O covidu, plavání proti proudu a zneužívání vědy s Tomášem Fürstem

What was interesting was that on the first numbers it was clear that the average age of deaths with COVID-19 was over 80. The breaking point was the study of failure. And with a serious face they quoted the results of that failure, which were completely nonsensical, like completely nonsensical. With the mathematical modeling expert Tomáš Fýrst, we talked about how he came to be interested in the topic of COVID and how he works with Czech data. On the other hand, it is necessary to praise USIS. Note that I praise USIS. That USIS exists at all. That it exists at all and was able to put the databases together and collect the data. For example, USIS is sitting on a dry deck. It is slowly opening its ears. We also thought about how much science is becoming a religion and how much it is a gun in the hands of politicians. We, naturalists, were proud to think that this past, that science in philosophy will be used for political purposes, that it is about the soft ones, those social sciences. Maybe people outside the science environment are not so aware of it, but they should be aware of it. The fact that textbooks write people on academic grounds again, they don't have a tendency to mention it too much here. I am very glad that Mathematician Tomáš Fýrst accepted the invitation to the interview. Good day. Good day. Thank you very much for the invitation. Mr. Fýrst, you are an academic focused on mathematical modeling and data processing and you are also a co-founder of the Association of Microbiologists, Immunologists and Statisticians, known as SMIŽ, and you are also a member of the Initiative 21 and Zdravý Fóra. So these are all organizations or associations that have tried to critically address these issues during the COVID period. And if I'm not mistaken, you also like Zdravý Selský Rozum. So if it's about how you got to COVID and its research, did Zdravý Rozum bring you to it, or was there some other obstacle? That's actually a very interesting question. I remember it quite well, because when the shots from China started to come out, so at the moment when everyone here said, mainly don't panic, mainly don't buy, because nothing will come to me and so on, so I already had a complete overview. I said, okay, we'll make supplies, if anything, or something like that. And then, as it got to March, no one knew much. So I didn't protest, we packed up, we went to the mountains, to the cottage, especially grandma, we tried to get away from that civilization somehow. And somehow during that March, February, I don't remember exactly, but we watched what was happening. Then the first numbers started to come from Italy, if you remember. And what was interesting was that on the first numbers it was clear that the average age of those who died from COVID-19 was over 80, which is the average age of those who died normally. So it was already starting to be seen that if it was dangerous for someone, like for old people, for those young people, that young people didn't die much at the beginning. Then some such warning messages started to come from Britain, where young doctors started to die, which is still a very interesting thing. A lot started, or a lot. The messages that came in were often about doctors who were originally Indian, from Pakistan, and so on. To this day, it is speculated whether it somehow has nothing to do with vitamin D. If people who have darker skin and are in Britain, where there is no sun at all, if they are not so high on vitamin D, but so good, then we still somehow got along. But what I remember as a breaking point was the Preval study. Because, of course, at the beginning of the epidemic, it was very important to know how deadly it was. Here we came across insufficient Czech terminology, which no one has corrected to this day. In fact, there are two terms, infection fatality rate and case fatality rate. Infection fatality rate is the number of people who died from COVID-19, divided by the number of all infected people, whether they find the infected or not. But how am I supposed to know how many are infected? And that's case fatality rate. Case fatality rate is the number of dead people with COVID-19, divided by the number of exposed cases. Unfortunately, there is no terminology for it in Czech. Or if there is, no one knows and does not use it. And we said all the time, look, we need to know how many people already have it behind them. Because when the number of deaths with COVID-19 was divided by the number of exposed cases, it turned out to be a terrible number, about 4%. So it was said everywhere, just be afraid, be afraid, 4% of infected people will die. We used to say, look, that's nonsense. We need to know how many people really already have it behind them. And we said, if those who we found with those PCR tests are all who had it, then it is sacramentally dangerous. Let's be afraid, let's hide. But if there are 100 people on each positive PCR population who did it and we don't know about them, then the mortality is lower than the flu, and it's not a problem. This didn't just happen to us, it happened to more people. And that's why so-called seroprevalence studies began to be done. Because how to find out if a person has it behind them? To take blood, look at specific antibodies against the coronavirus. At that time, there was no vaccination, so there was no question of whether they were