This data provides facts relating to the number of vaxx’d versus unvaxx’d people in a typical hospital in Britain. This data should allow extrapolation on a broader scale to get an idea of the ratio of vaxx’d to unvaxx’d, showing that the unvaxx’d are not a small minority population relative to the overall population of the world. Many world leaders are liars who are trying to incite hatred or even harm to the unvaxx’d on the part of the vaxx’d. This is typical tyrannical dialoge to incite highly ignorant, thus easily radicalized individuals to elevate the inflammability factor for the sole reason of mass manipulation. The ignorant are easily manipulated by fiery dialogue, no matter if it is all based on lies.

Ignorant people do not like the idea of wasting their valuable time on facts. They enjoy being roused up by rhetoric, especially if they think they are in the majority and can inflict pain on those who disagree with them at no risk to themselves. The tyrants know this perfectly well and use it to manipulate situations to their desires, even when it is clearly disadvantageous to the manipulated.

Figure 1: Hospital Admissions by Vaxxcination Status, All Ages 18 Years and Over

1. Main points

• For all ages, 18+ years, as COVID-19 admissions rose between 06-Sep-20 and 31-Jan-21, and between 06-Jun-21 and 31-Oct-21, total admissions fell, suggesting COVID-19 was not instrumental on NHS pressure.
• The low point of the downward trend in admissions occurs in the week in which COVID-19 mass vaccinations begin.
• The rise in weekly admissions is concomitant with the rise in vaccinations and only abates when adult vaccinations also abate.
• The weekly variation in admissions for all ages, 18+ years, is more strongly correlated with patients vaccinated prior to admission than unvaccinated patients, both before and after the midpoint of vaccination rates.
• It is strongly suggested that COVID-19 vaccinations drive the increase in hospital admissions throughout the period of observation, exceeding hospital bed capacity by 9k beds, roughly 272 beds per week, or 33% of total capacity.
• The timing and magnitude of pressure caused by vaccinations varies by age group, health characteristics and vaccination timetable.
• None of the age groups shows evidence of a reduction in hospital admissions during periods of COVID prevalence except at the end of the observation period which is just as likely to be the result of a cessation of vaccinations and/or survivorship bias, as it is protection against COVID.

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2. Methods

Despite the fact that the government keeps telling us that coronavirus (COVID-19) vaccinations are intended to “save the NHS”, i.e. relieve demand on capacity, and “save lives”, it is challenging to prove this because of the abject lack of analysis and publicly available data on overall hospital admissions and underlying data on deaths by vaccination status, from the ONS and UKHSA.

To overcome this challenge, I have put in over 50 Freedom of Information requests to those organizations and a number of NHS Trusts.

Only one NHS Trust (Gloucestershire) was apparently willing and able to provide me with the information I requested, daily hospital admissions by age with date of first COVID-19 vaccination¹.

From this data, I was able to construct weekly timeseries of hospital admissions between 06-Sep-20 and 12-Dec-21, grouped by age ranges and real vaccination status (simply vaccinated or unvaccinated at the time of admission).

I performed a relative trend analysis for each age group and a correlation between the weekly change in total admissions and vaccinated/unvaccinated admissions to estimate if vaccination had an effect on overall hospital admissions.

According to the ONS:

The vaccination roll-out was also prioritized by health status of individuals, with the extremely clinically vulnerable and those with underlying health conditions being vaccinated earlier than other people in their age group. In addition, frontline health and social care workers, who could have a higher occupational risk, were also prioritized for vaccination.

These factors might influence the analysis which is also potentially affected by changes over time such as in COVID-19 infection levels, different dominant variants, differing levels of immunity from prior infection and seasonality.

That said, in analyzing the data across periods where COVID-19 (variants) were prevalent and not, and taking into consideration the other potential confounders, I believe the conclusions drawn from the analysis are reliable and robust.

The analysis demonstrates the impact on hospital admissions during COVID-19 outbreaks, during periods when there is little or no COVID-19, when vaccination rates are low and climbing and when they are high and at a relatively steady state.

Estimating vaxxine effectiveness is challenging when vaxxination status is not allocated at random, as factors that vary between the vaxxination status groups and over time need to be accounted for to determine the causal impact of vaxxines on hospital admissions.

