7.5.3 Epidemiology 101 in the COVID-19 Era

Introduction

The concept of epidemiology dates back to Hippocrates, who observed that by and large, there were two types of diseases: endemic diseases, which occur continually in the population, and epidemic diseases, whose occurrences are sporadic, such as infections with unprecedented symptoms.

Although the science of epidemiology has made much progress since antiquity, understanding the occurrence and the evolution of a new disease that creates significant morbidity and mortality is still a huge challenge. The occurrence of a new pathogen—its transmission in human populations, the interaction with the infected host leading to diseases of varying seriousness, and the ultimate resolution of an epidemic as it progresses to the endemic state—is a highly complex multifactorial phenomena whose driving forces are difficult to identify. The relative contribution of the various factors is also very difficult to measure. We have learned a lot since the beginning of the modern scientific adventure, yet our knowledge is still very limited.

The paradigm of modern reductionist materialism, starting with Descartes, is that the world is like a machine whose parts interact with one another according to specific laws of nature written in the language of mathematics. According to this paradigm, all we need to do is identify the components and discover how they interact with one another. With that understanding, it‘s assumed we can control the world.

That worldview has several shortfalls as it applies, for example, to the science of epidemiology and as it converges at the intersection of statistics, physics, biology, engineering, psychology, sociology, and politics. The many unknowns in all of the parts of this idealized machine, let alone in the ways they interact, make any modelling attempts to describe and predict how that works is at best naive, if not totally misleading, in many respects.

A flurry of mainstream media commentators took centre stage to project the illusion that epidemiology was a mature science able to predict the evolution and control of pandemics with sophisticated models fuelled by powerful computing. The reality was that what we learned about epidemiology over the past decades had not made our understanding of the COVID-19 pandemic any different than previous pandemics. This illusion of knowledge fooled many people into thinking that they could attribute the rise and fall of epidemic waves to specific human interventions or lack thereof.

The main innovation during the COVID-19 pandemic was the questionable deployment of the RT-PCR diagnostic as a proxy to follow the epidemic waves. Strangely, instead of monitoring the waves of sick people, public health focused their attention on the presence of a viral genetic sequence to define a “case,” irrespective of its consequence on morbidity and mortality. Moreover, despite the poor predictive power of the RT-PCR tests to inform disease progression, they were used in attributing deaths from COVID-19 to respiratory illnesses of all kinds and without formal demonstration. This created systematic errors of attribution that biased official statistics all over the world.

Over-reliance on Modelling

We were misled by models. Without delving into too many details, it is necessary to discuss the concept of viral transmission. The physicochemical interaction of an ill-defined biological agent, the virus—which is sensitive to all kinds of environmental conditions, like UV and humidity—travels in the air to enter the airway of another biological being, which will interact with this virus in different ways, depending on the robustness of the mucosal immune system challenged with an unknown viral load.

Combining all these parameters, which we cannot properly measure in a web of interactions, quickly becomes a combinatorial explosion of probabilities that are impossible to determine. Assuming we could measure all of the parameters, which we cannot, modelling is then challenged by the mathematical laws governing the interaction of the different components.

A relatively simple example is the law of fluid dynamics to estimate the virus transmission in the air, depending on the gravitational force, wind velocity, and humidity. As the equations cannot be fully resolved, we have to assume several measurements. Without those precise measures, what are we going to input as a modelling parameter?

The bottom line is that modelling is a useful tool to generate a working hypothesis, based on approximate assessment, to be validated by empirical measures. Modelling cannot make accurate predictions of complex systems. The limitation is not only the computing power but also the uncertainty about the input parameters to run the model. If the assumptions are incorrect, the output of the model is useless for prediction and is referred to as a GIGO model (garbage in, garbage out).

The over-reliance on models can be due to the difficulty in testing model predictions experimentally or due to the time involved to collect data before the model‘s accuracy can be formally assessed. Nonetheless, when a modeller consistently misses the target by a long shot, it would be wise to question the assumptions and mathematical process used to produce its prediction.

A stunning example was the misleading prediction that resulted from inaccurate modelling. This modelling team had been repeatedly off target in their predictions for more than 10 years: notably in the last flu pandemic of 2009 and the mad cow disease debacle that led to the unnecessary slaughter of cattle herds and huge economic losses for UK farmers.

Proper Monitoring of Pandemic Progression

It follows that we must be very wary of modelling. The only way to determine if any human intervention will influence the progression of a pandemic is to carry out well-designed observational studies in randomized trials whenever possible to eliminate the unknown influence of confounding factors, while carefully avoiding random errors and monitoring systematic errors resulting from selection or information biases.

Only true experts with established credentials and a track record can generate and properly interpret those epidemiological studies. Many of those experts informed us of what to expect by making analogies with similar epidemics of the past. The inescapable conclusion of the careful analyses of the best experts, like Prof. Denis Rancourt and Pierre Chaillot, was that there were no pandemics of extraordinary magnitude in 2020–2023, as the authorities promulgated for those three years.

