now, with my statistics background our group was able to develop statistical analysis methods that go far beyond classic epidemiology. Because the classic epidemiological view before COVID was to simply put, essentially, a sinusoidal curve through the seasonal variations and to try to extract something from that. It’s not a sinusoidal curve; there’s big problems with that. So we had to, in a sense, reinvent the wheel to develop more robust methods that include error propagation and everything. So we’ve done that.
But any trained scientists of the government or an academic researcher who knows about statistics and understands data can do this. And Statistics Canada does do good work on mortality and does quantitative work and error propagation, so they can all do it. But for some reason they don’t want to see it. They don’t want to— It leads you— The data leads you to concluding things like, “There was no pandemic,” and “The vaccines caused death.” And they don’t want to see that.
Thank you very much.
Good morning, Doctor. I have a couple of questions. You were, in your discussion, talking about different age stratifications and the effects of the vaccines and the peaks and what not. But you didn’t specifically talk about probably two of the most helpless—two of the most at-risk age groups—at least, not specifically. And that is babies, both prior to birth and after birth. Did you look at the incidence of death in the womb and death of babies throughout that time period?
Dr. Denis Rancourt
No, we didn’t look at it. We confined ourselves to all-cause mortality data that could be obtained on a per age group basis. For example, we have many countries where we can look at the zero to five-year-olds and things like that. But these are small numbers of deaths, relative deaths. You will remember one of the graphs I showed that the relative increase in all-cause mortality for the youngest group was fairly small compared to the other age groups. So it’s hard to detect quantitatively from this kind of method.
However, it’s the kind of thing that’s easy to do from clinical observations, right? All deaths are recorded and premature deaths, and so on. So other researchers using other methods should be able to do this very well.
Yes. We heard testimony, I think it was in Quebec City, from one of the experts with regard to pregnant women getting the vaccines. I believe under questioning one of the witnesses said that it was conceivable that a pregnant woman through the course of her pregnancy could get three shots. The first one in the first trimester, the second one a month or so later, and the third booster just before delivery. They also talked about the correlation: I think it was a statistical correlation between the number of vaccine injections you got compared to the risks. Again, I’m guessing that you haven’t looked at those numbers.
Dr. Denis Rancourt
Every time I read the safety evaluations done by the pharmaceutical industry in order to get these vaccines approved on an emergency basis, it’s like I’m in a nightmare. It’s incredible. That’s all I can say.
Well, one of the other things that came out in previous testimony was that doctors were seriously promoting the vaccines to pregnant women. We also heard that one of the reasons for that is they said pregnant women were susceptible to COVID. We also heard there were no or few studies confirming that pregnant women were. I’m wondering if you looked at the reported mortality rate of women in pregnancy. There are statistics available from Statistics Canada that report— I think the incidence of death prior to COVID in pregnant women was one in 15,000.
I was wondering if you looked at whether there was any kind of increase in that.
Dr. Denis Rancourt
Well, again, that’s a specific area of mortality, if you like. Very targeted. And the numbers are small compared to an entire jurisdiction or by a whole age group. So I don’t have the resolution to look into those things by the methods that we’re using. But I have to say the following thing: When they justify these dangerous medical interventions on the basis that you may get COVID or that you’re susceptible to getting the so-called COVID, you have to ask yourself, what the heck are they talking about? Because my all-cause mortality data, which is absolutely robust, suggests that there was no particularly virulent pathogen on the planet. It did not happen.
Now, clinicians and emergency people are wearing glasses where they look for it because they’ve been told. And they’re seeing all kinds of things that they would see at other times if they’d been told the same kind of thing. So they’re wearing their COVID glasses; everyone’s getting kind of crazy. But in the end, there were not people dying in the street in most places. There was no particularly virulent pathogen. People have to grasp that. There is no fundamental reason to do anything special. And this is now a completely firm conclusion.
I mean, if mortality cannot be used to draw this kind of conclusion, then we’re living in a mad world where whatever they say is true. It’s all about whether there was something that happened on the planet that killed people. I can look at all-cause mortality and I can see an earthquake in Chile. I can see a heat wave that lasted three days in Paris. I can see a World War. I can see the Vietnam War. I can see an economic downturn as causing an increased mortality. I cannot see any of the previously declared pandemics after the Second World War. They’re not there.
