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Chantale Collard
Good morning, Professor Denis Rancourt. For those of you who have just joined us, I’m Chantale Collard, a lawyer who is now a prosecutor for the Citizens Commission of Inquiry. Monsieur Rancourt, first of all, please identify yourself by first and last name.

Dr. Denis Rancourt
My name is Denis Rancourt.

Chantale Collard
All right, then. And I’ll swear you in. Do you declare that you are telling the truth, the whole truth, and nothing but the truth? Say, “I do.”

Dr. Denis Rancourt
Absolutely. I do.

Chantale Collard
Perfect. So Professor Denis Rancourt, I’ll provide a brief description [Exhibit QU-1a]. If, however, you have anything to add, please feel free to do so. So Professor Denis Rancourt, you have a BSc, an MSc, a Diploma in Physics and a PhD in Physics from the University of Toronto. You were an international postdoctoral fellow at the Natural Sciences and Engineering Research Council of Canada (NSERC), working in scientific laboratories in France and the Netherlands. You went on to become an NSERC University Research Fellow in Canada and a full professor of physics at the University of Ottawa, where you were principal investigator and professor for 23 years. You were also an interdisciplinary research scientist, publishing over one hundred papers in peer-reviewed scientific journals in many different scientific fields. Since the very beginning of 2020, you have published over 30 reports on COVID-related issues, and much earlier even, on masks.
Today, we’re going to focus on the results of your research. I believe you also have a PowerPoint presentation to make it easier for the audience to follow.

So first of all, can you tell us about the results of your research in relation to excess mortality during the COVID period, and subsequently, following COVID-19 injections?

Dr. Denis Rancourt
Yes, of course. I’m not going to show my slides just yet. I’m going to say a few words first. I’m going to tell you that if we’d done nothing-that is, if the government hadn’t reacted at all; if there had been no talk of a pandemic; if there had been absolutely no reaction, either in institutions or hospitals or in terms of government action-there wouldn’t have been any excess mortality anywhere. If we had done what we normally do, there would have been seasonal mortality as we’re used to seeing for over a hundred years of taking detailed measures. Nothing would have happened. That’s the conclusion I draw after three years of detailed study of mortality statistics, all causes combined.

Chantale Collard
Basically, you’re going to talk about excess mortality in connection with the measures. So there have been excess deaths.

Dr. Denis Rancourt
Yes, of course.

Chantale Collard
But it was not due to COVID, but instead due to the measures, as I understand it.

Dr. Denis Rancourt
So what I’m doing is studying all-cause mortality statistics. This means that we count the dead, we count the presence of a person who dies, we know their age, we know the place where they died and we know the date on which they died. And we compile these statistics on the scale of a nation or a province or a region or a city, and so on. And it’s this type of data that I analyze across several countries and around the world. We collect all the data we can, wherever we can, and analyze it. And on the basis of this analysis, which I’ve been doing in detail for a long time-and I can’t explain it all to you because there are too many of them, and they’re scientific reports of a hundred pages with lots of graphs, and so on-I’ve come to the following conclusion: The data prove that it couldn’t have been mortality due to a transmissible respiratory disease.

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It’s inconsistent with a viral respiratory disease because a viral respiratory disease-and this includes what’s known as COVID-when tested clinically, kills people with a risk that increases exponentially with age, with a doubling time of ten years. This is well known, as detailed studies show.

I’m not saying it’s not true. I’m saying that if we accept that the virus kills in this way, well, the excess mortality that we measure in detail and quantify, for example in the United States, is not correlated with age at all. So if I show you-and I’m going to show you later-the excess mortality in the United States, for example, by state; and I plot this mortality as a function of the number of people over 80 or the number of people over 65 or the median age of the state’s population, there is no correlation. Which is strictly impossible if this excess mortality were due to a respiratory viral disease, period-and above all, COVID, where clinical studies have shown that the risk of death is exponential with age. So we can demonstrate that mortality is not due to the transmission of a viral respiratory disease. No doubt about it. And I’m going to show you other types of data which establish this, which are really striking: maps on a European scale, and so on. That’s the first point.

Second point: The excess mortality we see, which occurs suddenly in mortality peaks following certain events, is directly associated and synchronous with measures taken by the government. So for example, at the very start of the pandemic, as soon as the pandemic was declared, there was a demonstrable spike in mortality as a result of treatment protocols in hospitals in the early months of the pandemic.

Chantale Collard
You are talking about March-April 2020. To situate us in time.

Dr. Denis Rancourt
Yes. So the pandemic was declared on March 11, 2020, and immediately from then on-I’ll show you some graphs-there was a very large excess mortality in certain hotspots. And this is further proof that it wasn’t a virus. It only happens in certain hotspots and is synchronous across the world wherever it occurs, which is strictly impossible for a virus that is spreading. It’s strictly impossible. I also do modeling research. Epidemiological theory shows that the time between the “seed,” as we call it, the first cases, and the rise in mortality, is a time that depends very much on the circumstances in the country, the cultural and institutional structure, and so on. It can’t be synchronous everywhere in the world; it’s strictly impossible if we accept what we know about the epidemiology of respiratory viral diseases. So there’s plenty of evidence that excess mortality is associated with things we can see directly. I’m going to show you some very striking examples.

