Pierre Chaillot, témoin expert et statisticien, éclaire la manipulation des statistiques liées à la COVID-19. Il discute de la manière dont les données peuvent être manipulées pour présenter un récit spécifique, et comment cela a été fait tout au long de la pandémie. En utilisant des exemples spécifiques et des données, Chaillot démontre comment ces manipulations peuvent fausser la perception du public de la pandémie et de ses effets. Si vous souhaitez comprendre l’importance de l’analyse statistique et son impact sur la prise de décision lors d’une pandémie, cette vidéo est incontournable.
Yes, so, hello; my name is Chantale Collard. I am the acting prosecutor for the National Citizens Inquiry today. And Monsieur Chaillot, I don’t know if you’re online. So hello, Monsieur Chaillot.
So first of all, we are going to proceed with identification. Please state your name.
My name is Pierre Chaillot.
All right. And also, for the purposes of the Commission, I must swear you in. Do you solemnly declare to tell the truth, the whole truth? Simply say, I do affirm.
I do affirm.
Perfect. So Monsieur Pierre Chaillot, if you don’t mind, I’m going to introduce you briefly. And if I make any errors, don’t hesitate to correct me. So you have training as a statistician at ENSAI, the National School of Statistics and Information Analysis. You have also obtained a degree in mathematics from the University of Rennes 2 and you have been a statistician since the start of the COVID crisis.
Every week, you have scrupulously collected all the official data available from the Eurostat, INSEE [National Institute of Statistics and Economic Studies], the DREES [Directorate for Research, Studies, Evaluation and Statistics], and various ministry websites. You also won the 2007 INSEE public statistician competition. You have attended engineering school and worked for ten years at the National Institute of Statistics and Economic Studies. On the INSEE website, there are posted around 20 studies in which you have participated. But since 2020, you became interested in this COVID crisis as an ordinary citizen.
You have also anonymously written many articles transcribed into video, notably on your YouTube channel, without claiming authorship. It’s also important to mention that you’re not making money with this civic activity, neither from your YouTube channel, where there is no publicity, nor from your articles, which you offer freely on internet platforms. And you are the author of the book: COVID-19, ce que révèlent les chiffres officiels: Mortalité, tests, vaccins, hôpitaux, la vérité émerge [COVID-19, What the Official Figures Reveal: Mortality, Tests, Vaccines, Hospitals, the Truth is Emerging] with the royalties being paid to the association: Où est mon cycle? [Where is my period?] Correct?
So Monsieur Chaillot, you are going to tell us about the results of your research. I believe you also have a PowerPoint that we can share on the screen.
Yes. So first of all, you are going to tell us about the deaths. So to the effect that there was no mass mortality event [hecatomb], can you explain this to us?
Yes. For my purpose, what I would like to explain to you today is that statistics are of no interest in themselves. A statistical figure means nothing. A statistic is a tally, and to understand the statistic, the number doesn’t matter. We must first understand what we have counted. What is most important in statistics is to know what has been counted and how it has been counted.
And so it is the person who decides what we are going to count and how we are going to count it who has already determined what the final statistic will be. And what I show in my book is that all of the statistics labelled COVID-19 are not scientific at all. They are nothing more than the result of bureaucratic counting decisions. Therefore, anyone who uses statistics labelled COVID-19-whether it be of cases, hospitalizations, or deaths-to make it look like they are doing science are not, in fact, doing science, and are creating nonsense, producing nothing usable. And so the book tells how we experienced statistical fraud throughout this period.
And indeed, I start with the deaths because it’s the most important element. It’s important to show that, statistically, absolutely nothing has happened from the perspective of deaths, since the whole world has forgotten that it is necessary to take into account the age of people before starting to speak of deaths. Obviously, the number of deaths in a country corresponds first to the size of the population. The larger the population, the more deaths there are; and after that, it is the age of the people that counts.
And for example, here you have the number of deaths in metropolitan France, which says-and I carried out this exercise for all the European countries for which I had data-where we have seen the number of deaths each year since 1962. And we have institutions that cried in horror when, in 2020-which we see here-there was an increase in deaths, saying that it broke the record number for deaths, which was true. But the previous record was set in 2019, before that in 2018, et cetera. There are more and more old people in France, and it is normal that more and more of the population is dying. And to illustrate this, you have to look at what is called the age pyramid.
The age pyramid represents the population in a country according to age. Here we are in the year 2000 in France: 20 years ago. The age pyramid is this. Each bar represents a share of the population in France, and it is by age group. There are the 0- to 4-year-olds below, 5- to 9-year-olds above that, 10- to 14- year-olds, et cetera. We go up to the over-90s and we put the men on the left in blue and the women on the right in red. And we see that in the year 2000, there is a big gap that begins at around 55 years; and we see this hole which represents the people who died or who were not born during the Second World War. The Second World War left a lasting impression on history in a very marked way for more than a century. And below that gap there are those under 50 who were called baby-boomers-the baby-boomers born from 1946 in France, Europe and Western countries. There were a lot of births; and therefore, that makes up the people who were under 55 years old in 2000.
And therefore, in 2000 in France, there were 9.5 million French people who are 65 years old and over. And 20 years later, quite inevitably, people are 20 years older, and so are our baby boomers. And so in the graph on the right, our baby boomers have shifted 20 years upwards and they are now approaching 75; and at 75, many more people die than at 55. And it’s not just a little more: it’s a lot more. Death by age follows a curve that we call exponential, so there is a multiplication of the number of deaths for each year that passes. And so you have to take that into account-the continuous evolution of the age pyramid-whenever we make calculations on mortality. And there are official calculations that allow us to do this, such as the standardized mortality rate by age, the “age-standardized mortality rate,” which we can find at the WHO, at Eurostat, as well as at Stats Canada.
And so when we take this into account and calculate the age-standardized mortality rate, we obtain this curve in France, and we realize that 2020 is the sixth least fatal year in all of the history of France. So this is the case for all the countries of Europe where we see variations. Sometimes the year 2020 is the least deadly year in history and sometimes it’s the tenth, something like that. Well, it depends on the country, but there is nothing exceptional, and we are not able to find the slightest mass mortality event anywhere in the world for which we have data. That is about totals.
And it’s even worse for those under 65, since some have said that there was, after all, an increase compared to 2019 but of course for those under 65, we see nothing. So anyway, those under 65, who represent 80 per cent of the population, have absolutely never shown the slightest sign of any danger or any increase in mortality, and have never been affected by anything whatsoever. And the over-80s, of course, died more in 2020 than in 2019 in some countries, but their mortality rates remain among the lowest ever recorded in all of history.
