Last year, the general election in the Czech Republic was won by Andrej Babis, a distinctly Czech version of Donald Trump. And things started to happen. During the confirmation hearing of the government, Babis – in his inimitable mixture of Czecho-Slovak dialect – said that the whole Covid response under his previous government was a mistake, that he was cheated by the “so-called” experts and that the “wonderful” vaccines provided by the European Union proved not so wonderful after all.
Shortly after this revelation, Jindrich Rajchl – a lawyer and a member of Parliament for a smaller government party – organized a conference under the title “Three years from Covid.” The large room of the Chamber was completely full with the public. I was invited to do what I have been doing for the past five years: Talk about data. This is what I said (in italics, I have added some explanation for the American readers).
Good afternoon, ladies and gentlemen, I would like to thank Jindřich for organizing this seminar, thank you for the invitation, and thank the other panelists for their courage and perseverance.
Today—or rather already with Wednesday’s comment by Prime Minister Andrej Babiš that we should have taken the Swedish path—the third period of our Covid match begins. The first period consisted of a global loss of sanity itself. I called the second period the Great Covid Silence, when many players fervently hoped that how they played in the first period would be forgotten. The last period will—so I firmly hope—consist of catharsis and lessons learned. Let us hope no overtime will be needed.
During the Covid loss of sanity, all the key pillars of Western societies failed.
The executive branch failed, arbitrarily, absurdly, and counterproductively restricting people’s fundamental rights and freedoms.
The legislative branch failed, passively watching and repeatedly issuing blank cheques to the executive in the form of recurring states of emergency.
The judicial branch failed, which (with the exception of one panel of the Supreme Administrative Court) refused to defend natural law and instead—like in previous totalitarian regimes—retreated into servile legal formalism.
The media failed in an unexpected and repulsive way, led by the public-service media, which instead of truthfully informing the public and scrutinizing those in power, lied on political order.
Physicians failed, who instead of treating the sick either shut down their practices or served the pharmaceutical–industrial complex as its sales department.
And, as so many times in the past, at the head of the march toward new slavery marched state-funded scientists, who on political order dressed Covidism into the shroud of Science.
Covid arrived in the 21st century, which many claim will be the century of data. If it showed anything, however, it is that our ability to collect data far exceeds our ability to work with it meaningfully. And because working with data has brought me (somewhat involuntarily) into the ranks of Covid dissenters, I want to tell you several fascinating Covid stories that are written in the data but have not been fully appreciated yet.
Mathematical Modelling Is a Weapon of Mass Destruction
A large part of the Covid repression here and abroad was based on predictive modelling. Before the vote on extending the state of emergency, the Institute of Health Information and Statistics (UZIS) presented MPs with this prediction (slide 16). This slide contains many interesting stories that need to be explored in more detail.
The figure is in Czech, sorry for that. The horizontal axis shows time in months from July 2020 to April 2021. The vertical axis shows the number of new cases of Covid per day (i.e., positive PCR tests) in the Czech Republic. Yellow dots represent real data and the red curve is the “prediction” of the Ministry of Health model. The “prediction” was made around the time of the black arrow. The blue text says “the effect of counter-measures, dangerous development averted” (it makes no more sense in Czech, the translation is verbatim). The disclaimer in italics at the bottom of the slide is explained below.
First, it is necessary to realize that all models of epidemic spreading were some variants of a computer game called SIR (Susceptible–Infected–Recovered). It is a computer game based on the model of a perfectly mixed gas. People are modelled as balls randomly colliding with each other. If an infected ball bumps into a susceptible ball, two infected balls come out. An infected ball eventually spontaneously turns into a recovered one. And that’s all. There is no notion of space in the model, no cities, no schools or factories, nobody sleeps, nobody eats, etc. Essentially, it is a model of a simple chemical reaction taking place in a closed vessel filled with an ideal gas. It has almost nothing in common with the reality of an unknown virus spreading in space and time in a society that reacts in various ways to the novel situation.
Moreover, all SIR models behave similarly—they produce a single wave of infections. But when multiple waves occur in reality, instead of abandoning the model as falsified, various experts start hallucinating that reality deviates from the model due to our “management of the epidemic.”
A fundamental problem of these models is overfitting. The models have several knobs (free parameters) that can be turned until the model behaves exactly as its author wishes. Some professor then shows unfortunate MPs (who last studied mathematics in high school) a graph whose parameters were fine-tuned to match past data. The poor MPs have no chance of understanding that this is pure overfitting—that the model parameters were adjusted after the real data had been known. Therefore, the agreement between the model and reality in the past says nothing at all about the quality of predictions. If I told you what numbers were drawn in the lottery last week, would you believe me that I could correctly predict what will be drawn next week?