And finally, my other important conclusion is that vaccine deployment directly caused immediate excess mortality. As soon as you deploy a dose of vaccine, there’s an excess mortality that can be measured and quantified. So we are, I think, the first research group to quantify this on the basis of all-cause mortality. And I’m going to tell you the result of this quantification; I’m going to show you the mortality risk per injection. And this risk increases exponentially with age. We’re the first to demonstrate this, and I’ll show you that we’ve proved it for several countries. And this means that we absolutely should not have given priority to vaccinating the oldest people. It’s the opposite of what should be done. The basic presupposition of those who want to inject us is that the risk of side effects doesn’t depend on age, it’s simply a risk, whereas we’ve shown that the risk of mortality increases exponentially with age.

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It’s very, very significant, and rises to very high values per injection when it comes to the elderly.

So now that I’ve told you my conclusions after three years of research, I’m going to show you some graphs that illustrate these points. I’ve prepared some slides that we can put on the screen now. So there you have it. This is to show that my detailed scientific expertise is in several fields that are relevant to the COVID study. For example, I’m an expert in environmental nanoparticles, nanoparticle synthesis, nanoparticle properties and nanoparticle characterization. This is very relevant because we say that viruses are nanoparticles, and these nanoparticles are the basis of vaccines. I’m an expert in molecular science, molecular reactions, theoretical and experimental molecular dynamics. I’m an expert in statistical analysis, error propagation, advanced Bayes-type statistical analysis. These are fields in which I have published scientific papers.

I’m an expert in theoretical modelling. I’ve modelled environmental phenomena and I’m now modelling epidemiology to show how classical epidemiology, as it’s promoted, can’t explain the phenomena we observe. So I’m an expert in modelling and I’m an expert in scientific measurement methods. So I’ve written articles to develop and advance techniques such as diffraction, different kinds of spectroscopy, magnetic measurements, measurements of all kinds, calorimetric, et cetera, and microscopy methods. And in my laboratory, I had an electron microscope, I had a nuclear spectrometer, I had these instruments; and I was the head of a laboratory that used these instruments to do detailed research on environmental substances, et cetera.

So all that to say that I have a lot of expertise that is directly relevant to these issues. I have a group; I work in collaboration with people I really like, including Christian Linard who joined us recently, and then there’s Marine Baudin, Joseph Hickey, Jeremy Mercier, John Johnson, who is a professor at Harvard University with whom we recently wrote an article comparing the effect of lockdowns in the United States. So those are my collaborators. The articles I base my work on are on my website, denisrancourt.ca. There are more than 30 articles in this field; they’re big reports and you can find them all. The vast majority of these articles have been translated into French. The translation is on the article page of my website, where you can find a link. I’ve prepared a book of evidence that’s almost 900 pages long, containing 20 of the articles most relevant to the conclusions I’m drawing today, which I’m making available to you as evidence [Exhibit QU-1].

Chantale Collard
Also available on the web.

Dr. Denis Rancourt
I’ve also made this book of evidence available on the web, yes, but I want it to be tabled before this Commission too. So those are the conclusions I’ve already described. I’m sorry, the slides are in English. The fact that there was no pandemic, et cetera, I’ve already explained.

Here, I’ll show you what all-cause mortality data can look like. Here we see mortality by month in the United States from the year 2000 until recently, and we can follow the seasonal variations of this mortality. We can see that there’s a dip in February, and that’s simply because there are 28 days in February. There are fewer days, so there’s less mortality. You can spot the Februarys here. This is to show you what it looks like when we do mortality by month for an entire nation like the United States. And you can see that the last group, in this sort of mauve, is mortality during the COVID period.

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So from the moment a pandemic was declared, mortality was much higher in the United States. And the mortality has a structure-has peaks-that is completely unusual. Normally, you can’t have peaks of mortality in the summer in a country in the northern hemisphere, but there were in the United States during the COVID period. We’ve explained and shown that this is only true in poor states, where there are lots of poor people, where people were killed in the summer, and we try to explain this in our articles. But that’s to show how mortality appears. And the black dots are the sum of all mortality over a period such as the COVID period versus the period just before that, but of the same duration, versus the period just before that of the same duration. So we can see the black spots: it’s the total mortality for a period that would be equivalent to the COVID period. We can see that there’s a big jump in mortality in the United States when we enter the COVID period. This is a very precise quantification of total mortality over the COVID period.

Chantale Collard
Professor Rancourt, I know you’re going to give us a very elaborate answer, but in general, the arguments one might say we hear are: “The population is aging, maybe that’s why it happened.” I hope you’ll respond to that.

Dr. Denis Rancourt
There isn’t a sudden spike in the number of elderly people who will die during the COVID period. There isn’t a bulge in the elderly population that, as time progresses, reaches the age at which they’re going to die, and then die suddenly. So the effect of age, for example, the aging of the population, will cause a gradual increase in this integral, this total mortality. But when there are sudden jumps, it can’t be, for example, the baby boomers or things like that. It has to be a sudden event that happens in the population when you do this kind of study.

Now, this is just to give you a sense of what all-cause mortality looks like. This is the same mortality for the United States, but seen by week and where the same integral is used. Here, the black dots have the same meaning, but here, we see in greater detail the mortality per week and we see the peaks I was talking about, which are very abnormal, and which I’ll describe in a few moments. And you should also know that this relatively gigantic mortality in the United States corresponds to 1.3 million deaths that would not have occurred had we not done everything we did during the COVID period: in the United States, 1.3 million more deaths!