So the mass mortality event didn’t happen in the way it was promoted. So we cannot defend any measure that has been put in place on any justification of reducing mortality, especially not among young people, nor even among the oldest. And so as I was saying, I did this for all the countries in Europe. And on this map, I represented where the year 2020 is in terms of mortality compared to all the past years. And we see that-for example, here we have Iceland at the top, Ireland, here we have Norway, Denmark-2020 is the least lethal year in history for these countries. Absolutely nothing happened. It’s even a record low mortality. For Germany, Finland or Sweden, well, it’s the second least deadly year in all of history, so only 2019 is less deadly. For countries like France, this is normal for the decade. And for the worst in black, the year 2020 remains the tenth least deadly year in all of history.
So it is important to look at age and stop pretending that a mass mortality event has happened anywhere since 2020. This is completely false. In Europe, I downloaded all the data from Eurostat, but you can also look for it on a site called Statista, data on the United States or even China to realize that-even in China in 2020-there is no trace of a mass mortality event. So that is the first thing we should completely refute: there was no mass mortality whatsoever.
Basically, you confirm that there was no mass mortality event in terms of deaths. Now what about hospitalizations?
Exactly, this is the second level. We have to ask ourselves the question of hospitalizations. And in France, as in many countries, we had propaganda that was extremely strong-numerous images on television saying that French hospitals were completely overwhelmed by what was called the first wave (we will come back to this) in March-April 2020. Therefore, everyone was persuaded. Since then, there are official reports that show that here in France during the 2020s, the total number of registered COVID-19 patients in the hospital-that is, the burden of COVID-19 patients-was 2 per cent. Therefore, the suggestion that it was COVID that caused hospitals to be overcrowded in 2020 is perfectly ridiculous. It is completely impossible with a figure as small as 2 per cent.
Ninety-eight per cent of patients had nothing to do with any kind of respiratory infection that could have been labelled COVID. So it was something insignificant.
It’s even worse than that, since, here on this graph, I have shown the evolution of the number of hospital stays in 2020 compared to other years. And we can see very clearly that these are the months of the year, and we see the number of stays from previous years. In red are the numbers of hospital stays for the year 2020. The yellow bars represent the decline, and we see that there was a huge decline in hospital activity in 2020. Why? Because with the panic that had been unleashed, the French government decided to put in place a plan blanc [general emergency plan] from February onwards which was used to throw out all of the sick people who needed to be in the hospital saying, “COVID patients will take up all the space.”
In the end, this story of COVID in hospitals was totally insignificant, and the hospitals remained empty. And up to 50 per cent empty in April, while all the TVs were telling us that they were overwhelmed and that the hospitals were full of COVID patients. So not only were they half empty, but there were hardly any sick people labelled COVID inside. So that’s the hospital aspect.
Now let’s talk about diseases. So is an epidemic apparent?
This is the third level, in fact. And in France and elsewhere in the world too, there is a network called the Sentinelles Network, where doctors report patient cases via a network that makes it possible to count and track what are called outbreaks. And in particular, it works well for the flu. That’s what I’m going to show on this graph.
So here we have the results of what is called the incidence, in other words, the number of patients per 100,000 inhabitants as reported by the network of doctors called Sentinelles. And so here we see the black curve, which is what was recorded during the winter flu season in 2014-2015-so up to 800 new patients per 100,000 inhabitants-and here, 2015-2016, in yellow; and 2016-2017 in blue, where we had reached 400 patients per 100,000 inhabitants. And all the red curves on the right represent patients whom doctors have diagnosed with COVID-19, and who had consulted doctors. And we have never exceeded 150 patients per 100,000 inhabitants in France since the start of this crisis.
In other words, according to the usual definition of what constitutes an epidemic, there has never been an epidemic of COVID-19 in France. It’s quite simple: doctors did not see enough patients to declare that there was an epidemic.
So in other words, there has been no mass mortality event anywhere. There has been no overwhelming of hospitals as was promised. There was a total disorganization of the hospital system. There was a lot of fear. We turned people away from the hospitals, saying that COVID-19 was going to overwhelm everything; and in the end, there were very few hospitalized cases. And even regarding disease, doctors did not see patients in sufficient numbers to declare any epidemic. So there is something wrong. And these are the three ideas to sort through first in order to ask the question: What have we counted from the start?
And here I have a question for you. The famous tests, the tests: is there a link between the so-called COVID tests and any disease?
That is the whole question, since we have changed the definition. This is what we have just seen, since there were no patients. We never should have been able to initiate any kind of hysteria, and especially not for medical reasons. But the definition was changed. I recall that there were reports that criticized the WHO in 2009 for having launched an H1N1 panic by changing the perception of severity. In other words, in the past, before declaring a pandemic, large numbers of serious patients had to be found in countries. Since 2009, the WHO changed its definition to say that the severity criterion no longer applied and that you only needed to find patients. By the way, the WHO was strongly criticized for having participated in trying to launch a panic in 2009, but in 2020, it’s much worse, because it’s no longer a question of counting sick people but of counting cases. And so, in effect, rather than having an epidemic of sick patients, we have epidemics of cases based on testing. And so, we don’t have an epidemic with these famous tests, we have a simultaneous count everywhere.
Here is a screenshot of the site called “Our World in Data,” where you can look at new confirmed COVID-19 cases, and these are the deaths per million confirmed COVID-19 deaths. And therefore, you see there’s an almost synchronized count starting all over the world at the same time. We are not yet necessarily at the testing stage because the tests are not necessarily provided everywhere, but we still have a count that starts everywhere at the same time. And besides, this simultaneous count everywhere demolishes the idea that it would be due to a communicable disease-we will come back to that later. What people need to know is the way in which patients are registered in hospitals-this applies to all hospitals in all countries affiliated with the WHO-is done on the basis of a nomenclature called ICD-10 [CIM-10 in French], the International Classification of Diseases.
Soon we will see version 11, but at the time, it was ICD-10. A new code was put in place by the WHO beginning on January 31, 2020, and so all WHO-affiliated hospitals around the world were asked to start counting COVID-19 from February 2020 onward. And this start is indeed the beginning of the count, which takes place almost everywhere in the world at the same time. Indeed, the WHO memo specified that there were two codes: code U07.1 for confirmed COVID-19 and U07.2 for unconfirmed COVID-19 virus, but it also said not to use the second code. Everything was to be registered as virus confirmed.