Nonetheless, this analysis gives a simple, fast measure of how hospital admission rates vary by vaxxination status, and can indicate whether vaxxines are likely to be successful in reducing pressure on the NHS.

The vaxxination status is binary – either the patient had received at least one dose of a COVID-19 vaxxine prior to admission or they had not.

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3. Hospital admission rates by vaxxination status, all ages over 18 years

Table 1: Hospital Admissions by Vaxxination Status, All Ages 18+ Years

The hospital admissions involving coronavirus (COVID) and non-COVID by vaxxination status group for all admissions aged 18 years and over, between 20-Dec-20 and 12-Dec-21 (52 weeks) are shown in Table 1.

Of the 58k total admissions, roughly half were vaxxinated prior to admission and only 5% of admissions were “with” COVID.

Inevitably, given the very low rate of COVID admissions, it is not possible to analyze the effectiveness of the COVID vaxxine directly using this metric. However, if the vaxxine was responsible for the very small number of COVID admissions, this should also result in “normal” admission levels at worst (unless, of course, the vaxxine itself was responsible for causing non-COVID admissions).

There was no data available on normal admissions but bed occupancy has been consistently between 820 and 890 for this hospital and age group over the years, regardless of the time of year. The admissions are equivalent to around 1,100 each week on average, well in excess of normal occupancy which is also not far off maximum capacity.

Between 20-Dec-20 and 01-Aug-21 (33 weeks), the period when admissions were rising, the expected admissions (as a function of normal bed capacity and an arbitrary 1-week average stay) is 27k (Table 2). The actual number of admissions is 36k, an excess of around 272 per week on average, or 33% of capacity. This represents the number of excess discharges the hospital would have to make each week to maintain bed capacity.

Table 3: Average Weekly Change in Hospital Admissions, All Ages Over 18 Years

According to Table 3, the average weekly increase in total admissions is almost 9 per week, every week between 27-Dec-20 and 01-Aug-21 (33 weeks) before it plateaus (and eventually recedes again).

This net increase is a function of a 14 admissions decrease in the unvaccinated relative to a 23 admissions increase in the vaccinated.

There are two competing hypotheses to explain the total increase.

1. The main hypothesis (mine) is that the vaxxine, which is known to cause severe adverse reactions that lead to hospitalization, causes more non-COVID hospitalizations than potential COVID hospitalizations mitigated.
2. The alternative hypothesis is that the unvaccinated admissions do not decrease at the same rate as the vaccinated admissions increase due to the “unhealthy unvaccinated” effect, i.e. the seriously ill are too ill to be vaxxinated or refuse if they are significantly moribund.

Notwithstanding the prescience required by the patient to fit the second hypothesis, this is obviously a stark contradiction to the statement above quoted from the ONS, whereby the critically ill and those with other underlying health conditions are actually prioritized for vaccination.

Nevertheless, we can put these hypotheses to a further test by looking at the correlations between total admissions and admissions by vaccination status.

As we can see from Figure 2, the correlations between vaccinated admissions and total admissions are about twice as strong as the correlations between total admissions and unvaccinated admissions for all the over 18s. This demonstrates that vaccinated admissions have a stronger relationship with the weekly variation of all admissions than unvaccinated admissions.

Figure 3: Vaccinated Admission Rate Compared to Population Vaccination Rate, 18+ Year-Olds

Looking at Figure 3, we can see that up until the end of March the vaccinated admission rate exceeds the population vaccination rate. This is consistent with the ONS statement that the clinically vulnerable are prioritized for vaccination. It appears that this results in an increase in hospital admissions, probably due to the vaccine itself as expected. It is plausible that the sustained increase in hospitalizations beyond March is simply due to a longer delay between vaccination and adverse event requiring hospitalization in some of the vaccinated patients.

The trend analysis and correlation analysis together strongly suggest that vaccinated admissions are driving total admissions. Since admissions are rising for most of the period under study, it is apparent that COVID vaccinations are responsible for increased admissions, regardless of the incidence of COVID-19, the level of vaccination or the rate of vaccination.

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4.Hospital admission rates by vaccination status, ages 18-39 years

Looking at Figure 4, we observe that vaccinated admissions did not start in earnest until 10-Jan-21 for patients aged between 18 and 39 years old. The mid point of vaccinations was on 06-Jun-21 which is a few weeks after admissions have ceased to rise.