antibodies from a vaccine or from a virus. At that time, all specific antibodies were from a virus. So I said, okay, let's do it. And a nationwide study was published in Czech Republic, where they tested about 20,000 people. These were the stands built at the supermarket, if you remember. They tested 20,000 people with tests that had some declared sensitivity and specificity. These are some technical parameters of those tests. And the results that came out of that nationwide study, they found about 100 positive people among those 20,000. And they didn't realize or realized and lied. We don't know that today. They didn't realize that the tests they used were just lying. Because those tests had such parameters that even if no one had COVID, there must have been several thousand false positives. And since they didn't find any positive ones at all, it was a clear indication that the test was lying. I thought that everyone who had a medical semester behind them would say, sorry, we used bad tests, it doesn't work, we'll do it again. But those people just got on TV and there were all these professors, doctors, teachers standing next to each other. Marian Hajduk from Olomouc, Dušek from Úzis, Primula, then a minister, all medical professors. And they stood there and quoted the results of that failure with a serious face, which were completely nonsensical. And I looked at it and said, this is not possible. If this team is responsible for the epidemic, then it's really over. And to this day I don't know if they understood that it's all nonsense, but for political or other reasons they were not willing to admit it and just lied about the fact that these are real results. Or if they really are so ill-educated that they didn't know what was going on. So this was a moment when I said to myself, this is not possible, we need to start doing something. We got together with Martin Kuba, who was not yet a hate man, he was a former hate man, but he knew everyone in the southern Czech Republic. And we said, let's do our own, well-done seroprevalence study. And we collected about 2,000 people, we took their blood in the hospital, and we calculated the results. And our results, of course, after two years, it was clear that we were telling the truth. Our results were such that for one positive PCR in the population there were about 50-100 people who already had it. That is, it was so that already in five days we had clear information about it, that the real mortality rate of the virus, that is, the infection fatality rate, is not such a terrible disaster, it is below 1%, and we knew from the beginning that it grows exponentially with age, that is, for a younger age group it is not a problem. And we knew this in May 2020. And did anyone care about these data? Well, no, no one cared about the results, which made me think that the failure was not a mistake, that it was an intention. I even remember that Marian Hajduch, who was a professional guarantor or something, he complained to us publicly about how impactful the magazine was and where he had been. So they made certain, non-trivial efforts to say that we are a party of amateurs and no one should look at our results. After two years, it is clear that we met a lot precisely then. While they were out of order, we met a lot precisely. So that also confirmed to me that something was happening that should not be happening. And we gradually started to get together with other people, with Zuzka Krátka and with people about whom I thought that they had a brain in their head and could look at it intelligently. And gradually our association Smys, Smíš, depending on how anyone wants to read it, It still exists, it still works and it has brought the whole four years of the epidemic, it has brought true and undefined information. We came together once, then we brought it to the right dimension, but otherwise we never had to correct anything. We are standing behind what we have done for four years. You have done several studies. Before we get to them, I would like to ask you because you need data for those studies, how difficult or easy it was to get to those data. Because we, for example, with Epoch Times, when we asked for some data for our purposes, for our reporting, it was not easy. That's right. There are a lot of things going on everywhere about how the 21st century will be a century of dates and what was the crop in the 20th century will be the data in the 21st century. Well, I'm a little disappointed because as the first big problem of the 21st century, let's say the coronavirus epidemic, it was supposed to be based on data. The response was supposed to be based on data. We have a lot of methods, whether evidence-based medicine or causal inference. There is a fairly sophisticated machinery on how to get to the right conclusions and the right decision. And so little has been used of that that it shocked me as a data scientist. On the other hand, it is necessary to praise USIS, note that I praise USIS, that it is USIS at all and was able to put the databases together and collect the data. USIS is sitting on a dry deck, Slovaks don't have anything like that, unfortunately. So USIS was collecting, they probably had a lot of work to do the databases, so I believe that there are a lot of smart people who work and then they are angry when we criticize them. But USIS did a lot of good work in collecting the data, but it did a lot of very bad work in how to