And what we see in the French hospital statistics, available on a site called ScanSanté, is that the introduction of the COVID-19 code-this COVID-19 code of the ICD-10-is then used to determine the price at which the hospital will be reimbursed. Then, there is a passage from the ICD-10 code to another price, another code, which is called the GHM. And the COVID-19 code allows you to enter information into the different boxes, seen in yellow. But almost all have been entered into this yellow box according to age: “respiratory infection and inflammation, age over 17 years.”
So we see that there was an explosion of these codes in France from the year 2020: an explosion of more than 400 per cent, then 500 per cent in 2021 compared to 2019. So we had 50,000 people per year pass through the hospitals under these codes, and we went to 250 [250,000], and then to even more than 300,000. And we see that the use of these codes was made at the expense of all the others. So in other words, at first reading, one would have the impression that COVID-19 is a disease that cures bronchitis, even asthma, pneumonia, bronchopneumonia, pulmonary edema, interstitial lung diseases, all other diagnoses on the respiratory system: bronchiolitis, tuberculosis, chronic bronchopneumonia and flu. In other words, all other respiratory diseases seem to have disappeared in favour of COVID-19. And what we understand very well by looking at this table is that we are only dealing with a transfer of coding. What has been called COVID-19 is the synthesis and sum of virtually all other respiratory diseases that existed until then, and which are now placed under the same banner.
It’s a story of transfers and codes. I also specified that these codes correspond to a reimbursement price for the hospital, and “respiratory inflammation infection,” for example, is much more highly reimbursed than flu. So there is greater interest in entering a patient in this box rather than in the flu box, thereby improving hospital reimbursement. So in the hospital, we only see a transfer of coding and that’s it: there is no new disease.
And indeed, you are right to talk about the tests. Perhaps before speaking about the tests, which are the key to all this, we should go back to what people died from.
The cause. Indeed, you are also going to talk to us, Monsieur Pierre Chaillot, about the effectiveness of vaccines.
We are going to talk about effectiveness and the cause of death. This is a question that I would like to raise now, since we said there was no mass mortality event, there were no overloaded hospitals. There was no visible pandemic, no epidemic in terms of the number of patients. We had a transfer of hospital coding, but we did have increases in deaths. Here, I will show you two different neighboring countries.
So here are the weekly deaths that occurred in France since 2013. So you see variations. Every winter, there are increases in deaths throughout the northern hemisphere at the same time, simultaneously. And we see here, in 2020, I put in yellow the period of strict lockdown in France in March-April 2020 and we can clearly see a peak in deaths which only affected the oldest people. I put here the different age groups, and it really affected the older people. And we have the neighboring country, which is Germany, in which during the same period absolutely nothing happened. There was no strict lockdown at all. There were rules that were put in place, of course, which closed certain public places, but there was no strict lockdown.
So we have a country which strictly locks down, which has too many deaths over this short period-at the end of the year, it was not that much, but over this period it shows up-and then Germany, where absolutely nothing happens. So it does not make sense to have countries like that, which behave so differently in terms of the level of deaths. And I have included a map here which highlights in red the countries where we observe an increase in deaths that is significantly higher than usual. This uses the Eurostat data, the official data. I have 9 out of 33 countries, which is a minority.
So the idea of the pandemic and the first wave is completely wrong. It’s a minority of countries that are seeing an unusual increase in mortality. And if we dig a little deeper and look within each country, for example here in France, it’s the French departments-there are 100 of them-and so in France, there are only 14 French departments which have an abnormal increase in mortality. So, it’s the same, it makes no sense in terms of geographical distribution. They are not even neighboring territories. We have all of Île-de-France, that is to say around Paris, and then we have a few territories scattered all over the place.
So we really have completely incoherent distribution zones, with a story that does not hold water, about a virus which is spreading and which would cause a mass mortality event from a geographic point of view. There are- Once again I repeat, the deaths labelled COVID, which we saw is mostly counting-well, they are almost simultaneous everywhere.
This can be seen when we look at the death peaks among the different countries. Here we go from the United States, to Spain, to England, which is an island, to Germany which is in the middle of Europe, et cetera. And we must have a maximum of 10 days of lag between any two peaks, which makes it perfectly impossible for us to accept that something is spreading. If there was something spreading in the population, we would have quite notable differences among the different waves, among the different countries. So there are far too many inconsistencies to validate this story, and that just shows that we are dealing with a simultaneous count everywhere and not a spreading epidemic at all.
What I showed for France is that, in France, we know where people die. We know if people died at home, in hospital, or in what are called retirement homes, nursing homes for the elderly, or EHPADs [residential establishments for dependent elderly people]. Here, we see the number of deaths at home; so in other words, these are people who were found dead at home, whose death was confirmed by a doctor at home postmortem. Therefore, these are people who have never been registered as COVID of any kind, otherwise they would have been taken to the hospital. If they had been in care homes, they would have been counted as COVID. As such, these people were really discovered afterwards at home.
Even so, there are doctors who said that the excess mortality which took place in March-April was due to COVID deaths. But no one can know, there were no autopsies. The institutes had fun attributing this increase in mortality from March-April 2020 to COVID-19 without there being the slightest proof of that, apart from death certificates-I repeat-issued by doctors who were convinced that COVID kills and who wrote that on the certificate, but without completing any autopsy.
And this excess mortality corresponds to 5,200 people over the period of the first French lockdown: March-April 2020. But we have an official report from Public Health France on May 7, 2020, which sounded the alarm over the fact that there had been a huge decline in the use of stroke and cardiac emergency care provided over this period-a deficit which was estimated at 4,800 untreated people, and therefore, possible deaths-because if we don’t treat strokes and heart attacks, it is not COVID that will kill them; rather it’s that we have deaths by neglect.
This figure was confirmed by another report, that of the ATIH [Technical Agency for Hospital Information], which said 3,000 for only one of the two pathologies-I believe it was heart attacks-and consequently, 3,000 times two: that’s 6,000. So we are between 4,800 and 6,000 possible deaths from lack of care, as established by official authorities, to cover an excess mortality of 5,200. In other words, the entire bump that we see from deaths at home during this first French wave has nothing to do with a virus, even in the slightest, but only with neglect.
It’s the same for EHPADs, in other words, the retirement homes I talked about. Here, I put the number of daily non-COVID deaths in blue, and in orange, those labelled COVID. So we see that from the moment we have the right to count COVID, all other types of mortality disappear. It’s an obvious scam. Nevertheless, there is excess mortality over the period, which corresponds to 5,000 people. And I would remind you that in France, like many other countries, the government was being advised by consultants, and decided that there was a new deadly disease-COVID-19-which was going to infect everyone, and that there would be no room in hospitals for the elderly because they would be full of COVID patients. We saw that this wasn’t actually the case.