So we have to reset our thinking and start to recognize that the virologists have been exploiting us and have been screaming fire where there’s not really anything present, as far as I can see. If we can’t go back and look at the actual data of who’s dying, where, and when, and what does it correlate to, then we can’t do anything.
That kind of leads me into my next question. And that is, you were talking about estimated vaccine deaths in Canada and you put that number—estimated around 30,000 people. Being a statistician and interested in history, can you tell me when was the last time something happened in Canada that caused 30,000 deaths?
Dr. Denis Rancourt
Well, it sounds like a lot, but it’s not a lot when you look from the perspective of all-cause mortality. In the sense that there’s a seasonal variation and every winter far more people die than in the summer. So on that scale—and also the amplitude of that seasonal variation has been decreasing historically since the Second World War. It was much higher and it’s been decreasing. It follows the health status of the population, the age of the population, but also the living conditions of the population.
It is dramatic to see in European countries, for example, how big it was just after the war and the very gradual decrease.
And then, beyond just the age structure changes, you can actually see large economic downturns and a shift in economics if you like: you can see that as a gradual increase in all-cause mortality. These are big effects. And so even though 30,000— From a forensic point of view, they killed maybe approximately 30,000 people. We’re refining that number. They definitely killed people. This was a huge crime. But in terms of the scale of the mortality for the whole nation, it’s maybe not that great.
Well, perhaps I asked that question in the wrong way. Because you have an overall excess in mortality. And a portion of that in your presentation you said would have been caused by some of the measures, some of the other effects of the measures, and some of the vaccines. So in an all-cause excess mortality over that period of time, what was that number, plus or minus?
Dr. Denis Rancourt
Yeah, I think the mortality in Canada— Roughly speaking, because we’re still doing the analysis for Canada in detail, I think even though the mortality on a per capita basis in Canada is much smaller than the U.S.—factor five, okay—the situation is similar to the U.S. in that there was a lot of mortality before the vaccine rollout. And then there’s a lot of mortality that continues after the vaccine rollout, and there’s a good portion of that mortality that’s directly due to the injections.
I think that is generally true in Canada, as well. Like the mortality of young men in Alberta happened before the vaccine rollout. And the higher mortality in certain provinces is before the vaccine rollout. And that continues, there’s still that trend. So the strongest evidence we have is when you get an actual peak in an unusual place that is directly synchronous with a rollout of a booster or a dose or something like that. It’s similar in that sense to the U.S. But Canada is unique in that 2022 is a high mortality year compared to the previous two years. And there are only about 10 or 20 countries that are like that. Canada is one of them.
You talked a little bit about Statistics Canada and we had witnesses in previous hearings that didn’t use Canadian numbers. They talked about Australia, they talked about United States, they talked about Germany. And what they said was that the statistics available in Canada are not there and they are delayed significantly. For instance, Statistics Canada still has not released final numbers on mortality for 2021.
Did you experience issues with getting the detailed mortality numbers in Canada that this other researcher had?
Dr. Denis Rancourt
Yes. At the beginning of the pandemic, where I wanted to get to work as soon as possible, it was very hard to get good mortality data for Canada—even though many other Western countries, including the U.S., were putting them out very quickly. We wrote to the people responsible in Canada and basically, we shamed them. We said, “Look, here are the other jurisdictions. This is what they’re doing. We’re supposedly in a pandemic here; you cannot not put this data out.” Within a month, we started getting data. I’m not saying we directly caused that, but we were among those that voiced very serious concern about their slowness. But they remain slow and behind many European countries and the U.S.
For example, I showed today the year 2022 for Canada. Well, that came out a few days ago, but it’s been out for quite a while in most other places. So it’s slow. And there are certain provinces on a by-province basis that really lag behind others. Manitoba is a good example. They’re very slow. We still don’t have Manitoba’s data. That should be up to date. It’s far from it. So when we make comparisons between provinces, we have to leave Manitoba out just because it’s so darn slow to get the data. But the national data is reliable and it has just come out for 2022.
But most other jurisdictions in the Western world have many months more data. So Canada has been slow and I don’t understand why that is.
Mr. Buckley, after testimony previously, did we not send out an invitation to the head of Statistics Canada or a responsible person in Statistics Canada to attend these meetings and discuss this with us?
Commissioner, I can’t say from memory if we specifically sent one out to a Statistics Canada person, but I can get that over the break and report back.