Notice the inconspicuous disclaimer written in small print below the graph, which says that the model is not intended for predictions. Yet at the top, a prediction is displayed in bold.
The prediction is, moreover, clearly absurd: the model predicted that the number of newly infected people would reach 37,000 cases per day by the end of March 2021, i.e., about 370 cases per 100,000 inhabitants. Since the beginning of the epidemic up to that point, it had never happened anywhere in the world that the number of newly confirmed cases exceeded 170 cases per 100,000 inhabitants. The prediction was therefore completely unrealistic—but it served its purpose. Frightened MPs approved another state of emergency.
Also, notice the black arrow that suggests that the discrepancy between the prediction and reality was somehow caused by the effect of the measures adopted by the government. I can assure listeners that nothing of the sort follows either from real data or from the model. On the contrary, the spring Covid wave had already peaked by the end of February. The hard lockdown starting in March could not have changed anything.
What lesson follows from all of this? Do not try to predict future behavior of systems that you do not understand by means of mathematical models. Follow the data, not naïve computer games.
I once wrote a long article about this for Brownstone Institute.
A major lesson from this modelling catastrophe applies particularly to the issue of climate change, where we are making the same mistake. We have bet the future of the entire civilization on the prediction of a mathematical model—because the hypothesis that the climate is warming catastrophically due to human CO₂ emissions is nothing other than the prediction of a mathematical model. During Covid, reality was merciful and showed us the absurdity of predictive models within weeks. With climate models, we had to wait decades before it became clear that the catastrophic predictions of the “warm-mongers” were wrong.
We Still Know Almost Nothing about the Effectiveness of mRNA “Vaccines”
To this day, we know almost nothing about the real effectiveness of mRNA products. In the Czech Republic, we administered almost 19 million doses of Covid “vaccines;” globally, several billion. Most of them were experimental gene products. The CDC even had to change the definition of a vaccine so that these products would fit it. It is fascinating that we still do not understand what we know and do not know about the effectiveness of these products.
The media hammered into our heads that mRNA “vaccines” were 95% effective. Where did that number come from and what did it mean? It came from a randomized study with about 20,000 participants in each arm. Symptomatic infection was the endpoint. In the active arm, 8 people fell ill; in the placebo arm, 162 people did. Thus, the vaccine reduced the risk of symptomatic infection from 0.88% to 0.04%, i.e., by 0.84%—yes, by less than one percentage point. For marketing reasons, this was communicated to the public in the following way: 0.84 out of 0.88 is 95%, right? The public assumed that this meant that 95% of vaccinated people were protected from infection. That is almost the exact opposite of the truth. All principles of evidence-based medicine say that reporting risk reduction in such a misleading way is unacceptable.
Later we learned that Pfizer used a completely different substance in the registration study. The clinical trial was conducted with a pure laboratory-produced substance, but mass vaccination used a substance produced by bacterial cultures. Such “vaccines” are contaminated with bacterial DNA and endotoxins. Therefore, we cannot say anything about the safety and effectiveness of mRNA “vaccines” on the basis of the registration study—it was a different substance.
The registration study took place during the reign of the Wuhan variant of the virus which had become extinct by the time of the mass vaccination. So, we knew nothing about the “vaccine” effectiveness against other variants, either.
Moreover, the British Medical Journal reported an alarming case of scientific misconduct in the Pfizer registration trial.
After it became clear that vaccines do not prevent infection (in the summer 2021 at the latest), the establishment changed the tune: Vaccines may not prevent infection or transmission but they prevent severe disease, hospitalization, and death. However, we never saw convincing evidence for this claim, because it was not investigated in randomized trials.
But we do have plenty of observation data, so we should know, right? The media were full of reports that only the unvaccinated were dying. We, however, carefully examined the data and repeatedly demanded the publication of complete datasets. Eventually, we succeeded and summarized the findings in a unique study that showed many fascinating aspects of the entire vaccination campaign.
At this point, I repeated at length in the Chamber what I wrote for Brownstone Institute here. The readers of BI seem to be better informed than the members of the Czech Parliament!
This study shows that it is almost impossible to say anything about vaccine effectiveness solely by observing the population. The vaccinated and the unvaccinated are simply completely different people. But since 2021, no randomized studies have been conducted anymore. Yet, only randomized studies can reveal the true effectiveness of vaccines. After vaccinating billions of people with an experimental gene product, we still know next to nothing about the true effectiveness of the products against clinically relevant endpoints. We cannot rule out that the effectiveness was negative.