And so the only thing that was proposed was to offer them a palliative: a double injection of a palliative drug. In many countries, it was Midazolam. On the other hand, there was a worldwide shortage of Midazolam because of the Canadians, the English, the Americans who had taken all the world stock. And therefore, in France, there was a special decree called, “the Rivotril decree,” which authorised Rivotril.
And so on the graph below, we see the sale of injectable Rivotril in French pharmacies. And consequently, we can estimate the number of beneficiaries of the palliative Rivotril, which is estimated at 5,000, and which corresponds exactly to the excess mortality in that period. In fact, with Rivotril, we can clearly see the first so-called wave of COVID-19 from March-April 2020 here and the second so-called wave of COVID from October that we see there, and which is again perfectly reflected in this policy, which says “we no longer treat”-no doctors, no treatment for the elderly-“and we go straight to the palliative.” This seems to cause deaths in a perfectly logical way, without the need for a virus at all: it’s just a change of protocol.
Now for illustration purposes, we also have the data in England. So here is the excess mortality that can be calculated from the English ONS [Office for National Statistics] data-so the excess mortality in the over-90s, and below that, the distribution of Midazolam over the period, that’s it. So as I said, there was no longer a stock of Midazolam in France, but there was in England. It was used to do the same thing in England and therefore, we also have perfect correlations in England for the same protocol.
The last place you are when you die is in the hospital. In fact, the hospital is even the primary place of death in France, since the majority of people who die, die in the hospital. It’s 1,000 people every day, and we see the same thing: the blue curve of the number of daily deaths in hospital-I should say the blue curve excluding COVID, which goes down from the moment we have the right to register patients as COVID.
So here we are registering those who have just died as COVID patients, but there is still an increase in mortality over the period of March-April 2020, which we can estimate to be around 7,000 people, as I said. And we have an official report. Members of the Scientific Council published a report in Nature which shows this rather exceptional curve where we see, over this period, among the patients labelled COVID-the orange curve, here. It’s the time between their admission to the hospital and their death. And we see a huge death rate on day one and also very, very strong on days two and three, knowing that we are apparently talking about a disease that is supposed to make you sick in a few weeks, and then you die from it several weeks later. So dying on the day of admission to the hospital, or within three days, is not normal.
I remind you that the protocol in France at that time was not to consult a doctor, but to self-medicate with the antipyretic Doliprane, to wait it out, and only when you could no longer breathe to go to the hospital. So in terms of patient survival, it’s not great because we have patients who arrive at the hospital in the emergency room in a very advanced state of distress.
Second thing, I showed for the retirement homes that I could calculate the number of people who benefited from the protocol called Rivotril, but I have no idea of the number of people who benefited from a palliative treatment instead of care at the hospital, which can very well explain why we have people who die on day one.
And the third thing is that the protocol, seeing that we said that there was no treatment in the hospital, the only thing they claimed was going to save people was to intubate people deeply, put them on a ventilator, and put them in an induced coma. Well, this practice has been shown in many ways to be harmful and to cause people to lose their chance to recover, since it’s not easy to survive it.
And therefore, if we add these three causes-so, the iatrogenic effect, in other words, people who are put on a respirator and who do not survive whereas if we had done otherwise, they could have survived; if we add palliative treatment replacing care; and if we add the non-care in early stages-well then we can explain 100 per cent of the excess mortality, which is just that we didn’t do what we normally do, and we implemented deleterious decisions that harmed patients. And there is no need at all to bring in even the most minor new virus to explain this excess mortality. You just need avoid doing what you normally would have done.
Now we are going to get to what you spoke about earlier, that is the tests, which are indeed the engine of statistical fraud. In effect, in statistics, as you mentioned, the tests are indeed the engine of fraud since tests don’t normally reflect reality. Take the example of a pregnancy test. So a pregnancy test is when a woman pees on a pregnancy test and there’s an indicator that tells her if she’s pregnant or not pregnant. But that’s not the reality. The reality is to be pregnant or not pregnant. Pregnancy tests are more than 99.9 per cent reliable, and that’s okay. If we imagine that we make all the little 5-year-old boys on the planet pee on pregnancy tests, we will probably have some that will be positive. Well, should we have a 5-year-old boy checked for pregnancy because he has a positive test? That is not the reality. The reality is to be either pregnant or not pregnant, and not simply to have a positive test.
However, for this idea of COVID-19, that’s what we did. That is to say, a person who had absolutely no symptoms could, on the basis of a simple test, be considered sick. So being sick with a non-disease: in other words, a disease without symptoms. Even being considered contagious: that is, that they could transmit their non-disease to someone else and their non-symptoms to someone else. After 15 days, they were administratively considered cured of their non-disease and even immune to their non-disease.
Therefore, this is a complete absence of reality. And it explains why the doctors did not see any sick patients yet still cried pandemic, as a result of these famous cases and these famous tests-famous tests, moreover, the worth of which we absolutely cannot know. For our famous pregnancy tests, to determine how often they are wrong, all that is needed is to have pregnant women pee on them and then all the tests that show “negative” when the woman is pregnant, well, we know right away it’s a false negative. This allows you to test the sensitivity of the test. And conversely, if we have non-pregnant women pee on the test, we look at everyone who shows “positive,” and that allows us to test the specificity of the test.
And so the fact of being pregnant or not pregnant, which is reality, is called a “gold standard.” For this COVID-19 story, there is no “gold standard,” simply because there is no precise definition of the disease. We have a set of symptoms that has been stated, which include headaches, cough, fever, chills, fatigue, stomach aches, nausea, diarrhea, and all that could fit into the COVID-19 box. With any of these symptoms, and based on a test, you could say, “Oh well, it’s COVID-19 disease.” That’s why there are a great number of scientists who say that it’s a disease, which is specific, which is multifactorial, which is diabolical-quite simply because we are including anything. We are counting a test without being able to measure it against something concrete, and there is no “gold standard.”
With the French data, we can even verify that this test has absolutely no meaning. That’s what I’m going to show you now. Well, if we imagine that the test is 95 per cent reliable, we can say to ourselves, “Well that means that if I test everyone, and the sequence of the virus I am looking for does not exist, well, I’ll find 5 per cent positive.” Well, right there we have a problem. Because for a good part of the year 2020 in France, there were less than 5 per cent positive tests. That’s the Ministry of Health telling us whether we have a positive test, a negative test, a person who is symptomatic, and a person who is asymptomatic-that’s 4 boxes. If we add symptomatic positive tests and asymptomatic positive tests, we are less than 5 per cent for a large part of the year, which means that we are possibly in the process of locking people up for something that does not exist. In the end, we are just talking about a test which is too sensitive, which is not specific enough, and therefore, in fact, we can’t do anything with this data.