I appreciate that. Also, Doctor, your statistics deal with what has happened. And when we’re talking about vaccine deaths, is it not reasonable to assume that if you’re giving vaccines that are causing immediate death, there is a strong possibility, or a possibility that long-term deaths will continue to accrue because of that? And of course, we have no idea what that will be in the future.
Dr. Denis Rancourt
Yeah. In terms of answering that specific question, a more powerful approach is to look at adverse-effect monitoring. In one of our papers, we analyzed the VAERS [Vaccine Adverse Events Reporting System] database. In that study, which is among the studies that I’ve given you, we showed that there is an immediate peak that lasts a few days right after injection. And then we showed that there’s an exponential decay, from the time of injection, of death that lasts at least two months. You cannot have an exponential decay with such an unusual decay time if it’s not causally connected to the injection. We’re sure of that.
Now, there are probably all kinds of other physiological effects and they may last a long time, and people are talking about accelerated cancers, and so on. What I can tell you from all-cause mortality is that there are many jurisdictions that from the time of the initial vaccine rollout, and where they’re maintaining the vaccines, you enter a regime of higher mortality. It’s a very definite regime of higher mortality. The summer troughs don’t come back down to where they should be. It stays high. That is the case in Australia, Israel, many jurisdictions. So there are clean countries like that that allow you to conclude that there’s probably a long-term resistant effect on the death.
And you have to appreciate that from jurisdiction to jurisdiction, it’s extremely complex. The populations are different, the treatments are different, the pathogens are different, everything’s different. For example, it was a nightmare— We spent years analyzing the U.S. data just trying to understand why each state is so different. Finding the correlations that we did was a lot of work. It’s going to be almost impossible from all-cause mortality to say that there are deaths induced by the injections that are a year later, for example. I think that’s going to be impossible to say from all-cause mortality.
So I think there, you have to rely on autopsies and things like that.
I guess what we’re saying is we’re never going to know because, as we’ve heard from testimony, certain jurisdictions have forbidden autopsies on COVID-19 death patients. When deaths go up in the long term in different areas— Of course, it wouldn’t be a year later if there were rapid cancers or something else. You see that happening now, they wouldn’t necessarily relate it to the vaccine. You would think that that would still be reflected in an increase in all-cause mortality, but I guess, due to the complexity overall between jurisdiction and jurisdiction and province to province and city to city, we’re never going to know the answer to that.
Dr. Denis Rancourt
That’s true, but I would add something else. When you study all-cause mortality, you quickly realize that it’s a very robust feature of a population. It’s really hard to get more deaths than usual. You have to have an earthquake or something really special. So you generally have a certain amount of death per population given the culture and the health status and everything. And that’s very robust.
So any of these excess mortalities that we measure, that means something very dramatic is going on. And like I said, we’ve seen economic depressions, wars. We’ve seen those things directly. Everything that we see related to COVID looks like a societal transformation that was imposed. That’s what we’re seeing. It’s really about the measures, including vaccines.
This was an assault against people, and it killed many people. That’s really the conclusion.
I can’t see how to get around that.
And the other big thing, which a lot of scientists have a hard time wrapping their heads around, is there was no especially virulent pathogen. There is no evidence of the spread of a viral respiratory disease. In fact, there is counterevidence that disproves that that could be the cause.
Scientists have got to look at our data and wrap their heads around that because many of their sentences start with, “They got COVID, we had to do something.” “There is a high probability of being infected.” “Which are the populations that are most at risk from getting COVID?” This kind of thing.
That thinking has to be reset. Otherwise, we’re never getting out of this and they will keep doing this whenever they want. They will declare pandemics whenever they want. And they will assault the population in these kinds of ways anytime they want, if we don’t start resetting it. The way to reset it is to use hard data that cannot be disputed— And that is mortality data.
My last question. I wouldn’t ask this of any other witness. And the reason I ask you this, Doctor, is because you are not only a statistician, you are a physicist, and that means a particular thing. Physics is a very fuzzy thing and you need to ask all kinds of basic questions and understand where you are going. You are a professor of business, as I understand as well, so you look at a broad range of things and causes.
And so my question to you is this: Why? Why did they do this? Why did they potentially cause the deaths of millions and millions of people worldwide? And I know the statistics numbers don’t give you this answer, so I’m boxing you into a corner here, Professor. But I’m asking: when you were looking at these numbers and you were seeing these conclusions, you must have asked yourself, or your team must have asked, “Why?” I would like to know what those discussions may have been. If you’re comfortable—
Dr. Denis Rancourt
I don’t know if I’ll let you into that room.