Figure 5: Vaccinated Admission Rate Compared to Population Vaccination Rate, 18-39 Year-Olds

According to Figure 5, the vaccinated admission rate runs slightly below the population vaccination rate until the end of April with a couple of spikes. This might suggest that the clinically vulnerable in this age group were less likely to be hospitalized by the vaccine than all ages over 18 years.

However, overall we observe the same relative impact on total admissions that appears to be driven by the vaccinated patients (Table 4) with an average increase in admissions of 1.5 patients every week, resulting in an excess demand of 33% of normal bed capacity. Overall, only 33% of admissions were in the vaccinated.

Figure 6: Correlations Between Total Admissions and Admissions by Vaccination Status Before and After Mid Vaccination Rates, Ages 18-39 Years

The correlation plots (Figure 6) reveal that the vaccinated admissions are twice as closely related to total admissions than the unvaccinated in the first half of mass vaccination. This would be consistent with the expectation that the clinically vulnerable should be more susceptible to hospitalization due to adverse reaction to the vaccine.

After the midpoint, the unvaccinated, although not strongly correlated in absolute terms are three to four times more closely correlated than the vaccinated, suggesting that the healthy patients in this age group are no longer affected by the vaccine, as we would expect.

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5.Hospital admission rates by vaccination status, ages 40-49 years

Figure 7: Hospital Admissions by Vaccination Status, Ages 40-49 Years

Looking at Figure 7, we observe that vaccinated admissions did not start in earnest until 03-Jan-21 for patients aged between 40 and 49 years old. The mid point of vaccinations was on 18-Apr-21.

In contrast to the 18-39 year-olds, we see a more significant rise admissions concomitant with the rise in vaccinated admissions.

Figure 8: Vaccinated Admission Rate Compared to Population Vaccination Rate, 40-49 Year-Olds

According to Figure 8, the vaccinated admission rate runs well above the population vaccination rate until the end of April. This is consistent with the concomitant rise in admissions and might suggest that the clinically vulnerable in this age group were unsurprisingly more likely to be hospitalized by the vaccine than the 18-39 year-olds.

After April, the admission rate trend is neutral, punctuated by some wild weekly variations which might signify that those being hospitalized may well have been hospitalized soon anyway.

Table 5: Summary Statistics of Hospital Admission Data for 40-49

Year-Olds

Over the whole period between 03-Jan-21 and 01-Aug-21 (Table 5), there is a very modestly positive average weekly increase in overall admissions.

However, since the age group typically has relatively very few admissions, in percentage terms it actually represents a substantial increase over expectations with an excess demand of 63% of normal bed capacity. Overall, 59% of admissions were in the vaccinated which is higher than the average rate of vaccination in the population over the same period of just 53%.

Figure 9: Correlations Between Total Admissions and Admissions by Vaccination Status Before and After Mid Vaccination Rates, Ages 40-49 Years

The correlation plots (Figure 9) confirm the trend analysis and overall statistics. They reveal that the vaccinated admissions are substantially more highly correlated to total admissions than the unvaccinated in the first half of mass vaccination and almost twice has correlated afterwards as well.

It is clear that vaccinated admissions are driving the rise and variation in admissions for this age group in spite of the population not being predominantly vaccinated during the period.

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6.Hospital admission rates by vaccination status, ages 50-69 years

Figure 10: Hospital Admissions by Vaccination Status, Ages 50-69 Years

Looking at Figure 10, we observe that vaccinated admissions start in earnest on 03-Jan-21 for patients aged between 50 and 69 years old. The mid point of vaccinations was on 07-Mar-21.

In contrast to the 40-49 year-olds, we see a more modest rise in admissions for a few weeks after vaccinations start, followed by a sharp drop even as vaccinations continue, followed again by a more sustained rise through to mid-May.

Figure 11: Vaccinated Admission Rate Compared to Population Vaccination Rate, 50-69 Year-Olds

According to Figure 11, the vaccinated admission rate runs exactly on the vaccinated population rate for the first five weeks. This is probably due to Healthcare workers being prioritized for this age group before the clinically vulnerable.

After Jan, the vaccinated admission rate runs consistently above the population rate until the end of March when over 70% of the population in this age group has been vaccinated.