Well, in Canada, there was almost none. The excess mortality during the COVID period in Canada is so small that it’s almost impossible to measure. We’ve quantified it and I’ll show you in a moment: it’s very small, and much smaller in proportion to the population. It’s not because there are fewer people. And so we would have to conclude that the virus refused to cross the border between the United States and Canada, which is completely absurd if we want to believe that it’s due to a virus.

This is further proof that it’s not a respiratory disease: because the border is several thousand kilometers long, with constant economic exchanges. It’s strictly impossible for there to have been a virus in the United States that killed 1.3 million people and virtually nothing in Canada. It’s strictly impossible in the context of respiratory viral disease theories.

So for the United States, there was this excess mortality, and it can be calculated on the scale of the 50 states of the United States. This is a graph of excess mortality in y for the entire COVID period as a function of the percentage of the U.S. population living in poverty. And here, we see that there’s a correlation: in science, we say that it’s a very strong correlation. There’s a coefficient called “the Pearson correlation coefficient,” which has a value of +0.86. A strong correlation like that is unheard of.

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And it’s not just a correlation, it’s a proportionality. That is, those who are used to looking at graphs like this will notice that it passes through the origin, meaning that in a state where nobody lived in poverty, nobody would have died due to the measures that were involved. And so this is another demonstration that it can’t be a respiratory viral disease. Respiratory viruses don’t attack poor people. They attack people who are old, vulnerable and have comorbidities, and that’s how they cause death. They don’t choose to kill people who are poor.

Chantale Collard
By the way, I’m sure you’ll be talking about the African continent, if we are considering poor people.

Dr. Denis Rancourt
That would be another topic, but not right now. So poverty has a very strong correlation in the United States with this excess death, as well as the number of people who are “disabled,” who are not functional due to severe mental illness. In the United States, there are 13 million people suffering from severe mental illness to the point where they can’t function in society on their own, and who have to be cared for by various institutions, and who are heavily medicated. So we have a correlation graph with the number of people per state in this condition, and there’s a very strong correlation there too. So the correlations we find between excess mortality and societal factors are: poverty, the number of people in this type of extreme misery-mental illness, et cetera-and average family income. If you make more than a $130,000 a year per family in the United States, you don’t die from COVID, period, according to the statistics we’ve studied.

So I’m not showing all these graphs but I just wanted to show this one, which speaks directly about poverty. So in the United States, there are a lot of people living in poverty and misery, I would say, caused by a medical system that gives psychiatric drugs to a lot of people on a large scale. There are many, many people who are in this misery, who are in very poor health, and that’s why there’s a very high mortality rate in the United States and almost none in Canada. This is the excess mortality for the ten most populous states in the U.S. by age group. So you see, age groups 0- to 24-years, 25- to 44-years, and so on.

And here we show the excess mortality expressed as a percentage of what the mortality would normally be. This is the period before we started vaccinating, so, this is the COVID period but before the vaccine was deployed. We can see that, even in that period, excess deaths by age group were of the order of 20, 30, 40 per cent in excess of normal mortality in those ten states, to give an example. And then, in the period when we started vaccinating, the same graph looks like this: we see that for the youngest, it goes up to 60 per cent for the 25- to 44-age group. So we see a change in the structure by age group when we start vaccinating people in the United States. It’s very measurable.

Chantale Collard
So this is the first dose.

Dr. Denis Rancourt
Here, we’re including mortality over the entire period from vaccine deployment to the final days of this study. So we were still vaccinating. This is the result.

But what’s surprising is that we’ve just explained the United States, but now we’re going to look at Canada. And what we see in Canada is the light blue curve. The light blue curve shows all-cause mortality per week in Canada from around 2010 to the present, essentially. You can see that there’s virtually no change.

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We’re entering the COVID period and there’s not really a big change. And what I’ve highlighted in red, and this will surprise you, is what the Canadian government is telling us, what Theresa Tam wrote in a scientific article: she said that if the government hadn’t done everything they did-the vaccines, the masks, the distancing, the lockdowns-then there would have been about a million more deaths in Canada. This graph shows the absurdity told to us by Theresa Tam and her co-authors. They claim that if nothing had been done, the mortality rate would have been this high. And the mortality you see on the screen, because the scale starts at zero in y, is an absurd mortality. There hasn’t been a world war, there hasn’t been an earthquake on a time scale that could be normalized, there hasn’t been any known phenomenon in history since these data were first measured that could produce such a high mortality.

Chantale Collard
Purely hypothetical.

Dr. Denis Rancourt
And Theresa Tam claims that, because of these measures, this great mortality we would have had is down to the level that is exactly what it would have been had we done nothing. In other words, they didn’t bring it down to half, they didn’t bring it down by 90 per cent to get to ten per cent. No, they lowered it to a level as if there hadn’t been a particularly virulent pathogen. We’re in this absurd situation. It’s what they’re telling us, what they want us to believe. And for a scientist like me, it is the realm of the absurd.

Here I’m taking the data for Canada and putting it on a scale where we look at it in a little more detail. And now, I’m doing this integral for a year-cycle; so I’m going from one summer to another to capture the mortality that tends to be higher in winter, to show the extent of the small increase that is nevertheless seen in integrated mortality for Canada when we get into the COVID period, and in the cycle after that too. So there is a small increase that we can quantify. On a larger scale, we can still see this small increase. And in Canada, we can also compare all-cause mortality with vaccine deployment. So in Canada, we can see that there’s a peak at a time in the seasonal mortality cycle when there shouldn’t be a peak, which coincides with the start of deployment of the first doses. And then, when the third dose takes place, that is, when there’s an acceleration in the cumulative number of administered doses, we see a peak in the winter of 2022 that’s much greater than all the other peaks on this graph. So we’re really seeing correlations in Canada of vaccination affecting mortality. We’ve analyzed this in more detail, but it’s just to give you an idea of what we’re doing.