One of the articles I included in my book of exhibits is an article I wrote in 2019, which is about geoeconomics and geopolitics since the Second World War. I believe that that really gives the proper analysis framework to answer a question like that. I believe that this was a military rollout of an injection. I believe that it was— They wanted to be able to practise and demonstrate that they could inject everyone. Many commentators have said, “Why would they want to inject people?” And injections are the most powerful bioweapon in the sense that you don’t have to rely on transmission of a disease which could affect your own people and this kind of thing.
You’re directly injecting the body of a person with something. That is a very powerful thing to be able to do. For the military to be able to roll out injections of an entire population is a very powerful thing. You can target certain groups, you can target certain jurisdictions, you can do whatever you want if you have a way of injecting everybody, in a military rollout, very quickly.
Now, therefore the injection itself can be a weapon. But also, it can be the antidote to a bioweapon. So it can be a way of providing an antidote to your population of a bioweapon that you have released. We’re talking about biowarfare. In my view, this was an exercise in biowarfare. And the Russians have said that plainly. They have talked about the bio labs that are in Ukraine that they have now taken and have all the data for. They have talked about what’s going on here. And they have given historic examples of bioweapons and what it looks like when they’re used.
So this has been analyzed by other countries. Our media don’t talk about it. But in my view, geopolitics did not disappear like they’d like us to believe. Geopolitics has been continuous and is the biggest wheel that drives the world. When you analyze it, you see what happened when the Bretton Woods Agreement was withdrawn from by the U.S. unilaterally. You see what happens when the USSR dissolved. You see huge tectonic shifts
in the economic structure of the planet and how populations are affected by that in Africa, Latin America, elsewhere—including Europe, Japan, and Canada. Canada had many social economic transformations as a direct consequence of the dissolution of the Soviet Union, and the accelerated so-called globalization that occurred immediately afterwards.
So these are the big trends and war is a big part of it. The Pentagon has said it is going to destroy China in the next 10 years. The biggest part of the Pentagon budget right now is to encase and encircle and isolate China. There’s a military base and naval presence like we’ve never seen before around a country that is thinking, what do we do and how do we defend against this? And can we sign an agreement with Russia to have these supersonic weapons that destroy ships? This is the level that these people are thinking at right now. So, COVID is just part of that. It’s just part of that. In my view. That is my view. I’ve dared to talk about it. I’ve analyzed it from my perspective, but it’s just an opinion.
Dr. Rancourt, I’d like to thank you for that testimony, your courage to give that testimony. It gives a perspective to this whole thing.
Folks sitting in the audience, including myself, when you’re watching the numbers—and I have a numerical background, it has a certain meaning. But when you put it into human terms the way you just did: that’s probably the first- or second-most chilling thing I’ve heard in the last 20-some odd days of testimony. Thank you, sir.
Dr. Denis Rancourt
Denis, there are no further questions from the commissioners. So on behalf of the National Citizens Inquiry, I sincerely thank you for coming and testifying today.
Dr. Denis Rancourt
It was my pleasure. It was my honour.
Final Review and Approval: Jodi Bruhn, September 6, 2023.
The evidence offered in this transcript is a true and faithful record of witness testimony given during the National Citizens Inquiry (NCI) hearings. The transcript was prepared by members of a team of volunteers using an “intelligent verbatim” transcription method.
For further information on the transcription process, method, and team, see the NCI website: https://nationalcitizensinquiry.ca/about-these-transcripts/
- Bachelor of Science, a Master of Science and PHD in physics from the University of Toronto
- Natural Sciences and Engineering Research Council of Canada (NSERC) international postdoctoral candidate in prestigious research laboratories in both France and the Netherlands
- Professor of physics at the University of Ottawa for 23 years, attaining the highest academic rank of tenured full professor.
Dr. Denis Rancourt, a professor of physics for 23 years at the University of Ottawa and the highest academic ranking, tenured, full professor, tells the Inquiry that he is an expert in five areas of science; nanoparticles, molecular science, statistical analysis, theoretical modelling and measurement methods.
Dr. Rancourt stated that he ran a large laboratory that conducted interdisciplinary research, using the methods of physics and mathematics to analyze problems in everything from environmental science to planetary science to theoretical physics to biogeochemistry, including interactions between bacteria and minerals in the environment. He was the lead researcher in a laboratory that developed techniques to study these problems and wrote more than a 100 articles in scientific journals about these questions.