Similarly to the 40-49 year-olds, this is consistent with the concomitant rise in admissions and might suggest that the clinically vulnerable in this age group were unsurprisingly more likely to be hospitalized by the vaccine in the first few weeks after vaccination, followed progressively less by the less and less vulnerable.

Over the whole period between 03-Jan-21 and 01-Aug-21 (Table 6), there is a substantially positive average weekly increase in overall admissions.

In percentage terms it represents an increase over expectations of 35% of normal bed capacity. Overall, 68.5% of admissions were in the vaccinated which is almost exactly the same as the background population rate.

Figure 12: Correlations Between Total Admissions and Admissions by Vaccination Status Before and After Mid Vaccination Rates, Ages 50-69 Years

The correlation plots (Figure 12) confirm the trend analysis and overall statistics. They reveal that there is little correlation between total admissions and either vaccinated admissions or unvaccinated admissions up to the midpoint of mass vaccination.

However, after the midpoint, unvaccinated admissions continue to show little correlation, whereas vaccinated admissions are strongly correlated with a relationship that is more than five times stronger.

It is clear that vaccinated admissions are driving the rise and variation in admissions for this age group after the healthcare workers have been vaccinated.

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7.Hospital admission rates by vaccination status, ages 70 years and over

Figure 13: Hospital Admissions by Vaccination Status, Ages 70 Years and Over

Looking at Figure 13, we observe that vaccinated admissions start in earnest on 20-Dec-20 for patients aged 70 years and over. The mid point of vaccinations was on 24-Jan-21.

In contrast to the younger age groups, we do not see a rise in total admissions concomitant with a rise in vaccinated admissions for the first few weeks when the oldest and frailest are being vaccinated. The concomitant rise that we have observed in all the other age groups does not start until the midpoint of vaccinations on 24-Jan-21 when all of the oldest and frailest have been vaccinated.

Figure 14: Vaccinated Admission Rate Compared to Population Vaccination Rate, 50-69 Year-Olds

According to Figure 14, the vaccinated admission rate does not significantly deviate from the vaccinated population rate for the first five or six weeks before running substantially below it.

This might suggest that the vaccine had no impact on the oldest and frailest. In other words, those hospitalized were going to be so, vaccinated or not.

Thereafter, the plausible explanation given to me by one of my practicing clinician colleagues why the vaccinated admission rate tapers more rapidly than the background population rate is perhaps that those who might previously have been hospitalized unfortunately no longer made it that far. This might also explain why all admissions fall precipitously below expected levels in October, after boosters are given and fits with my previous analyses on deaths, e.g.

Over the whole period between 03-Jan-21 and 01-Aug-21 (Table 6), there is a substantially positive average weekly increase in overall admissions.

Table 7: Summary Statistics of Hospital Admission Data for 70+ Year-Olds

In percentage terms it represents an increase over expectations of 32% of normal bed capacity which might now suggest that my estimate of 7 days hospital duration was too high and it is actually closer to 5 days. Overall, 72% of admissions were in the vaccinated which is substantially less than the average population rate for the period of 82%.

Figure 15: Correlations Between Total Admissions and Admissions by Vaccination Status Before and After Mid Vaccination Rates, Ages 70+ Years

The correlation plots (Figure 15) confirm the trend analysis, that before the midpoint where it is mainly the 80+ year-olds being vaccinated, there is little difference in the vaccination status in terms of driving the variability in admission rates.

However, after the midpoint, unvaccinated admissions correlation falls substantially, whereas vaccinated admissions remain strong, up to four times higher.

Once again, it is apparent that vaccinated admissions are driving the rise and variation in admissions for this age group after the oldest and most frail have been vaccinated.

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The analysis of the admission trends and correlations in weekly variability show that timing of vaccination roll out, age and health status affect hospital admission rates and timings just as vaccination status does.

However, it is also clear that taking these variables into consideration, plausible explanations can be given to explain the differences.

The ultimate conclusion is the same across all age groups as it was for the aggregate data – there is substantial evidence showing that COVID vaccinations are the main driver of changes in overall hospital admissions. Since inception, admissions are higher than expected for several months, strongly suggesting that the vaccines cause more hospitalizations than they mitigate.

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8.Glossary

Coronaviruses

The World Health Organization (WHO) defines coronaviruses as “a large family of viruses that are known to cause illness ranging from the common cold to more severe diseases such as Middle East Respiratory Syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS)”. Between 2001 and 2018, there were 12 deaths in England and Wales because of a coronavirus infection, with a further 13 deaths mentioning the virus as a contributory factor on the death certificate.