This is an enlargement of what we’ve just seen: the correlation between mortality and vaccine deployment. The peak I’ve marked as C is a very strong peak in Ontario, especially for people aged between 50 and 65, and it’s exactly when vaccines were deployed in this age group. The peak referred to as D is a very thin peak due to a heat wave that took place in British Columbia at exactly that time. It’s well known that heat waves cause very thin peaks that last little more than a week. So we can analyze each of these mortality peaks. But the peak I’d like to illustrate in greater detail now, and you’ll be really struck by the result, is the peak I call Peak A: because the arrow pointing upwards, that’s the date on which the pandemic was announced, and immediately afterwards, there was this huge rise in mortality. So I want to analyze it and show you what this peak looks like. And I’m going to show you that there was such a peak, which was very, very strong in certain states of the United States, especially in New York.

[00:30:00]

So here we see this very, very strong peak. Here I have all-cause mortality per week for the states of Connecticut, Maryland, Massachusetts, New Jersey, and New York all combined. And you can see that seasonal mortality, when normalized by population, is always about the same, but this peak is very different from state to state. There were 30 states in the United States that didn’t have this peak. So it’s a virus that was attacking just some states, and very strongly.

The same peak occurred at the same time on the other side of the world, in Europe. And so here we see the same peak taking place in Lombardy in Italy, similar places in Spain, and so on. There’s also one in France. There are hotspots like this, where very thin peaks in mortality occur immediately after the pandemic is announced. And so when I wrote my first article about this peak, in June 2020, I said: “This is not a viral respiratory disease pandemic. It’s not possible for something like this to be caused by a virus. It must be caused by what you’re doing in the big hospitals in those jurisdictions.” And so, we’re going to look at what’s happening on maps, what’s happened in Europe with this peak, and you’re going to be amazed.

So I’ve got a map here, just to remind you where the European countries are. I’ve also marked in blue certain borders that I want you to look at-because these are borders that the virus has absolutely refused to cross. So from Portugal to Spain, it was impossible for the virus to cross; from Spain to the south of France, it was impossible; Germany was protected in its entirety and the virus didn’t penetrate Germany at all; the north of Italy was hit hard but it didn’t spread further north, and so on. Just like that, there were hot spots. In Sweden, there was a hot spot in Stockholm that never spread. So they killed a lot of people during the first two months of the declared pandemic, which didn’t spread. So that’s to show you where to look on the maps I’m going to show you.

So here it is. This is the first map: excess mortality in Europe for January 2020. And you see, the map is white because there’s virtually no excess mortality, everything’s normal. Everything is normal this January 2020 compared to all other Januarys in the past. If we extrapolate the historical trend, it’s the same mortality we’ve always seen. And now I’m going to February: same thing, no excess mortality for February 2020 in Europe. And here’s the mortality for March, the month in which the pandemic was announced. As you can see, the boundaries I pointed out have been respected. The mortality supposedly due to the virus has not crossed into Germany. Germany is a jurisdiction with a very low mortality rate, and you can see that the borders have not been crossed. And if I go another month, to April, we’re still in that early mortality peak and it’s still pretty much the same places; and the borders are respected, the virus isn’t crossing. And then, in May, it’s over. It’s a very thin peak that ends in May and in June, there’s none.

So this famous peak in the first few months of the pandemic did occur in Quebec, the province with the strongest early peak, and it occurred in hot spots. We were able to go to the regional level in France and identified counties where there were large hospitals where people died. So this mortality cannot be due to a virus. We think it’s due to what was done in the hospitals. Mechanical respirators in hospitals were very important because in Lombardy, Italy, they invented a way of putting two patients on one respirator machine. They were very proud of this: “We’re going to save everyone; we’re putting them all on respirators.” This partly explains the very high mortality rate in Italy at the time.

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I’m going to shock some people in the audience a little. But hydroxychloroquine, HCQ, is a very interesting molecule with beneficial effects but with a therapeutic window that is very well defined and relatively narrow. And when you go beyond a certain dose, it becomes lethal. And at the start of the pandemic-because a lot of researchers like Didier Raoult said, “Look, it’s useful”-well, people who didn’t know how to use it in hospitals in the territories used it a lot, but in a less supervised way, I think, than what happened in Marseille. There is a correlation between a peak in the use of hydroxychloroquine and this high mortality. And this peak can be seen in European countries where there are these mortality hotspots.

Chantale Collard
The places related to hydroxychloroquine are where the protocol had not been followed.

Dr. Denis Rancourt
Exactly, it happened where a protocol had been invented which was way too high by dosage and it certainly poisoned a lot of people. So there’s this correlation. A German researcher, Dr. Claus Köhnlein, was one of the first to suggest that: “Look, in Germany, we didn’t do that and there were no deaths. Wherever two grams or more has been used, there have been many deaths.” He had suggested this, and so we went into the statistics to see if there were any peaks in the prescription of these molecules. In fact, we’re in the process of identifying many molecules used in aggressive treatments at the start-because everyone was in a panic and so on-which are correlated with this high mortality.