Second thing: Let’s assume that the test is not entirely bogus, that it is very reliable, above 95 per cent. Well, one can ask the question: is it coherent? For example, we can look at whether our positive tests indicate any actual disease and you can see that over the whole of 2021-well, among my positive tests-I have a lot more asymptomatic ones, that is people who have nothing at all, than people who are symptomatic, that is people who have symptoms. In other words, the test is absolutely inconsistent, and when you have a positive test, you are not actually sick. And so that’s a huge problem, which means the test is bogus.
We can check in the other direction: We can look at people who are symptomatic, that is, they are said to have the symptoms of COVID-19. We make them do a test and what do we notice? We notice that the overwhelming majority of the tests, three-quarters, are negative.
So for the sick, the tests are mostly negative; and when you have a positive test, you’re likely not sick, which means that the test has never had anything to do with the disease in the slightest. It is therefore- well, I don’t know what you can call it, a scam, in any case, scientific nonsense; and therefore, it is above all not a statistical tool since it’s nonsensical.
It is rather an epidemic of cases. So we have an epidemic of positive cases, but without disease. That’s what you are telling us, Monsieur Chaillot?
Exactly. Absolutely. If we go to 2022, then I can show you that the positivity rate increased in 2022. It has nothing to do with the fact that the virus arrived. It would be somewhat unfortunate to say that it arrived just when everyone has been vaccinated. These statistics don’t even make sense over time, since gradually, as virology laboratories did not find the SARS-CoV-2 virus, but started finding other sequences, they called them variants.
And we suddenly increased the sensitivity of the test by looking for more and more variants-the record having been established from the end of the year 2021 to the beginning of 2022 with the alleged Omicron variant, which skyrocketed test positivity rates all over the world. In France, we reached 30 per cent positivity; and there was, I believe, 70 per cent positivity in Sweden at that time, so all the Swedes were positive. It was remarkable.
So that still doesn’t make sense. It’s just that we’re changing the protocol all the time and so we do anything at all. And then we even changed the protocol in the opposite direction. But in addition, it’s winter, and therefore in winter, the number of symptomatic people increases among the negative cases as well as among the positive cases, and that’s all. Fortunately, there is science for that, to enable us to count. In winter, people get sick, and then if you increase the sensitivity of the test, there are more positives, and that’s it.
Therefore, there’s no consistency. There’s never been the slightest consistency in the positivity rates of these famous RT-PCR tests. There wasn’t the slightest consistency with any disease. And we’ve been forever changing administrative rules that made no sense all along-and that’s very clear if we allow ourselves to analyze the statistics.
So we now come to the question of vaccines. So the tests have no efficacy according to your research results, but they do have an efficacy to promote the vaccine. Do the vaccines provide protection?
There are very few people who know that indeed, the vaccines- So neither Pfizer nor Moderna have ever promised people who were vaccinated that they would be protected against any disease. By disease, I mean symptoms. Personally, that’s how I define the word “disease”: to be sick, to have symptoms. Neither Pfizer nor Moderna promises that people will have fewer symptoms or be less sick once they are vaccinated. They promise that people will have fewer positive tests, that’s all. It’s supposed to play on the positivity of the test. The two phase III studies are very clear on this: they are based on positive tests.
An additional small thing is that when the trials come in, you’re supposed to say that COVID-19 is dangerous for people over 65 years old. But the study protocols for the two tests here from Pfizer and Moderna have three-quarters of the test population be candidates under 65, which means that the two studies should have ended up in the trash just because, quite simply, the population doesn’t correspond to the target. And there you go.
We’re going to dwell a little on the fact that it is based on the positive tests. We say an “output” means that the patient has symptoms, whatever they are: so we said fever, we said difficulty breathing, chills, muscle pain, loss of smell, diarrhea, vomiting, et cetera, there are plenty of them. As soon as we have one, then we get tested. Here we have a problem: it’s that in the protocol-I’ll take the example of Pfizer-it’s not mentioned at all that each person must be tested the same number of times. This means that if we tested those who received the placebo more often than those who received the vaccine, consequently, we’ll find vaccine efficacy simply through test bias. And so there is nothing at all in the study that guarantees that the two cohorts were tested in the same manner, and we have clues instead that tell us this wasn’t the case.
Finally, I will remind you that the alleged 95 per cent vaccine efficacy of Pfizer is eight cases-that is, in six months, out of the 40,000 people tested, they found eight positive people in the vaccinated group and 162 in the placebo group. So the first Pfizer result-even after six months of study-is that there is no pandemic. Eight versus 162, when we study 40,000 people for six months, means that this pandemic story does not exist. They haven’t found enough people to say that. And it’s on this eight to 162 which leads to 95 per cent efficacy. These are figures that are so ridiculous that the biases required to arrive at this result can be colossal.
I remind you that there is a testimony in the BMJ [British Medical Journal] of a researcher who was head of the laboratory at Pfizer denouncing the number of breaches of the usual protocol that had happened in the laboratory. And in particular, there are doubts about the secrecy being properly maintained throughout, because once again, if people know who is in the placebo group and who is vaccinated, well, then they simply need to test only the placebo candidates and not the vaccinated.
Again, in the Pfizer study, there is this particular table, which is interesting, which shows that for people who have been vaccinated, here, we see many more cases of fever, chills, muscle pain-that is, sick people-than in the placebo group. So what the Pfizer study shows very clearly is that their vaccine makes you sick. It’s written down very clearly with these statistics: the only thing we can be sure of is that it makes you sick. And besides, people are therefore forced to take anti-fever medications or painkillers such as, for example, paracetamol, which will have a great impact because it will suddenly mask their symptoms. So the population that is the sickest and that takes the most medication to mask these symptoms, well, that’s the vaccinated population-and by far.
So, there’s some doubt about the fact that they tested the right number of people and that, as we look at the study, they didn’t just decide that for the same type of symptoms- Because you see that the symptoms that are written down are the same symptoms of what is called COVID, they’re the same-but when we talk about vaccination, we’re going to consider that they are adverse effects to the drug, whereas when we talk about people in this placebo group, we can consider that they are the effects of COVID-19.