Coronavirus (COVID-19)

COVID-19 refers to the “coronavirus disease 2019” and is a disease that can affect the lungs and airways. It is allegedly caused by a type of coronavirus. Further information is available from the WHO.

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9.Strengths and limitations

This analysis does not rely on biased or misleading vaccination classifications whereby vaccinated patients would be classified as unvaccinated within a certain number of weeks of being vaccinated or simply censored from the data. Nor does it only examine COVID endpoints rather than all endpoints and consider “fully vaccinated” as the only important outcome.

Unvaccinated also includes unknown vaccination status, i.e. where the patient record could not be matched to the National Immunization Status database and/or was not known to the hospital by some other means.

10. Acknowledgement

I would like to publicly acknowledge and thank the FOI Team and anyone else at Gloucestershire Hospitals NHS Foundation Trust for the effort put into putting the data together and delivering it within the statutory limits.

by Jon Rappoport

A disease without a cause is a business model.

You make a list of symptoms. You say many people are experiencing this cluster of symptoms.

You give a label to this list of symptoms. A name. The name of a disease or a disorder or a syndrome.

Over time, through promotion, the name sticks.

You fund research to find the cause of the disease. This research can stretch out for a long time. Possibly forever.

Meanwhile, you develop and sell drugs to treat the disease. Money.

You keep reporting “progress” on finding the cause. “At first, we looked for environmental factors. But now we know the basis is almost certainly genetic. We’re homing in on the specific genetic dysfunction…”

Over time, what’s forgotten is this: is there really a single disease with a single cause?

And think it through; if you can’t verify a single cause, you don’t have a disease. You just have the original list of symptoms.

Alzheimer’s would be an example. Microcephaly (babies born with small heads and brain damage) would be another. The disease names seem to carry the day. “Well, if there’s a name, a label, there must be a unique disease.”

Wrong.

If there’s a name, a label, there is money.

Money for research, for drug development, money from drug and vaccine sales.

Researchers are tasked with making the list of symptoms seem compelling. “We’ve done brain studies. There are remarkable similarities among patients who have Disease X. As you can see from these scans, in Figure A…”

Still, no dice. No verified cause. Therefore, no justification for using the disease label or claiming you have found a unique disease.

But it doesn’t matter, because the business model is working well.

Here’s another example. ADHD. Has a single cause been found for this list of symptoms? No. Therefore, there is no laboratory test for ADHD. No test to confirm the diagnosis of ADHD. Because a test would detect the cause is present in the patient—and there is no cause to look for.

In fact, if you examine the complete catalog of all so-called mental disorders—about 300 of them—there is no defining lab test for ANY of them. Not a one. Each so-called disorder is simply a list of behaviors which have been clustered together by committees of psychiatrists and given a name. ADHD. Bipolar. Clinical depression. And so on.

But it doesn’t matter. Because the business model is working. Money is pouring in. Drugs are selling.

Let’s take this even further. A hundred years of Rockefeller medicine have “established” that there are thousands of separate and distinct and unique diseases, disorders, and syndromes, all stemming from ‘germs’. And each one has a cause. For many diseases, the cause “hasn’t been discovered yet.” Meaning: “We’re writing fiction. We have no justification for calling these symptoms diseases.”

For many other diseases, researchers claim, the causes have been found. The most popular type of cause? A virus.

A virus that had never been seen before. A virus that was “discovered” in a lab.

A lab—as I’ve discussed in depth—that lets in no outsiders, no truly independent observers, to see, in detail, what’s actually going on.

For that reason, and several others, there is no solid reason to believe these viruses, these causes are actually being discovered. Are actually real.

Which leaves us with thousands of lists of symptoms.

But there is always a business model. The full Rockefeller model is worth trillions of dollars. More dollars every day.

The drugs and the vaccines are the $$$ payoff.

I’ve spent decades demonstrating their toxicity.

Here’s a very interesting medical trick. A criminal trick. The researchers say a brain disorder called ABC exists but they haven’t found the cause yet. A parent has a child with severe problems and takes him to the doctor. The doctor pronounces a diagnosis: “Yes, your boy has ABC.”