release the data and how to interpret them. So we made a lot of terrible mistakes and I hope that as soon as the loss of the credibility of the coronavirus passes, we will be able to look at this and make some recommendations on what to do next and what not to do next. There is a problem with the data, but now it is getting better. Recently, USIS released huge individualized, anonymized datasets that are a real treasure. There are many detective stories written there and they are waiting for them to be written and published. So detective stories for those who can read it. What conclusions do you as a detective have to bring us from that? I'm not the only one who can read it and my great disappointment in the coronavirus is that my academic colleagues, who are paid by tax payers to look for the truth and when they find it, they completely give up on it. There are five people in the Czech Republic who have know-how in data science and do something with coronavirus data. I think that's a big waste of academic resources. If I had to sum it up in points, it's a telescopic story, you can talk about it for a week, a day, an hour or five minutes. So what are the main points that can be seen from Czech coronavirus data? First of all, everything is terribly dependent on age. Any data analysis that does not contain age is pointless. We learned very quickly not to read anything that is not a detailed breakdown by age. Death is almost exponentially dependent on age. It has been true for a long time. It is still true that for healthy young people, coronavirus is not a problem. Its mortality is perhaps less than a flu. For old people, it's something completely different. That was the first finding. The second finding for the whole epidemic is the difference between the world with various indicators and the interpretation based on the image of the world. It's very easy to get lost in that. There was a beautiful situation when they looked at the world with an indicator but the indicator did not measure and they replaced the image of the world with the world. This happened many times during the epidemic. The third learning is that when the data is selectively accessible, that is, when you give data to friends and do not give data to those who are afraid that they will not follow the government narrative, there is a lot of misinterpretation. It is very easy to make a lot of mistakes in data analysis. It can only be added by publicizing the data and letting everyone look at it. If I write something from the data and Primula thinks it's wrong, she can take the data, do her analysis and show why it's wrong. This is the only way out. Fortunately, we are slowly heading there. My biggest surprise from data analysis is what we call the healthy vaccine effect. When the vaccines came, half of the people were looking at it as if it were a salvation of the world and the other half was looking at it as if it were a conspiracy theory. It was such an extreme narrative that we were quite interested in what the data said. After a certain fight between a public health insurance company that sent us a breakdown after a few months of the size of vaccinated and unvaccinated cohorts and the number of dead with and without a vaccine, the total mortality, we also learned that looking at the causes of death in the official databases is mildly negligible, because at the beginning it was shown what was going on with COVID. On the other hand, looking at the total mortality, I am almost 100% convinced that the Czech state has a very good order in this. You can't die here without being noticed. So the total deaths are correct. When we got the data from the public health insurance company, I quickly drew how it looks in individual age cohorts. My first conclusion was that it was wrong. They are wrong again, because what we saw is that the vaccinations are immortal. Vaccinated people almost do not die compared to unvaccinated people. For example, in the summer of 2021, where there was no coronavirus, one out of 300 people died of coronavirus. That's a statistical zero. And yet the total mortality in a given age cohort of unvaccinated people was three times higher than that of vaccinated people. We looked at it and said to ourselves, it's impossible, it's stupid. Today we know that it's not stupid, because the public health insurance company gave us aggregated data. Other health insurance companies gave us anonymized individualized data much later, where we calculated it ourselves and it turned out exactly the same. Today we know that this is a reality. And on top of that, there is an article that I think is quite good for historical reasons. We calculated all the possibilities of what it could be. If I remember correctly, the first possibility is that the data is stupid. I thought it was the most likely possibility. If I was a little more educated in evidence-based medicine, I would know that this vaccine effect exists. We came up with many other hypotheses of why it could be. And later we confirmed it with the data we got from the health insurance companies. That was my biggest surprise from all those coronavirus data. Because it has two very important implications. One is that all the guesses of the effectiveness of vaccines, which are full of them, are based on observational data. Exactly on the data we are talking about. But it did not occur to anyone to look at the fact that the baseline, that the vaccinations are