And the final theme of my presentation is the high toxicity of vaccines in terms of actual mortality. So I’m going to talk about that. I’ll start by saying that there can be no doubt that vaccines are killers. Vaccines can kill people, can cause death. There are many lines of evidence. There are very detailed autopsy studies that demonstrate this and I quote from those studies. There are adverse event monitoring systems that show spikes in adverse events, including death, at the very beginning immediately after vaccination, and then up to two months later. The statistics show this very clearly and we’ve written an article on the subject. There is a study that was done in the United States by Mark Skidmore which showed that, on the basis of scientific survey questions in the United States, he had calculated 300,000 deaths due to the vaccine in the United States. We quantified the figure using our own methods and came up with the same figure. So that would mean that in the United States, there were 1.3 million excess deaths; and in that figure, there are more than 300,000 people whose deaths were caused by vaccines.

So that is one line of evidence. There are plenty of articles on pathologies that are induced by vaccines and there are more than 1,250 articles in scientific journals that speak about the damage that can be caused by vaccines. So I think, when you look at all of this, you have to conclude that it’s possible that the vaccine could kill people. Our task is to quantify that. How often does it kill people? And so that’s the autopsy studies. Now, we’re going to see if we can use mortality to quantify the risk of dying from the vaccine.

So the first article we wrote was on India because in India, it’s very difficult to get good data on all-cause mortality. Some researchers had published data but hadn’t noticed that there was a peak-but a huge one!-of mortality in India which, coincidentally, was exactly when they deployed the vaccine in India. All right? So in India, we were able to quantify that the vaccine definitely killed 3.7 million people. There was no excess mortality in India until they deployed the vaccine. There was no COVID in India; the data are clear, there was no excess mortality. And in India, they had what they called a “vaccine festival”.

[00:40:00]

The Prime Minister said, “Go vaccinate the most vulnerable people.” They made a list of 12 comorbidities and said, “Go get these people and vaccinate them.” Essentially, they encouraged people to vaccinate the oldest, most vulnerable people; and in a very short space of time, they killed 3.7 million people in India with their vaccine. We wrote a whole article about it.

Here, the graph shows Australia. We chose to study Australia because it’s another country where nothing happened in terms of excess mortality until the vaccine was deployed. They don’t say that in the media. There is no excess mortality in Australia except when the vaccine is deployed. And so, we enlarge this for Australia and you see the seasonal mortality and you see the deployment of the vaccine: you see that we’re entering a higher degree of mortality. You can see that there’s a peak. You’ll notice that in Australia, because they’re in the southern hemisphere, seasonal peaks in mortality occur during our summer, which is their winter. So it’s reversed. And then, during their summer, which is our winter, there’s a peak in mortality right in the middle, which you see here, which is very large, coinciding with the third dose of the vaccine, deployed very rapidly at that time. Without any doubt. Here, I have a graph showing the deployment of the vaccine, the number of doses administered per week, in black, compared with the peak in mortality at a place which holds the historical record for mortality in Australia, but where there has never been an excess of mortality or a peak in mortality-never in history.

And in Australia, people don’t die from a heat wave; it’s not due to a heat wave. I’ve traced all the heat waves in Australia and I’ve found that the most intense one caused a very small peak because in Australia, they’re used to being hot. So this spike is definitely due to the vaccine and it’s happening in every state in Australia. We can go through the states here: Victoria, New South Wales, Queensland, et cetera. So we have very clear data where we have mortality. There was no excess mortality until we deployed. When we deploy, we have a new scale of high mortality; and when we bring in yet another dose, we have a spike on top of that. So we can use this data to quantify how many people died per dose of vaccine administered. That’s what we’re going to do.

And so this is to show that it’s not just in Australia. This is Mississippi in the United States. You’ll notice that in Mississippi, there’s a huge peak in mortality-again in the middle of summer, that is, our summer, when there shouldn’t be any mortality in the seasonal cycle. Well, there is a huge peak, and it coincides with an acceleration in vaccination. But it’s not just any acceleration: it’s what was called in the United States “the vaccine equity campaign.” So “vaccine equity” was a vaccination campaign paid for by very influential financiers who spent tens and tens of thousands to hire lots of people to go and vaccinate vulnerable people who hadn’t yet been vaccinated. They caused this spike in mortality, but only in the poor states of the USA. People died in this vaccine equity campaign in states where there was a lot of fragility and a lot of poverty. So we spotted this peak, which coincided with an acceleration in vaccination due to the vaccine equity campaign in all the poor states of the United States. And that’s a phenomenon that has to be attributed to the vaccine.

And here again, we can quantify what this represents in terms of mortality. The mortality that took place in the poor states of the United States at that time has an equivalent risk to the mortality that took place in India, which killed 3.7 million people. This is the same risk of mortality in the poor states of the USA as in India. Here we see Michigan, a state in the north of the United States. In Michigan, there is an excess peak that occurs at the beginning, when the first doses of vaccine are deployed-a completely abnormal peak that is very similar to the same peak that occurred in Ontario.

[00:45:00]

So this is to show another example where the deployment caused sudden large mortality in an unexpected place.

So to sum up the question of vaccines, we-and we were the first to do so-wanted to quantify the risk of mortality due to the vaccine by age of the person receiving the vaccine. But to do this, we need to find data in the jurisdiction in question where they give us mortality by age group as a function of time, and also, vaccination for that same age group as a function of time. And when we find jurisdictions where we can find these data, we can make the calculation shown here.