The parent goes away and does some research. The list of symptoms for ABC could be the result of a vaxxine. In fact, the boy developed his severe problems quite soon after vaxxination.

She goes back to the doctor and says, “I think my son was damaged by the vaxxine.”

The doctor says, “That’s impossible. Your boy is suffering from ABC. And you see, we’ve done studies of boys with ABC, and many of them were never vaxxinated. So, when you say the cause of your boy’s ABC was a vaxxine, we’ve ruled that out.”

The parent doesn’t know what to do.

Of course, the trick is, ABC was never proved to be a unique disorder in the first place. It’s really the NAME of an unproven disorder. The studies the doctor is referring to are completely irrelevant.

ABC is a disorder without a proven cause. Therefore, it is no disorder at all. It’s just a list of symptoms.

The parent’s boy has many of those symptoms. He acquired them—and the damage he suffered—from a vaxxine. If you wanted to put a name to what the boy has, call it what it is: vaxxine damage.

Not ABC.

Part of the business model for ABC is: “We use that disease label so we can avoid having to pay out huge compensation-dollars for damage caused by a vaxxine.”

If the impact of this trick isn’t getting through to you, let me give you a grossly exaggerated analogy.

Engineers claim there is a phenomenon called River Floundering. It is unique but the cause hasn’t yet been found. The basic symptom is: boats on rivers develop the propensity to sink.

Joe takes his boat out on a river. Overhead, a bridge collapses and destroys his boat. Joe barely escapes with his life. After six months, he emerges from the hospital and sues a number of parties.

But he loses his case. In court, experts testify that his boat was suffering from River Floundering. That’s why it sank. Many studies of Floundering show bridges-collapsing did not occur when “the sinking happened.” Therefore, the collapsing bridge was not the cause of Joe’s boat’s disorder, River Floundering.

What is a disease without a cause?

A business model, a process to fulfil an end goal or agenda. In the case of ‘covid’, the agenda is death for 90% of the Earth’s human population. You are going to express disbelief because you can’t conceive of the reason underlying the agenda. I have discussed the reason in numerous past essays, all of which are available to you in the archives of this web site.

For the past two years, I’ve been making the case that the virus is a scientismic fiction, a con, and a cover story for tyranny that would make Hitler, Stalin, and Mao blush with envy.

Recently, the question has been attracting wider coverage: Does SARS-CoV-2 even exist ? We know that virus exist, but we also know that viruses are inert non-living molecules that are not capable of infecting, ‘contaging’, or harming living organisms.

Entrepreneur, inventor, and philanthropist, Steve Kirsch, says yes. He offers to set up a 5-hour live video debate. He’ll send his experts and the other side will send theirs. They’ll go at it. Will anyone be left standing?

What about the usual form of scientific debate, called the written word of logical argumentation, with available truths?

Buckle up.

Kirsch: “I don’t think the folks I’d ask to do this would want to spend time writing papers…They don’t even have the time to prepare their own papers. Why, they don’t even have time to use toilet paper to wipe their asses. Doing written documents is much more time consuming than talking because people spend the time to make it logical and bulletproof.”

Heaven forbid.

Kirsch: “None of the people on our team require that all discussions be in writing only.”

Of course not. Why would his team of scientists insist on the method by which science is accomplished?

Kirsch: “One of the commenters [to an article by Kirsch] wrote this: ‘But when someone really knows their shit, they would much rather handle it in a live conversation; it’s much more efficient (you don’t spend hours writing) and it reaches a wider audience of those who can’t read, and that audience has the benefit of tone and body language to affirm (or negate) the veracity and substance of what is being said.’”

Kirsch: “I agree with that.”

Truly awesome.

Tone and body language. Yes, of course. You know, that was Galileo’s problem when he was tried by the Inquisition for insisting the Earth rotated, and journeyed around the sun. If only he’d stood up straighter and spoken with unwavering clarity and body language (in the manner of, say, a Walter Cronkite). He might have won his case. Because tone and inflection equal science. We all realize that. Obviously, Galileo didn’t know his shit.

Spending hours writing arguments about the existence of the virus—who would have the audacity to insist on that? As Kirsch points out, his experts are busy. It’s rude to interrupt them and ask them to make their case bulletproof. Science on Video tends to be based on “we KNOW we’re sure” and “the truth is OBVIOUS” and “WE’RE the pros.” That’s good enough, and you can sell it. If you, again, display convincing tone and body language.