completely different people than they are vaccinated. And everyone is still feeding us with reports that if COVID comes, unvaccinated people die much more than vaccinated. But no one will tell us that if there is no COVID, unvaccinated people also die much more than vaccinated. So it is necessary to adjust it to the difference in the baseline. And it's not that simple and no one has done it yet. I'll do it, but I haven't done it yet. So the big implication of this observation is that those things about the effectiveness of vaccines are just things. They are not based on data. The numbers that feed us the effectiveness of vaccines are not true. And let me say one more thing. And the second implication is that studies of the safety of these vaccines also have a problem. Because if the cohort of vaccinated people is much healthier than unvaccinated, it is not easy to compare the prevalence as adverse events between these two groups. So the result of this huge vaccine effect is that the effectiveness of vaccines is overestimated and their safety is overestimated. Well, as you talked about that interpretation and different points of view, I think that at some point in time it came to the point that we as people began to rely on science in quite a lot of things. And maybe if we went a little deeper, we might even consider that it's some kind of new religion in philosophies. And of course, when a society reaches such a point that someone sees that someone is dependent on something or relies on it, they see some kind of power in it, some way, how people could be controlled. Do you think it can be so that science is already used for political purposes? I'm not saying absolutely, but it's already happening. Certainly, but it has always been done. Although we are so old that we still remember the scientific communism, on various cathedrals. We, naturalists, proudly believed that this past, that science in philosophies will be used for political purposes, that it concerns the soft ones, those social sciences, scientific communism. Every new religion that came, always the first thing it did was say, we are very scientific. Before the war, the whole industry, before the second war, the fascist and nazi regimes, a lot of scientists rushed to say that this is all very scientific. This has always been the case. Maybe people outside the science environment are not so aware of it, but they should be aware of it. The fact that the textbooks of history write people on the academic field again, they don't have a tendency to mention it too much. But if you read the real history of the world, you know that this was done regularly and it always happens. If it happens to a greater extent, it's hard to comment on it. But I agree with what you say, that science becomes a religion in the sense that as they die, those traditional, good old religions, there is a hole in the soul of a modern man in the shape of a religion. And whatever is around, it fits quite well into it. I watched my colleagues, some of whom I thought were reasonable, educated, rational people, who really fell into this covidism with the divine tolerance of Martin Luther. And there were a lot of them. I think we underestimate this, that a lot of people who understand what's going on with covid see a gap in it and look for money and services and whatever. And they underestimate the divine tolerance that really came from this science. But I have one more comment on that. There were times when science, you know the emperor in the baker, the baker in the emperor, so that's a pretty good picture of science of that time. They did science as a marvel, some in monasteries and some in garages, some paid by the emperor and others by some madman who wanted to make gold and another madman wanted to be immortal. The result was a fairly diverse environment where everyone did something with a different motivation, paid from different sources. After the two world wars, more or less science and research became expensive and centralized. And all of a sudden, a paid state. And the terrible problem is that when the state falls into some religion and communism, fascism, covidism, global warming, it wants it to look very scientific. And that's how it pushes the whole financing of science in this direction. And scientists are not stupid, they know what they want to hear. So that's where I see a big problem. When the massive financing of science comes from the state with a clear task in science and with censorship and pressure to do something else, the result is this. The published scientific miracle is corrupted. But this is a problem. This civilization is based on technologies and innovations. And these technologies and innovations are based on the fact that we understand how and why nature works. And the moment we start falsifying science, these technologies and innovations will simply stop falling. And that's an important thing. I just want to clarify that we don't want to talk about the aspect of excessive arrogance and fanaticism. I think it's the other way around. I think those good old religions were unfortunately sealed by these modern cults. I'm saying this for the viewers, I understood it from you, just to be sure. And now there is a situation that some scientists or academics are going against the trend. And it has already happened here in the Czech Republic that they just hit, which we can mention Professor Turánka, for example. It's so