Many of these undesirable side effects happen within the first seven days, by the way, and the first seven days aren’t included in the study results. So that is again a possible bias. In other words, if the vaccine, for example, makes you really sick for the first seven days, so you take antipyretics and painkillers, you won’t feel anything afterwards-well, you won’t test positive afterwards. Whereas if the placebo doesn’t make you sick, then there’s a better chance of testing positive.
And then one last thing is that at the end of the study, you have to look at the number of people who were excluded from the study, which is the primary method for Big Pharma to get rid of the embarrassing results. And here, we see that of the 40,000 initial people, there are 1,800 vaccinated who were removed from the study before the end and only 1,600 among the placebos. That’s a difference of 200. That’s not normal, and those numbers are colossal in relation to the efficacy.
So that is, the efficacy we see is 8 against 162, even though 3,000 people were removed in all, and 200 more people were removed from the vaccinated group than from the placebo group. So the bias can be colossal, to be certain that they haven’t kicked out people who would have had positive tests if they hadn’t been removed from the study. This is a very typical way to succeed in promoting any medication on the basis of supposedly scientific studies-by making these kinds of small statistical adjustments.
So Pfizer is not showing at all that you will be less sick after the vaccine. You are sicker after being vaccinated. And as for the alleged effectiveness in relation to the test, we have a whole host of reservations-even more than reservations-with regard to the study when we see all the figures put forward, when we see the shortcomings, and furthermore, when we know the track record of this brand. So what we can say then is that everything is based on the tests-and knowing that the tests are a scam, all we have to do is not test the vaccinated and only test the unvaccinated to get the results that suit us.
If I take France as an example, well, we can show-thanks to this simple graph which is available on the internet, which was produced by a person who, by the way, received the Legion of Honour from the French President for all his work during the crisis-this graph shows the entire scam. In other words, the link between test, health passport [pass sanitaire], and vaccination. Since in fact, when we set up a health passport, we arranged it so that only the unvaccinated are tested.
And so here is the graph for France. It’s the positive cases reported for the population, so it’s a positivity rate, if you will, according to vaccination status. And so, orange shows the unvaccinated; blue are the vaccinated, two doses; and black are the vaccinated, three doses. There is a small data error that comes from the site. And what we see is that when the health passport was introduced in France on July 12, people were forced to go and test themselves because they were on summer vacation. So they went to the campsite, to the restaurant, they tested themselves all the time.
And so, there was a wave in the middle of summer, a wave of positive tests, no sick people at all. There is no wave of sick people at that time. We have a wave of positive tests in the middle of summer which begins from the moment the health passport is introduced. And as long as there is a health passport, it is the non-vaccinated who are required to test themselves the most. Therefore, we have vaccine effectiveness, since the effectiveness of the vaccine comes from not having to test yourself.
And so it works very well, and the wave stops exactly on August 15, which is the usual date for the return of vacationing people in France. And so there you go: we have a virus that starts with the health passport and stops exactly when people come back from vacation. It lines up perfectly. The positivity rate, then, when people are at work, is relatively low because they don’t need to go to restaurants and camping. And we see that when the All-Saints holidays begin in November, there is a new increase, there, in the positivity rate among the unvaccinated. That has nothing to do with a virus; it’s a new administrative rule. Well, the French state decided at that time that all college students would have to test themselves every day to go to college. It was to encourage them to be vaccinated.
And so, that’s it; that’s why it’s going up. And it’s not a new virus at all, but as long as there is a health passport the unvaccinated are more positive than the others.
And a new administrative rule change took place just before the start of 2021. The Minister of Health decided that all people who have two doses will now have to take a third, otherwise their health passport would be deactivated-it’s a “vaccination passport” and it could be deactivated. And so rather than rushing for a third dose, everyone, especially those who had had side effects-you have seen testimonies-instead rushed to get themselves tested: it was free. To get this lauded positive test: it was in winter, you had symptoms, and you had a chance of avoiding the trap of having to get a third dose. And so people with two doses rushed to get tested so much, so that more of them will be positive than those with zero doses.
And so here we are, at the beginning of the end of the scam, as we realize that by modifying the administrative rule, well, then we modify the vaccine effectiveness. From now on, not having a vaccine, not getting vaccinated, is more protective because we’re not subject to an administrative rule that is worse than any other. We had those with three doses who still got tested and the results were quite positive. That’s pretty odd. I mean, people who think they’re protected, who still go to test themselves and find themselves to be positive.
And here, the most interesting thing is in March. It’s the end of the scam, in other words, we have the end of the health passport. And on the very day of the end of the health passport, the curves are reversed. That is, the least positive are those who test themselves the least: these are the unvaccinated. A little above that are those with two doses, and the most positive of the bunch are those with three doses, simply because what you see is a perfect reflection of people’s levels of fear-that is, the more we are vaccinated, the more we are afraid and the more we test ourselves-and it works perfectly.
So this graphic-all by itself-definitely destroys this scam that has been the “test, vaccine, passport” triptych. We set up a health passport so that the vaccine protects against having to be tested, and it artificially creates vaccine efficacy.
It’s quite clear, Monsieur Pierre Chaillot. I don’t know if you also had a follow-up to talk about post-vaccination deaths. So you claim that there were no deaths, no mass mortality event, in the COVID period in 2020. But after vaccination, do you have any figures to show us the statistics of deaths or hospitalizations?
Yes, I downloaded the deaths. There was no mass mortality event of any kind in either 2021 or 2022. There was no mass mortality from the vaccine either, otherwise we would see stronger statistical indicators, but we do see statistical signals. So I’m not going to say mass mortality event either, but we see signals. I’m just going to remind you-I think it’s in a screenshot I made in July 2022 for the release of my book-the numbers have increased. There it is. In European Pharmacovigilance [part of European Medicines Agency], the number of adverse effects have been entered according to category, reported by professionals or not. So proven cancers, cardiac arrests, myocarditis, pericarditis: these were already in large numbers in Europe. And then, the number of results that ended in the death of the patient reached 28,000 last July, and we must be at 33,000 in Europe today.
I remind you that the pharmaceutical industry says two things. The first thing they say is that none of these cases can ever be attributed to the corresponding drugs. Why? Because the industry tells us: “Myocarditis existed before vaccination. Therefore, you can’t prove that in a vaccinated person the myocarditis occurred due to the vaccine.” This is the primary spiel of the pharmaceutical industry-it serves to protect itself. This is one of the legal reasons why in France, in particular, it is almost impossible to win any lawsuit against Big Pharma, and moreover, what is said is true statistically and is further asserted by all the health, drug, and government agencies.