And the lesson? Our ability to collect data has far surpassed our ability to make correct inference, predictions, and decisions. A huge amount of work still lies ahead, and the sooner we start, the better for us. Czech authorities have finally published record level data on deaths (so far only for women), including causes of death. We can finally try to estimate the true effectiveness of the Covid “vaccines” against death from Covid. It would be good if this was finally done in a more systematic way, other than by us—a group of enthusiasts—during nights and for free.
Further Detective Stories Written in the Covid Data
At the beginning of 2022, the fertility of Czech women unexpectedly began to decline. From the level of 1.83 children per woman in 2021, the Total Fertility Rate (TFR) started to fall by about ten percent per year, so that by the end of 2025 it dropped below 1.3. While the media and the Covid establishment maintained that this certainly had nothing to do with the vaccines and that Putin was to blame, we persistently demanded data. Eventually, we obtained the data and published a globally unique study that showed that women vaccinated against Covid had, for some reason, about one-third fewer children than would correspond to their share in the population.
In other words, if vaccinated women had children at the same rate as the unvaccinated ones, no decline in fertility would have occurred. The data confirmed that vaccination is associated with reduced fertility in a strong way. This is not a hypothesis—it is a fact. The big question remains whether this association is causal, i.e., whether vaccination actually prevents conception through some mechanism, or whether it is merely behavioral—that is, vaccinated women for some reason stopped wanting children. This needs to be investigated.
And even in the case of this mystery, the readers of BI were informed before the Czech government.
For a long time, we sought data regarding reports of adverse events of the Covid vaccines. When we finally obtained it, we showed that the numbers of reported adverse events differed fundamentally among batches. The very first batches of vaccines that arrived at the beginning of 2021 had unbelievably high numbers of adverse events, while later batches had merely high numbers of reports. These data most likely point to instability in the manufacturing process, and it is necessary to find out what happened.
And I could go on like this for several more hours.
What Comes Next
So, what comes next? In many places abroad, the third period has already begun. In the United States, a regime change occurred, which to a large extent was a reaction to Covid tyranny. American public health is undergoing the largest and most inspiring transformation in the last 100 years. Investigations are under way in Britain, Australia, Canada, New Zealand, and many other countries; even in Germany things are starting to move. Much is happening in Slovakia. It is inconceivable that our country should remain the only one that refuses to play in the third period. I assume that the incoming government understands this.
We have a unique opportunity to learn from this sudden and global loss of sanity and to make substantial changes in the following areas.
In public health, where we should return to the principles of Evidence-Based Medicine, i.e., the interconnection of the physician’s judgment, the free will of a truthfully informed patient, and data from high-quality and unmanipulated studies. We must limit the role of various pressure groups such as the Czech Medical Chamber and the Czech Society for Vaccinology. It is time to leave the WHO, radically decentralize the entire healthcare system, and return decision-making to treating physicians.
In education, where we should focus on supporting correct reasoning, logical thinking, cultivating dialogue, and especially teaching how to work with data. The Western educational system has clearly failed, because independently and critically thinking people formed a clear minority in society during the Covid insanity.
Above all in science and research, where it is necessary to radically cut the toxic dependence of academic research on state funding. When the state goes mad—which happens regularly in Central Europe—the loss of reason is immediately transmitted through funding into the research environment. Scientists are not stupid and they quickly understand what those who pay them currently want to hear. Academia—instead of remaining an oasis of reason, freedom, and the search for truth—marched at the head of the procession of madmen and dressed the Covid insanity in a shroud of scientific legitimacy. We saw it in the past with fascism and communism, and the same happened during Covidism. The global warming madness is yet another example.
This country, in my opinion, needs a platform that would help us understand what actually happened during Covid and why. I am not calling for an investigative commission, because those are usually composed along party lines, which leads not to understanding but to partisan infighting. It would probably be more reasonable to appoint a Government Commissioner for understanding the Covid era and let him or her assemble the platform independently.
We owe understanding, catharsis, and lessons learned to the thousands of people whom we allowed to die during Covid and to their relatives and loved ones. We owe it to our children whose education, social life, and mental health we significantly disrupted. We owe it to the tens of thousands whose health was damaged by reckless and indiscriminate forced application of an experimental gene “vaccine.” And we owe it to those who were right throughout those five tragicomic years but were censored, persecuted, and harassed.
Many of them are sitting in this hall and together with me hope that today we are truly entering the final period in our match with the Covid insanity. We still have a chance at least for a draw in this match. But if we do not even try to understand what happened and why, it will be a crushing defeat. And it will be repeated in future matches, first with the team of global warm-mongers, who are already warming up in the locker rooms.
Thank you for your attention. Stay healthy and cheerful.
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