fresh, relatively. And what about you? How do you feel about it? So I sometimes make fun of it and say that the COVID totality is not so terrible again, because I have been publicly complaining for three years about what comes to mind, and they haven't even thrown me out of work yet, so I shouldn't bring it up again. I feel a lot of public pressure, so every now and then some article comes out like First is lying again and like whatever. So let's say it belongs to freedom of speech, it doesn't bother me in any way. At the University of Palacký in Olomouc, I'm a bit of a controversial figure for other reasons, because even before COVID started, I started to point out one scandal with the falsification of science, which is still increasingly unbelievable. So, of course, those people who didn't want this to be said publicly, they had a beer on me before that, and they are quite happy to add now as a disinformator. On the other hand, the Faculty of Science of the University of Palacký in Olomouc is, in my opinion, an institution that has a super-decent dean, and in addition to how we have been leading this unpleasant debate about falsified science for the last few years, self-governing bodies such as the Academic Senate, the Ethics Commission, and so on. We really have a vibrant academic community where people know what's going on, they're not afraid to say what they think, and in this, as the Faculty of Science of the University of Palacký in Olomouc, thumbs down. I feel good there, and they haven't kicked me out yet. I'll go back a little bit to some of that scientific stupidity and infiltration of some scientific, let's say, narrative, which doesn't always have to be completely substantiated. I think that the group of scientists who are, let's call it scientific mainstream, like to use such a weapon, I would say, and it's called a scientific consensus. What can you tell me about that? One of my colleagues, he said beautifully, Bonmot, so I'll promote him. He said that the number of scientific consensus in history is basically equal to the number of scientific errors. A scientific consensus is the last resort when you run out of arguments. A scientific argument is, look, here are the data, this is what comes out of the data. This is a scientific argument. The moment you can no longer simply show data and say what comes out of it, you start arguing that, hey, Franta thinks the same way. A scientific consensus is a pathetic argument. There have been scientific consensus in history, so remember, right? There used to be a scientific consensus that the Earth is definitely not round, right? Then when Einstein came up with these ideas, there was a scientific consensus that it was all nonsense, that it couldn't be like that. The number of scientific consensus is basically equal to the number of scientific errors. Are you doing anything else besides COVID, where there is such a consensus? Well, I actually got to those coronavirus data like a blind goose. I was originally a mathematical physicist, so I have to solve the flow rate, not the coronavirus. But gradually I moved away from the discipline called inference, or like the correct sorting of data and the correct decision-making. And I found it simply irresistible when suddenly the whole society depends on the methods of correct sorting and correct decision-making, not to look at it. And later I found out that I was the only one who looked at the data. So it still interests me quite a lot. But when I wrote some text, it's somewhere on the blog world or on Smíš, I don't know anymore, probably on the blog world. And there I put together something called mathematical modeling of weapons of mass destruction. There I feel relatively confident in my cramflexes, because I'm a master of mathematical modeling, so I know what I'm talking about. And while a lot of other people, scientists, natural scientists, have religious respect for mathematical modeling, I sometimes make fun of it, as it is in the book of the baker, how he explains how he makes the elixir. He says, you don't believe in the young elixir. No, I make it. So sometimes I make fun of it, that mathematical modelers are the only ones who don't believe in it. Remember the crazy things that were done at the beginning of COVID with those models of epidemic spread. The whole panic was based on that nonsensical model that came from Imperial College from London. And in that article I put together three terrible myths of mathematical modeling. The first was the financial crisis in 2007, which I don't want to talk about now, but it is also related to mathematical modeling. The second is the coronavirus disaster. And the third is, of course, global warming. When the whole consensus, which is now about 99.97% of scientists, the whole consensus is based on mathematical modeling. They are not direct. Because you can't make a planet in a store and try it there. What happens when I add CO2? What happens when I take CO2? It's not based on experimental observation. It's based on mathematical modeling. Very few people see those mathematical models. Those mathematical models simply contain tens of hundreds of different processes that we understand, some more, some less. So of course we have a tendency not to put those we don't understand there. But the fact that we don't understand it doesn't mean it's not important. There are a lot of processes that are badly modeled, and we know it, in the