The scale of the pandemic was exaggerated, and in rich countries—with notable exceptions such as Sweden, Japan, and a few American states—the health measures deployed caused more damage to the health of populations than COVID-19 itself. Although Sweden had a bad episode in long-term care homes during the first wave in early 2020, they refrained from imposing lockdowns and ended up with much better public health outcomes.

Statistics compiled at the global level showed that the median age of people who died of COVID-19 exceeded the age of life expectancy and that the vast majority of seriously ill people had several other pathologies. This led Richard Horton, editor-in-chief of the Lancet, to declare in his September 26, 2020, op-ed that the COVID-19 pandemic was truly a syndemic. That is, COVID-19 disproportionately affected the most vulnerable. Healthy young people were more than 1000 times less likely to be seriously ill or die from it. In these circumstances, he was advocating for more nuanced public health measures, as did the signatories to the Great Barrington Declaration.

Furthermore, the peak of excess deaths observed in different regions of the world coincided with the drastic measures put in place to manage the perceived threat. It‘s almost impossible that those excess deaths were caused by the dissemination of a deadly virus which didn‘t spread across borders and remained in discrete locations within a state or across states.

The drastic measures included (1) withholding early treatments, (2) inappropriate use of ventilators that restored anoxia in COVID-19 patients deprived of early treatments, (3) withholding antibiotics to treat incidental bacterial pneumonia, and (4) comfort end-of-life treatments for patients deemed incurable.

An accurate account of the COVID-19 pandemic revealed the story of a statistics fraud. What did they count? The definition of pandemic was changed. It became a statistical pandemic with cases. After changing the definition in 2009 by ignoring the gravity criteria and only counting numbers of sick people, the WHO further changed it in 2020 to count cases—including people who may not have been sick. Looking at all-cause mortality adjusted for the age pyramid, we note that 2020 was among the lowest since that statistic has been recorded. Nothing happened, anywhere in the world.

As for hospital saturation: for example, in France, even with the dubious attribution of hospitalization due to COVID-19, those patients made up only about two per cent of hospital occupancy. The only apparent hospital saturation was induced by an administrative decision to send all the respiratory-symptom patients (up to three million in France) to only seven of the 1500 hospitals.

Because of the planning already in place in anticipation of COVID-19 waves, hospitals were emptied, and people avoided them due to fear. As a result, the occupancy was much lower for many months—for example, 50 per cent empty in April 2020. No pandemic was evident when measured by an increase in respiratory disease by the sentinel network, a monitoring system. To put the data in perspective, during the worst previously reported pandemics, the sentinel system recorded up to 800 cases per 100,000 population, like in 2014–2015. During the COVID-19 pandemic, the number never exceeded 150 cases per 100,000 population. If one adhered to the definition of epidemic as an excess number of sick people, there was no epidemic in 2020, 2021, and 2022.

Pandemic by Alleged Fraudulent Testing and Attribution of COVID-19 Cases

Mainstream media reported alarming surges in COVID-19 deaths worldwide, which were determined not solely through initial RT-PCR testing but by the WHO‘s coding ICD-11, implemented on January 31, 2020. This method was seen as a broad and scientifically questionable way of attributing deaths to COVID-19, often based on superficial symptom diagnoses with or without confirmation via RT-PCR testing.

When all respiratory infections were broadly categorized as COVID-19 cases, it led to the creation of misleading “epidemic“ curves on the Our World in Data website. In reality, the spread of a respiratory virus infection did not align with synchronous events in numerous countries; some were merely delayed in adopting the ICD-11 code, while others had not yet implemented rigorous health measures.

The statistics showed that following the implementation of ICD-11, all other respiratory diseases seemed to gradually disappear and become COVID-19, even when the virus had not been detected. Perverse financial incentives appeared to trigger a diligent transfer of coding attribution.

There was an increase in deaths in some places, like France, that had instituted strict measures in April 2020, but not in other countries, like Germany, that didn‘t implement those measures. The so-called first waves occurred in a minority of countries or regions. This could be seen all across Europe and even across different provinces in France, as only 14 out of 100 provinces showed excess deaths, and the spike of excess deaths correlated with the stringency of implementing health measures. The same thing happened in the USA.

Furthermore, excess deaths were overrepresented in “deaths at home“ due to the lack of treatment because sick people refrained from going to the hospital. Even if most were attributed to COVID-19, there was no proof because autopsies were not performed.

For example, there were 5200 deaths at home during the COVID-19 period in France, and 4800 of those deaths were from “stroke and heart attack, non-treated“ during the same period in a given database, while another database reported up to 6000 deaths from stroke and heart attack. Therefore, every one of the 5200 deaths at home could be accounted for by the lack of treatment.