Except that the drug industry is saying a second thing: it says they are fully aware that there is a total underestimation of the number of adverse effects since people don’t report them. Almost no one knows that there is pharmacovigilance, and even when they do, it’s very complicated to make a report, so no one does it. So according to the drug industry, these numbers have to be multiplied by 10 to find out what happens in real life. It’s taken from the drug industry documents that say, “It reflects only 10 per cent, you have to multiply it by 10.” There are professionals who say that we should rather multiply is by 20 or 100, but even if we take the figures of the drug industry, we still have to multiply by 10, which is quite interesting and impressive when we look at these numbers.
What I did to give myself some insight is that I looked at the evolution of weekly deaths in France and in all the countries of Europe from Eurostat. Here, for example, I took Portugal. I made a model for calculating excess mortality, the details of which I wrote in my book, and all my programs are online. I have a red bar when I see a weekly excess mortality compared to the past, compared to the expected, and green when it is a lower mortality. Blue is the average of what happens and below I put the number of doses received.
So here, for example, is for 15- to 24-year-olds in Portugal, and what do I see? I see that there is an increase in mortality right during the vaccination campaign for 15- to 24-year-olds in Portugal. It lines up perfectly. And I also notice that for the 60- to 69-year-olds in Austria, I also have increases in mortality at each dose in a perfectly synchronized way. I didn’t make calculations just for these countries; I put two examples per age bracket in the book and I did all the examples, I did everything, for all the age brackets that were available.
Thus, to run my programs, I have absolutely everything, if you will. And I even did statistical calculations to find out if the vaccination peaks were close to the death peaks that we see in the excess mortality. And the statistics tell me that it can’t be due to chance-it’s too close too often. So I tried all kinds of things to see if it worked every time, and it works way too often. So I have real traces of increased mortality occurring exactly during the vaccination campaigns.
There are also details on births. That is, we have data in Denmark and in other countries such as France, Germany, Slovenia as well. We notice that since the vaccination of women of childbearing age, indeed, nine months later, we have a collapse in the number of births. In Denmark, we can see it very well: we are below the low significance curve, whereas births in Denmark were very regular. These are the numbers of births month-by-month. There it is from 2022. Therefore, nine months after the vaccination of women of childbearing age, it collapses and it does not go back up.
Here, in France, is a graph that was made by Christine McCoy, which I also checked. So by downloading data from France on mortality, representing the rate of children who died between 0 and 6 days-that is, neonatal mortality, which most often corresponds to children who are born too early, very premature-
we note that the vaccination of pregnant women officially started in France in May 2021, but rather it’s in June 2021 that we have the peak of vaccination of pregnant women, and we have a peak of neonatal deaths the like of which has never been recorded, that we therefore see here. And for the red dotted lines, it’s the very, very high excess mortality. Therefore, there is less than a one in 1,000,000,000 chance that this spike is natural. So we also have a record of the deaths of premature babies.
So from all that we’ve seen, what I’m showing is that we’ve been through a statistical scam from start to finish based on testing, and they created fear based on statistics of deaths, hospitalizations, and sick people who were never there at all. And the tests, with the health passport, have made it possible to set up a “test, vaccine, passport” triptych, which has made it possible to build perfectly, artificially, a vaccine effectiveness that does not exist. And then what we observe, and what is silenced by all the media and many institutes, is that right during the vaccination campaigns, we have unexplained increases in deaths, we have a drop in fertility that comes afterwards. Therefore, there are far too many traces, far too many signals not to worry about them.
Monsieur Pierre Chaillot, I have one last question for you. In fact, with regard to all these statistics, with regard to all your figures, the figures speak for themselves. You have done a very thorough and very, very, clear study. What could have been done differently or not done-I can go negative too-during this period?
For France, it’s quite simple since, as I said, there is a report from the Senate which chronicles the H1N1 scam. So the report is from 2010 on the 2009 H1N1 scam, which made it very clear that if this scam didn’t catch on-and which implicates the WHO by the way-but if it didn’t take, it’s because we behaved as usual. Meaning that when people got sick in the winter, well, they went to see their doctor as usual, who cared for them as usual. Each doctor treated his patients differently, incidentally, but it doesn’t matter. In all good conscience, each doctor treats in a different way and as a result, it worked; that is, nothing happened at all. In fact, a report was issued after this episode saying that this is what works in the event of a pandemic: we don’t panic, people go to see their doctor when they are sick, and when the doctor decides that they are very, very, sick, they go to the hospital.
So that is what should have been done. But there’s another report that came out in France in 2019 that broke these rules and now said: “In the event of a big pandemic, the first thing you have to do is tell people not to go see the doctor, to send them only to certain authorized hospitals.” So, no congestion of the hospitals occurred in France, but some hospitals were overwhelmed if they were among the ones called to the front lines. There were only 38 qualified to receive COVID-19 patients, and I remind you, it was anything and everything: it was headaches, fever, chills, nausea, diarrhea, et cetera. So all the French patients were sent to 38 hospitals, whereas there are 3,000 health centres in France, public, private-and they hadn’t seen the doctor before either, so we created a gigantic bottleneck for sick patients.
So, that’s what the report laid out. And the report also said something else: that a sick person was no longer defined as being symptomatic-that is to say, as having symptoms, knowing he is sick from it-but it was these famous tests. And that’s also what the WHO did, was to stop and say, “We have a pandemic because we have found a sequence of a virus from a sick person in China, and now that we have tests, we are going to launch this great hysteria.” So that’s what was new.
What should have been done was to stay within common sense, to stay pragmatic. What is a sick person? It’s not someone who is dangerous; it’s not someone we identify with a pseudo-test and who we consider dangerous. A sick person is someone who has symptoms who we must take care of, and that’s it. And there are doctors for that who must act in good conscience to receive all the sick and to treat them, and that’s all.
Therefore, what shouldn’t have been done was changing rules that work: rules that don’t permit launching a hysteria and that don’t make some people rich, whether it’s by way of tests or pseudo-vaccines that protect against testing.
Pierre Chaillot, thank you very much for your testimony. As far as I’m concerned, the questions are over. In addition, it’s quite possible there will be questions from the Commissioners. Thank you very much again for your collaboration during the Citizens Inquiry.
Hello, Monsieur Chaillot. Thank you very much for your very exhaustive presentation, which really sheds light on a lot of things. I won’t have a lot of questions, but there is one that bothers me. You have presented comparisons between different jurisdictions, for example, France and Germany, which had not deployed, in any case, lockdowns with the same intensity, so to speak, at a similar time. And we make the assumption that, well, if there is a virus circulating, it doesn’t know that there is a border between France and Germany, so we should normally have the same kinds of effects in the population in Germany.