So Israel and Australia have very good data, and that enabled us to make this graph. So this graph represents the risk of mortality per injection. It’s what we call the “vaccine dose fatality rate” as a percentage, as a function of a person’s age. We can see that there’s an exponential rise for older people, and we can see that the mortality risk reaches almost one per cent on this graph. This means that one dose in a hundred will kill a person of that age when injected-one dose in a hundred! That’s enormous. So we were able to prove this for the first time. We’re the first to have done this quantification.

Here, I’m showing on an enlarged scale what’s happening to young people. We can see that young people have also been killed by vaccines, the younger age groups, and that this mortality risk is higher than the exponential curve deduced for other ages. So young people have a mortality rate that is independent of age and higher than the exponential trend found for other ages. For those who are more used to looking at this type of graph, I’ve put the same data in semi-log and you can really see the exponential trend, the straight line. We can see, for young people, that we’re deviating significantly and that we’re remaining constant in the mortality risk. So there they are, the young people affected by the vaccine: that’s where we see them.

Finally, this is just to show what the data in Israel typically looks like. In black is the deployment of any given vaccine dose and in purple is all-cause mortality. We can see that when the vaccine is initiated, there is a mortality peak that is larger than the vaccination peak. When another vaccine is introduced, there’s another mortality peak and so on. But as the doses progress, mortality per injection is higher. And so there are a lot of curves like this for different age groups in Israel. That is the 80-year-olds and over, 70- to 79-year-olds. It’s just to show the shape of the type of data we’re analyzing. In the end, this enabled us to produce a summary graph showing the risk of death by injection as a function of age, but for the different doses received. So we can see that the first doses are not as lethal as the next ones and those after. Doses three and four are particularly lethal; and we can see that for the elderly, the higher the dose, the greater the risk.

Chantale Collard
And here, you have effectively stopped at four doses but there are others who have gone up to six or seven.

Dr. Denis Rancourt
At the time we wrote this article, that’s the data we had.

Chantale Collard
It can be inferred that-

Dr. Denis Rancourt
Ah yes, our studies continue in all directions. Many countries are now being studied. I will conclude with this last slide. To date, India, Australia, Canada, Chile, Germany, Israel, New Zealand, and the United States have been studied in detail. Many of these results have not yet been published but we are just about to publish them. The average risk of death following vaccination in Western countries, all ages combined, ranges from 0.05 per cent to-in the case of advanced doses-as much as three per cent for the most elderly.

[00:50:00]

That’s the kind of mortality risk you find. And when you use average values for all ages, you can calculate how many people would have died from the vaccine. So on a global scale, it’s 13 million people. In India, as we’ve demonstrated in detail, it’s 3.7 million people. In the United States, we’ve calculated-and we’re quite confident of this calculation-that 330,000 people would have died as a result of the vaccine. In Canada, we’re currently estimating and we’re in the process of refining our error calculation, et cetera. It’s more difficult in Canada because there’s less mortality, but we think that around 30,000 people have died from the vaccine. These are mostly very old people. We have the excuse of not thinking about the vaccine because we expect them to be frail and elderly. So it’s easy, perhaps, not to talk about it. These are deaths that are less visible, but which are nonetheless due to the fact that these people were vaccinated. And so vaccine-induced mortality is much higher than governments are prepared to admit.

Well, that concludes my presentation.

Chantale Collard
Professor Rancourt, I may have one last question. In fact, you have autopsy results. But on the other hand, we can see that the capacity to have autopsies conducted was rather hindered; people weren’t able to go that far. So what can we infer from the autopsy results?

Dr. Denis Rancourt
I’m not a pathologist; I’m not the person who does autopsies. I’m in contact with the researchers who do the autopsies. I talk to them and I look at their results and I ask for their help in interpreting what they see under the microscope and the tests they do, et cetera. But I know that, yes, we didn’t do as many autopsies as we should have; we should have done a lot more. But there are dozens and dozens of papers reporting very detailed autopsies which conclude that death was due to the vaccine-and more and more are coming out. So it’s typically family members looking for someone to do the autopsy. There’s a great German doctor who’s done several for family members and these data are starting to come in. Every month, there are new articles reporting autopsies.

Chantale Collard
They’ll keep coming out. And at the very beginning you answered the question we’re asking here for the benefit of the National Citizens Inquiry: So what could have been done differently? You answered, “We shouldn’t have done anything.”

Dr. Denis Rancourt
Exactly. What we had to do differently was to do nothing. If we hadn’t invented this pandemic- I mean, sure, there are always pathogens present; sure, there’s a whole ecology of pathogens; sure, people get sick and get better all the time, that’s not the question. The question is: Has there been excess mortality due to a particularly virulent pathogen? And my answer is: absolutely not.

And one thing I haven’t said is that in the United States, where there have been so many deaths, the CDC admits that, of the deaths they attribute to COVID, more than half of these people also had bacterial pneumonia, which is noted on the death certificate, in a country where they stopped prescribing antibiotics, okay? You need to know that in Western countries, antibiotic prescriptions dropped by 50 per cent during the COVID period and it’s stayed that way. I would argue that this is certainly not an accident. There have been suggestions from agencies to stop prescribing antibiotics; and so the poor people who have died in the United States are also the same populations who are normally prescribed a lot of antibiotics because they have a high susceptibility to suffering from bacterial lung infections. And so this same population that- Normally, when you look at a map of the antibiotic prescriptions in the United States, it’s red in the poor southern states. Well, we stopped prescribing antibiotics to these same people. They had bacterial pneumonia, and it’s largely this population in the United States that has died.