In the same vein, the reported 5000 excess deaths in LTC homes for the elderly were equivalent to the number of people treated with midazolam instead of Rivotril, as the stock had been exhausted by the U.K., the USA, and Canada.

The rationale was that COVID-19 was a deadly, untreatable disease. Therefore, hospitals would be saturated, and there would be no room to treat the sick elderly; instead the reasoning was give them palliative care for this deadly incurable disease.

In France, the most prevalent place of excess deaths was in hospital. An incredible spike of 6000 out of 7000 excess deaths in three days was reported, with 3000 on the same day. This can only be explained by two reasons: (1) people coming to the hospital were already very sick, and (2) the common treatment in ICU to put patients on ventilators was associated with a high mortality rate. The three causes described—denial of early treatments, ventilators, and palliative care—accounted for the bulk of excess deaths.

The RT-PCR tests were the driver of the statistical fraud. A test was not the reality. For example, the pregnancy test although very accurate has both false positive and false negative results. If we tested everyone, we would get falsely positive pregnant men and falsely negative pregnant women.

We required additional medical data to establish the reliability of the RT-PCR test. By testing everyone, including those without symptoms, we identified a substantial group of asymptomatic COVID-19 cases who were even believed to be capable of spreading the virus. This raised questions about how they could transmit the illness if they didn‘t carry an infectious virus but rather viral RNA sequences. If non-ill individuals who tested positive could transmit their non-sickness to others, it implied that everyone had the potential to be a source of infection. Moreover, the RT-PCR test had not undergone formal validation with a gold standard, as was the case with pregnancy tests.

Say the PCR test is 95 per cent reliable, and we tested everybody indiscriminately and found that both asymptomatic and symptomatic people were positive, on average, less than 5 per cent of the time. We would say, then, that the test lacks coherence.

Positive PCR testing over-represented the asymptomatic—detecting, more frequently, people who were not sick while missing people with symptoms—75 per cent of the time. Therefore, these symptoms were most likely not representative of COVID-19 disease. It was a “case-demic.“

During the Omicron phase, the percentage of positive RT-PCR tests increased dramatically to more than 30 per cent in France. Was it truly due to increased viral circulation, or was it from modifying the testing protocol?

In France, the combination of RT-PCR tests, COVID-19 vaccines, and the vaccine pass produced strange epidemic curves that were better explained by human behaviour because the RT-PCR tests were not coherent. Putting the vaccine pass in place created an artificial vaccine efficacy. The only efficacy seen in the randomized clinical trials (RCTs) in France was a reduction in RT-PCR positive tests.

If vaccinated people were not obliged to get tested, a bias of positivity of unvaccinated people who were obliged to get tested would be created. Similarly, when it was revealed that the vaccine was not preventing transmission and that the vaccine efficacy waned over time, people who were anxious about the possibility of infection, got boosted. At the same time, people who refused boosters, which were mandatory for an up-to-date vaccine pass, had to be tested more often. As a result, the boosted people tested less than the double-vaccinated or the unvaccinated, and that created the illusion that the booster worked.

However, as soon as the vaccine pass was lifted, the curves inverted because the boosted people, being more anxious, were testing themselves more often than the double-vaccinated or unvaccinated people that no longer got tested when it was not mandatory. It was all a statistical illusion.

The chilling implication of this rigorous statistical analysis of the official data was that it was a sham pandemic perpetrated by the military and administrative state through a sophisticated psychological operation against the civilian population.

While some people may have died from a virus, which probably escaped from the Wuhan lab, the deaths did not show up in the excess-death data. Deaths from the three other declared pandemics since WW II also did not show up in excess-death data. But wars did, intense heat waves did, and earthquakes did, yet proclaimed pandemics did not, except the Spanish flu.

A pandemic should be characterized by a significant excess of sick and dead people, not unreliable RT-PCR positive cases. The definition was perverted by an unvalidated display of pandemic waves that instilled fear in people and compelled them to submit to never-before-accepted NPIs as a prelude to the vaccination campaign that was sold as a relief to the unsustainable harmful health measures.

Recommendations

Due to the confusion caused by improper testing for COVID-19, particularly using unvalidated RT-PCR testing, the following recommendations were made:

A. Pause the use of RT-PCR or rapid antigen testing when it is not accompanied by a thorough medical evaluation of disease symptoms.

B. Conduct a rigorous validation of RT-PCR testing, including standardized cultivation of the active virus. Establish a defined threshold for the number of amplification cycles that show due use.

Considering the confusion that arose from the lack of transparency in official public data, the following recommendations are added:

C. Ensure that all government data is consistently and transparently shared with the public for independent evaluation by qualified experts in epidemiology and statistics.

D. Make any disparities between data analysis, done by the government and data analysis done by independent citizens, subject to review by an impartial advisory committee composed of experts in epidemiology and data analysis. This committee should be regularly vetted through public forums to maintain transparency and accountability.