Dr. Rancourt began by presenting his conclusions: “If governments had done nothing out of the ordinary, if they had not announced a pandemic, had not responded to a presumed pathogen, had done nothing other than what we normally do when we have a high season of mortality in the winter, then there would have been no excess mortality beyond the historic trend”.
“The measures that governments applied, definitely and quantitatively caused excess mortality in many jurisdictions. The final point is that the COVID-19 vaccination campaign itself, definitely caused excess mortality in definite peaks that are directly associated with various vaccine rollouts of different doses to different age groups and in different jurisdictions. There is no way to escape the conclusion that the vaccines definitely caused death in a significant number”.
Dr. Rancourt discusses the reasons for an uptick in all cause mortality in the USA in mid-summer and says they were due to poverty and health factors in particular jurisdictions and not a virus. If it was a virus, it refused to cross the Canadian/American border, which is impossible in terms of epidemiological theory.
The CDC agreed based on death certificates, that a co-cause of death in the great majority of the so-called COVID-19 deaths is bacterial pneumonia, especially in the southern United States. After the rapid rollout of the vaccine there is an increase of excess mortality by 60 percent in the age group 25-44 year olds in the USA.
Theresa Tam and her co-authors said in a scientific paper that if they had not applied all the measures, masking, distancing, vaccination, etc, that there would have been approximately one million extra deaths in the COVID period in Canada. Outlined in red in the graph “1M extra deaths vs reality in Canada”. Dr. Rancourt stated: “That is absurd, nothing known historically can cause that kind of mortality. This is ludicrous and this is the level of scientific propaganda that our government scientists are putting out these days”.
Excess mortality in Canada since the COVID period started in 2020 did not decrease whatsoever, because of this huge vaccination campaign. The vaccine was supposed to prevent illness and deaths. They have created circumstances where deaths are higher in 2022. A surge in mortality immediately after the pandemic was announced did not occur in several provinces. The most significant increase in deaths arose because of the third dose rollout. It is very hard to get good mortality data for Canada, because some provinces are very slow to release data. Canada is still being looked at but estimated between 10,000 and 35,000 deaths directly induced by the vaccine.
Immediately after the pandemic was announced peaks of high mortality occurred in very specific hotspots, synchronously around the world. From an epidemiological standpoint this is strictly impossible, because the time from seeding of an infection to the sudden and measurable rise of mortality varies by many months and even years. A map of Europe shows how the virus refused to cross borders and Dr. Rancourt explained when these specific hotspots occurred on a monthly excess mortality graph for Europe. Reckless prescribing of Hydroxychloroquine and various sedatives used along with mechanical ventilators are believed to have caused these hotspots, but the research into this is ongoing.
There is absolutely no doubt that the vaccines cause significant death. Autopsies show a relationship between the vaccination and its effect on the cells and organs of the body, and that would have caused death. There are many studies now showing that there are induced pathologies: meaning sicknesses, disease, related to having been injected. There is a risk from dying from the injection on a per-injection basis that is between 0.05 per cent for all ages in a western country, all the way up to almost 3 per cent for the most elderly fragile people. They calculated that the vaccine must have killed approximately 13 million people worldwide. Dr. Rancourt explained that the pharmaceutical industry trials excluded the vulnerable groups and those were the groups that were killed by the injections. That data was completely rigged.
In most countries, excess mortality is now dropping and coming back to normal. There are a subgroup of countries like Canada where in 2022, the mortality is higher than it was before. This is a concern for them that they are studying now in detail.
In Alberta when the energy sector shut down there was a very high excess mortality, this correlates with an increase in homicides, suicides and drug deaths. There were also more aboriginals working in that sector and they found that the highest mortalities among young men were in provinces with a higher aboriginal population.
The all-cause mortality data is absolutely robust and suggests there was no particularly virulent pathogen. There is no fundamental reason to do anything special, and this is now a completely firm conclusion.
“Everything that we see related to COVID looks like a societal transformation that was imposed. It’s really about the measures, including vaccines. This was an assault against people, and it killed many people. There is no evidence of the spread of a viral respiratory disease. In fact, there is counter evidence that disproves that that could be the cause”.
Dr. Rancourt concludes with his opinion of why he thinks this pandemic was launched.