And so you mentioned that in France, when you look in more details at the department level, it would seem that there would have been a greater concentration in certain departments in terms of the effects that we saw associated with this pandemic. Would the explanation for this be that the administrative measures or directives to deploy lockdowns would vary depending on the size of the departments, or because there is a big difference in certain departments at the geographic level, at the population level, and it wouldn’t have had the same impact on the populations at that time?
So from what I have shown of the two main causes that led to more deaths than usual, the first was to say that the elderly in rehabilitative nursing homes should no longer be treated, but instead just be injected with a palliative. There is a French report on the COVID crisis where we have testimony from a trade unionist doctor who says that for hospitals in Paris-that is, around the Paris region-there was a special group which was called the rapid response group. You had doctors who went around, based on a simple phone call, to provide a double injection of this product to the elderly, and who then left. And so this practice, that is, this idea-which was to say that the elderly were doomed and that we just had to inject them with palliative-was industrialised in Île-de-France, the area covered by AP-HP [Public Assistance for Paris Hospitals], and it is right there that we see a significant increase in mortality. So there you have it, there is a particular measure that hasn’t affected everyone but is part of the initiative that was taken there, and which is perfectly correlated.
The second thing is that we have to look at the practices of the hospitals that panicked and in particular, as I was saying, at intubation. Intubation and artificial coma. And in Marseille, they didn’t hide the fact that they did not want to do this practice because it was harmful for the patient. And so, it turns out that it’s likely that what we’re observing are the hospitals that panicked and implemented this protocol-that was probably promoted by ministry, that had also been done by the Italians at the beginning, and that everyone gave up on afterwards-and the hospitals who were the most relentless in their use of this method are where we see an increase in mortality. You would need access to the figures of the various implemented protocols to make a determination, which I don’t have. But that is quite enough to explain the differences in mortality between the territories: the level of panic, the orders that are given, and the way in which they are executed. And it has nothing to do with any virus from start to finish. It’s just administrative rules put in place, protocol choices, and iatrogenic effects [the effects of those treatment decisions].
My other question is about, well, the idea that there would have been a virus circulating, which would have caused major illnesses or hospitalizations or deaths. Do you deny the existence of the virus having the ability to cause disease in a certain number, or do you vigorously question the alleged effect on a large population? In other words, does this virus, in fact, exist in the population? Is there a new virus circulating which can cause illness in a certain number of particularly fragile people, but overall, is no more important than what we would see in other respiratory infections?
I am not a doctor, nor a chemist, nor a virologist, nor a microbiologist, and I have never observed even the smallest cell. So I can’t tell you if something exists or doesn’t exist based on actual observation. On the other hand, I can tell you that there are no traces: there are no statistical traces that there was any virus anywhere. And I told you that the curves were synchronous, which is to say that we have evidence that we can discuss scientifically, that it is impossible that the deaths, or even the sick people that have been attributed to this COVID, have anything to do with something that has spread. It’s just physically impossible. It is impossible for the curves to be synchronous with something that spreads in space and time. It’s not possible. Therefore, there are too many inconsistencies regarding this subject.
Personally, I am asking for scientific proof. That is, that we find existing proof-something in the order of an RNA sequence-that would arrive, that would spread, that would also be responsible for a disease. What evidence can we provide on this subject before it is possible to make a determination? I call on everyone to ask themselves that question.
As for me, I just maintain my point on the statistical aspect of things. The story that’s been told on this subject doesn’t hold water for two seconds when we look at the statistics that we have. And the only things we observe are a new method of counting, transfers of codification and iatrogenic effects, abandonment of people, and then, voilà, a change in behavior that explains the whole thing. I don’t know if the virus exists, but there’s no need at all to bring it into the equation to explain anything. So, in my opinion, you don’t even have to worry about it. If it exists, it’s perfectly insignificant and it has no influence whatsoever in what we have experienced.
My last question concerns, ultimately, trying to answer the question: To what extent has the deployment of the vaccine in fact resulted in either hospitalizations due to side effects or deaths? The challenge we have, of course, is that it doesn’t seem to be a high enough frequency in general for us to be able to detect a clear signal. Sometimes you can see it over time, when there’s a fairly synchronous aggressive campaign, but otherwise it’s pretty hard to detect in the general population. There’s the whole story of the doses: when we’re going to get them, second dose, third dose, et cetera.
And in the end, the best way to find out would be to have solid numbers on the vaccination status of people who are hospitalized and/or who are going to die, for all kinds of reasons, but who are vaccinated. So these figures must exist in the official statistics. How is it that we are not able to extract this information from the official figures?
It exists. It exists in France; it exists in all the countries of the world. There are very few countries that have attempted to circulate this information. Scotland did it at one time and stopped right away when it showed vaccinations unfavourably. We have England continuing to do so, and we have Norman Fenton doing exceptional work to show that the so-called vaccine effectiveness comes just from the fact that there is a time lag between when you get vaccinated and the moment when you are registered as vaccinated. And so we place the vaccine deaths of those who have just been vaccinated among the non-vaccinated. His presentation is very, very, clear.
In France, we’ve been asking for the data for months. We shouldn’t have to ask to see these figures when they are normally accessible, and even are-and have been-the subject of preliminary studies on the topic. There is nothing coming through at the moment. Maybe by insisting, by complaining, by demanding things we’ll get them. And once again, even if we’re given figures, we shouldn’t take them at face value. We have to look at where they come from, what their quality is, what we can infer from them first.
In the end, in any case, you have to do real statistical work. Demand it at least, but also have the raw data, and verify everything that’s inside and its quality before deducing anything.
Thank you. I’m sorry, I have another meeting now. I’m going to have to leave you.
Thank you so much. I’ll leave you with the lawyer.
Thank you very much. Thank you very much for your time. I know you have other commitments. Thank you again.
Here’s hoping that the recommendations of the Commission will go in the direction of your statistics. Thank you very much, Pierre Chaillot.
Final Review and Approval: Erin Thiessen, October 30, 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/
Pierre Chaillot, témoin expert et statisticien, éclaire la manipulation des statistiques liées à la COVID-19. Il discute de la manière dont les données peuvent être manipulées pour présenter un récit spécifique, et comment cela a été fait tout au long de la pandémie. En utilisant des exemples spécifiques et des données, Chaillot démontre comment ces manipulations peuvent fausser la perception du public de la pandémie et de ses effets. Si vous souhaitez comprendre l’importance de l’analyse statistique et son impact sur la prise de décision lors d’une pandémie, cette vidéo est incontournable.