[00:55:00]

So in terms of mechanisms, we’ve been able to identify this in our articles.

Chantale Collard
Professor Rancourt, I will let the commissioners ask you questions, if they have any.

Commissioner Massie
Thank you very much, Professor Rancourt, for your brilliant presentation, which is rather frighteningly dense. Fortunately, I had read a little of it beforehand, which helps, but I still have several questions. I’ll start with the last one so as not to forget it. When you extrapolate the deaths due to vaccination in Canada, you’re estimating, on the basis of averages that have yet to be refined, around 30,000. I note that in the United States, you estimated around 330,000?

Dr. Denis Rancourt
Yes, our estimate for the United States is more refined and better. So from one country to another, the error in this estimate may be greater or lesser. For India, we’re absolutely certain of 3.7 million. In Australia and Israel, we know in such detail that we can talk as a function of age and of dose. So there’s a great deal of certainty there. But what’s astonishing is that, when you go from one country to another-and now we’ve done a lot of countries, I’d say over 50-you always find the same risk per injection, more or less; we’re always in the same range. And when you take particular peaks, if you don’t just take the vaccination period, but if you take peaks and associate that with doses given at the time, you still get the same mortality risk. Do you see what I mean?

Commissioner Massie
Yes.

Dr. Denis Rancourt
So we’re very confident that’s a robust number.

Commissioner Massie
My question was that you had presented earlier that the excess mortality, all causes combined before vaccination- Well, when we looked at the measures that had been deployed before vaccination, what we observed in the United States compared to Canada was that the difference was not proportional to the population. And here, you put forward the idea that, in fact, the population or the proportion of poor and vulnerable people in the United States being much greater, it’s probably these target populations that have suffered more. And I thought I understood from your presentation that the more vulnerable people are also going to be the same people who are going to suffer more from vaccine injuries in any case-are likely to die from vaccination. And here, the ratio seems in any case to be within the margin of about one in ten, which corresponds to the proportion.

Dr. Denis Rancourt
Yes. I’d say, at this stage, looking at the data and all that: I gave 30,000 to give an idea for Canada. But in our final analysis, there’s going to be a margin of error, and it’s going to fall, I’d say, between 10,000 and 35,000. It’s going to be in that range. So there’s a lot of uncertainty about the estimate for Canada because we’re still in the early stages of analyzing the data, but it was to give an idea for the Canadian audience.

But, you see, when we went looking for the vulnerable in the United States with the vaccine equity campaign, the injection mortality rate was as high as in India. So we were in the one per cent range in those age groups, which aren’t even the oldest. But when we look at Australia and Israel for all ages, we find exactly the same figure-0.05 per cent-and our first estimate for Canada is still in the same ballpark. So I tend to use that figure to make this calculation, and that’s the figure I used for the United States, so I used the same proportion.

Commissioner Massie
My other question is that an all-cause analysis requires fairly precise figures on fairly large populations if we want to arrive at estimates. For example, in the case of vaccine-related deaths, there was at one point an episode in Quebec when the government wanted to launch vaccination campaigns in senior citizens’ residences in a rather, I’d say, sustained manner. And there were even articles about it in La Presse.

[01:00:00]

I saw a scientific article published almost a year later that recounted this episode and mentioned that they had slowed the pace a little because they found it was particularly aggressive. Can we do any studies on this, given the population and the event or incidence that happened?

Dr. Denis Rancourt
With the methods I use, all-cause mortality, I can’t quantify these things, but these are cases of specific institutions and we can get precise figures. And there are European countries that have noticed the same thing and have issued warnings not to vaccinate the elderly without a thorough clinical analysis. So they went too far at first in several countries, but we can see from what they said publicly that they then made adjustments. Some countries have noticed that the risk increases exponentially with age. They’ve noticed it; they’ve seen the consequences of vaccination in the elderly, there’s no doubt about it; we can see it in these governments’ communications.

Commissioner Massie
Finally, my other question concerns certain environmental factors. I’m sure that your studies have tried to make other correlations apart from those you’ve shown-and in particular, when we look at the period in which we deploy, for example, the second or third or fourth dose. Given that we know from studies carried out by people involved in vaccination that it is contraindicated to administer a vaccine to a person who is infected or who has just been infected, so as not to cause overstimulation, when we see the increases in toxicity as a function of dose, isn’t there a part of this that could be explained by the fact that we know that the Omicron wave was particularly abundant, according to the studies we’ve seen? Wouldn’t vaccinating a third or fourth dose at that point increase the problem?

Dr. Denis Rancourt
Here, we cross into the realm of immunology theory. So I’ve made a conscious effort to avoid venturing into that territory. I’ve always adhered to all-cause mortality data, to the mathematical correlations I can establish, and to a calculation of error in making this statistical analysis. And I’ve refused to go into that territory, to talk about the mechanisms, what could cause it. But, for example, when I find that more advanced doses are more lethal, we have to be careful because often, it’s in jurisdictions where advanced doses have been more directed at the elderly. So when we don’t distinguish by age group, we can, in the all-ages data, be wrong in a certain sense. It may appear that the dose is more lethal, but in fact, this is because more vulnerable people have been vaccinated.

And so when the data allow it, I can discern things. When I can’t, I have to admit that it’s a possibility. But I understand your question and to answer it, I’d need to have data on the level of infection of people who are injected, and I’d have to believe that these data are reliable. And so, as I’m not ready to have data by age groups at the level of the jurisdictions I’m interested in, and as I have absolutely no confidence in the assessments as to whether the person is infected or not, because we’re in the dark-are we talking about symptoms, which symptom, et cetera? Are we talking about PCR tests? That doesn’t mean anything. So my approach was: “I don’t want to know anything about all that.”