formation of cancer. The formation of cancer is one of the things that no one understands. And so in those models it is very rudimentary at all. The whole panic is based on mathematical modeling. I'm not an expert to say at first glance that it's all bullshit. But after what I've seen about the coronavirus, I think it's very useful to have the widest possible debate about it, so that it's definitely not the case that when someone says that they think the models are not completely relevant, so they kick him out of the university and destroy his Facebook. So I think another big branch that we will have to open and start talking about it honestly are climate models and the whole explanation around global warming. But it's not the only one. It is when, as many people know, what happened to those covid vaccines, where the institutions that are supposed to do a wall between the pharmaceutical industry on one side and the public on the other side, CDC, FDA, these institutions evidently lied and turned their backs. Instead of protecting the public, they protected the pharmaceutical industry. A lot of people understood this. Extensive research is underway in America, which will only intensify. And a lot of people realized that they were simply deceived. The COVID vaccine page was deceived. And a lot of people understand that. And from this point of view, it is not so far from the point of view where other drugs deceive us like this. And there are many Pandora's boxes of modern medicine on which the medical establishment sits with those fat asses and some of them will have to get up. We will learn a lot of interesting things there, but that's good, that's good, because it hasn't been talked about until now. Until now, when someone doubted the scientific consensus in medicine on the effectiveness or safety of some drugs, he was immediately convinced to the ground that scientists, scientists, and now it's not that simple anymore, because everyone saw what was going on behind the covid. So I'm looking forward to it. And you recently returned from the United States. Were you there because of the covid? It's hard to say. No, I wasn't. I took a very clever woman and she got a full-price stipend. So she was there as a scientific employee. And I was there, I have it written in my passport. My role was the dependent. I was the dependent. I basically had to look after the children there. I got a sabbatical from my university, that is, I continued my scientific work and I didn't study for a while. I had time to finish a lot of covid things, which was great. We managed to push through two impactful articles that do not fully correspond to the general narrative. It's hard as hell today. I wouldn't be able to do that here with a normal degree. There was one of them, that the first batch of vaccines were more risky. That's an article that came out now. It came out in the same magazine as the famous Danish study. It came out recently. The buzz around it will still be a bit strong, because our research confirms what the Danes found out. We had a little more detailed data from the Czech SUKLA. I'm surprised they gave us the data, but they gave it to us, which show the incredible thing that the very first batch of covid vaccines and it's not just about Pfizer, it's about pretty much all the brands. I don't want to talk too much. I don't have the picture in front of me. The first batches had a surprisingly high number of suspected side effects. And this number, relative to the size of the batches, this number fell very quickly. Then it was not only negligible, but then it was almost comparable to normal products. We can't explain it completely. We have a lot of hypotheses about why it could be. And basically, for the most likely hypothesis, we consider the failed manufacturing process. It was just that the vaccines were made so quickly that they really had some problems at the beginning. Of course, that's such a shocking hypothesis that we conducted a concessional battle with that magazine about whether the hypothesis will be there or not. We won that concessional battle and the hypothesis is there. We stand by the fact that it is a plausible hypothesis. We don't say that our article proves the failure of the manufacturer, but we say it's a plausible hypothesis. It's unpleasant for the vaccine manufacturers because they have something to explain. And it's even more unpleasant for the regulatory bodies because the regulatory bodies should have noticed it. Look, the American VERS database, the Vaccine Adverse Event Reporting System, is online, it's available. When I talk about the coronavirus, they still leave me somewhere. I just add that it's about reporting undemanding vaccine effects. It's about reporting suspicions of undemanding effects. This is a thing that we again conducted with the COVIDists as a battle for terminology. These are suspicions of undemanding effects, or adverse events. Adverse event is whatever unpleasant thing happens to you after the vaccine. Adverse effect is whatever unpleasant thing the vaccine has caused you. English has a logical terminology. Adverse event, without causal relationship. Adverse effect, with causal relationship. The correct Czech translation is suspicion of undemanding effects is adverse event and the proven undemanding effect is adverse effect. Nobody uses it, including SUKLA. Even SUKLA makes mistakes in this terminology. But every time we write something