I mean, when they announced the pandemic, you’d see people lying dead in China, then you’d see people falling down, and they’d say the same thing: “The hospitals are full.” But personally, the first thing I did was to go outside, then I looked to see if there were any dead people in the street and I didn’t see any. Okay? And in other words, what I did was I immediately went and looked for all-cause mortality data to see if there was any increased mortality. And there wasn’t!

[01:05:00]

There were just hot spots with peaks, like in Quebec, New York, London, Paris, and northern Italy. That’s what there was, but there was nothing elsewhere. In a study we did with John Johnson of Harvard, we compared states in the U.S. that were next to each other, that shared a border, that were very similar. One state did a lockdown and the other didn’t, and we found 12 pairs of states that we could compare directly like that. And we systematically found that the state that imposed a lockdown had a much higher all-cause mortality than the other.

And so all this to say that there was no excess mortality where we didn’t attack people and we didn’t kill people in hospital at the beginning and we didn’t do the lockdowns later. There weren’t any. That was the response I got to the suggestion that people were going to die everywhere, et cetera.

All-cause mortality is very powerful. I can look at mortality in Chile and tell you what day there was an earthquake. I can tell you what day there was a heat wave in northern latitude countries. I can tell you about the aging of the population, I can tell you about the wars that have happened. Do you know, I studied all-cause mortality in detail, and I looked for the pandemics that were announced by the CDC to see if I could find the number of deaths they said had occurred: Will I see them in all-cause mortality? I couldn’t find them.

None of the modern pandemics since World War II has produced a signal that can be detected in all-cause mortality. I’m not talking about COVID; I’m talking about the pandemics that have been announced since the Second World War. There haven’t been any. As far as I’m concerned, there’s no excess mortality. So what are we talking about? Why are we making such a fuss and showing people how to blow their noses and telling them to wear masks and do tests, when on the scale of a country like the United States and in all the countries we can study, these pandemics have not caused excess mortality. What are we talking about? While on the other hand there are real phenomena that cause mortality: war in particular. You can see the Dust Bowl in the United States in the 1930s, economic crashes: you immediately see the mortality. There are major social phenomena and structural changes that cause mortality. And I say that what happened during COVID was exactly this kind of attack on the population, as if there had been a meltdown in the economy. The population was affected in the same way; and in the United States, that’s what caused the deaths.

Commissioner Massie
I’d like to ask you, given the power of this approach: How many people are there who have the capacity to carry out analyses like you do and like Pierre Chaillot does, who recognize that it should be a practice that should be widespread in all governments so that we can precisely understand the phenomena we face? Is there a desire to move towards this kind of analysis or do we prefer, for the time being, not to practise it?

Dr. Denis Rancourt
Statistics Canada does analyses of mortality, birth rates and all that sort of thing. There are many experts who do this. There is no lack of technical knowledge to do so. What’s lacking is the motivation to really be honest and report what we see, what the data want to tell us. Mortality is very simple to understand. Once you get used to it and you can spot the kind of things that can cause mortality, you get used to it very quickly.

But you know, since I started working in all-cause mortality, my biggest job and my biggest frustration has been trying to get my scientific colleagues to understand that we have to look at all-cause mortality and stop talking in circles about all kinds of things and start by seeing if people are dying. Who’s dying, where are they dying, and why are they dying? And let’s leave aside all the theories and all that.

Personally, I get frustrated with my colleagues because I’m in several discussion groups with researchers and I’ve had all the trouble in the world getting them to understand. It took me three years, and now they’re starting to understand. They say, “Okay, so we’re going to analyze all-cause mortality; Denis, could you do it?”

[01:10:00]

Well, that’s where we’re at. But, you know, the education system is very faulty. We train very specialized people and we don’t place any importance on clear, robust, direct thinking.

So the scientific researcher wants to apply his theories and his way of seeing things to his field but doesn’t ask himself the question: What would be the best way to tackle this problem? Which expert should be called in? What do I need to learn to understand this phenomenon? They don’t ask themselves that question. Instead, they ask themselves: How am I going to apply the theory I’ve learned to say something about this phenomenon? And that’s a big problem in our society. There’s a shortage of thinkers.

Commissioner Massie
I will ask my colleagues: Ken, you have any questions you want to ask? Anyone else? Okay. I have more, but we will move on.

Chantale Collard
Professor Denis Rancourt, thank you. Your testimony is truly invaluable. You’re talking about major issues; you’re talking about all-cause mortality as much as post-vaccination mortality. And let’s hope that your research will be widely disseminated. Thank you.

Dr. Denis Rancourt
Thank you.

[01:11:37]

Final Review and Approval: Erin Thiessen, October 31, 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, and further translated from the original French.

For further information on the transcription process, method, and team, see the NCI website: https://nationalcitizensinquiry.ca/about-these-translations/

Summary

Scientist and researcher Denis Rancourt presents a detailed analysis of the causes behind excess deaths during the Covid-19 pandemic. He discusses the various factors that contribute to excess mortality, including the effects of lockdowns, the overdiagnosis of Covid-19 cases, and the misuse of respiratory equipment. Through his research, Rancourt sheds light on the complex nature of the pandemic and offers valuable insights into the most effective ways to manage it.