and accidentally don't write a suspicion, we immediately have a fire on the roof, because we are disinformers. I want to go back to the regulatory bodies. When I talk about COVID data, I have a video from Wayback Machine, where we can see that already in February 2021, a month and a half after the start of the vaccination campaign, there was such an explosion of reports of suspicion of undemanding effects in VERS, that it was already clear that something was wrong. And it's there, and everyone can look at it. In February 2021. So the regulatory bodies really have something to explain, and so does the Czech SUKLA. And finally, what are you preparing for the future? Are you preparing anything? Of course, we are still preparing something. Now, I don't know how it happened, but I will say a big public compliment to Úzis. Úzis, based on Article 106, which was sent by Mr. Veselý. The Law on Freedom of Information. By the way, we have to highly praise Ondřich Kužílka. He is the spiritual father of Article 106, the Law on Freedom of Information. And without Article 106, we don't know anything about the whole COVID. And you would have a very easy job with COVID. Don't tell us anything at all. Article 106 was the most important tool we used for Czech authorities during COVID. They know it and they are quite afraid of Article 106. Úzis decided to publish a completely anonymized set of all citizens of the Czech Republic, which will always contain the date of birth, not the year of birth, so that it is unidentifiable. They have it well anonymized. The year of birth includes the date of all COVID vaccines and the date of a possible death. That is, the data on the total mortality breakdown according to the vaccination status. This is a set of dates that I have been publicly calling for 4 years. As far as I know, no one in the world has ever published it in a country where a guy stole half of this set of dates and is in jail for it. The Czech Úzis published it. The set of dates is on the web and I have big plans with it, of course. The first thing we want to show is our previous article on the health vaccine effect that we made on the basis of health insurance data. We want to show, and we already know, that the total population data is the same. The second interesting thing in the Úzis data are the charges. There are charges. So we have the opportunity to look at individual charges, no matter what is in the bag, no matter what is in Úzis. That's a piece of work I've already done, but I haven't published it yet. So wait, it will be. And the third thing I want to do, now I'm hesitating a little to announce it publicly, because people will want it for me, but I will say it so that I have the motivation to do it. Calculating the effectiveness of the vaccine from observational data is notoriously difficult, if not impossible. As a golden standard, causal inferences, that is, inferences about the actual effectiveness of the vaccine, are prospectively randomized studies. That means you take people, randomly divide them into two halves, you score one, you don't score one, then you watch them, you evaluate them. This was done against the vaccine in 2020, when the Wuhan version of the virus was still circulating, when almost no one had antibodies yet. And it was done in a way that is so holey, that I am willing to publicly say that the studies were falsified. We can talk about how much they were falsified, but there are so many problems there, that it's just on the verge of falsification. And now we're talking about when vaccines were still in development. These are registration studies. These are studies that have to take place so that the FDA approves it, that it can be released. Since then, since the beginning of the vaccination campaign, since 2021, we have all the information about the effectiveness from observational data. And as I said a moment ago, the information is simply wrong, because from observational data it is not possible to compare the mortality of one group and the other group. These are completely different people, those who score and those who do not score. The fact that there was no randomization, but that they decided for themselves whether they wanted to score or not, actually destroys the possibility of making a correct inference about the effectiveness. I came across one very clever guy in America, who has been dealing with this all his life and came up with a method, which, in my opinion, is new, which can be used to make a causal inference of the actual effectiveness of a vaccine from observational data. I really want to try it on the observational data. I'm really looking forward to it, because I'm already so old that I've learned not to think about things. The longer you keep your mouth shut, the better you can work. And today I claim that we know nothing about the effectiveness of any coronavirus vaccine, from the beginning of the vaccination campaign, for any age group, for any endpoint, neither to catch the virus, nor to be hospitalized, have a difficult course, die of COVID. We don't know anything. And the effectiveness can be negative, zero or negative. I don't know anything. And I'm really interested in it, so the sooner I get to it, the better. We'll look forward to it. It was mathematician Tomáš Fierst. Thank you very much for accepting the invitation. Thank you very much for inviting me and don't worry, you'll be the first to know when you count something.