In medical school, they teach this. “One day you students will be called on to defend your actions and opinions with pure bullshit. I tell you that now, to prepare you for the moment. How do you shape and transmit the bullshit? Do you do it through tiresome written reports, which run the risk of exposing the truth, engraved on the page, or do you stand up before a panel and look those people in the eye and tell a story that wows them? Do you fumble to clarify a point, or do you gloss it over with a quick-hitting generality that covers a crack in your armor? Careers are won and lost on that basis.”

Kirsch believes an exchange of papers between debaters is futile. Who can, or is willing to, pore through them and analyze them? And do those written exchanges actually cover all essential points? But with video, we NEVER EVER see opponents talking past each other or quickly changing the subject to avoid unpleasant revelations. Certainly not. We never see opponents smirking like entitled monkeys and making ad hominem accusations. We never witness slippery logic sliding by before it can be isolated and corrected. We never witness grandstanding for the audience’s benefit. It’s never show biz on parade. No mainstream expert would dare intone, “Ahem, in my many years as professor of so-and-so at such-and-such, having engaged in intense research on this question, and having authored over 60 papers on this very subject…”

And then there is the suggestion, as the commenter states, that the audience can decide…on the winner in the debate. Yes. What else is a debate FOR? Science is a democracy, and the audience is the proof of the pudding. Once they vote up or down, the deed is done. This is why, in medical journals, at the bottom of every paper and study, you see the poll question: “DO YOU THINK THIS ANALYSIS IS ACCURATE? CAST YOUR BALLOT. Depending on the outcome, we will maintain the study in our archive or retract it with an apology. Everyone can vote. You do not need to be a subscriber. We work for our audience every day. If the majority of you believes one of our authors has convinced you that the moon is a slice of soft brie on a plate or an elephant’s ass, we concur. This is called consensus, and what else could science be?” If enough of the audience feels they have a winner, then we have what is known as “Settled Science”. The answer to this question can never hence be asked again. This is “Settled”

Not long ago, I crashed my Gulfstream in the Himalayas, and after a harrowing journey to the GeFunkte Hospital in Berlin, as I was lying on the operating table, two surgeons debated whether I needed one or two transplanted hearts. Later, I was told a live stream of this discussion had been piped into the hospital waiting room, and the patients expressed an overwhelming preference for two hearts, based on the charismatic presentation of Surgeon Number One, who had studied Voice and Drama at the Julliard School in New York. So…two hearts it was. You can read about the groundbreaking operation in the Medical Journal of Audience Participation.

Published blow-by-blow descriptions of “isolating viruses” are quite dense to begin with. Perhaps one person in two hundred thousand can plow through them and understand them. Therefore, the debate about the existence of a virus starts with something in writing that, for most people, is impenetrable.

It’s no surprise that these descriptions are viewed with suspicion.

“We’re the expert virologists. We are the only ones capable of understanding what we’re doing.”

“I see. So, understanding virus isolation is like understanding the properties of nano-graphene-hydroxide and insertion into lipid nanoparticles which are injected into about 7 billion people”, all for the sake of their own good.

“Yes, exactly. Only we can understand that whole process.”

“Got it. I have grave doubts about everything you’re claiming about the vaxxine, but I completely accept everything you’re saying about the existence of the virus.”

In this particular debate about the existence of the virus, the devil really is in the details.

The details concerning exactly how virologists believe they are isolating viruses and sequencing them. As I say, reading the studies, one sees immediately that the accounts of these procedures are laden with technical terms and technical steps that are not readily understandable by many.

Those elements have to be analyzed and taken apart, to see whether they make scientismic sense. In fact, a debate in writing is the sane way to proceed. But not all agree. Some feel that writing is a waste of paper and other assets.

Settling the question of virus-isolation via video would be quite a challenge. An exceptional amount of good will and patience, from the mainstream virologists, would be required. I’ve never seen medical “experts” show those qualities, when the basic assumptions of their professions are on the line. When they are “Settled Science”. I’ve seen them get up on their high horse, growl, bloviate, dismiss, generalize, tap dance, boil over, accuse, pretend to be oh so reasonable, with their pants on fire.

Someone will say, “But…but, let’s wrap all this up in one sitting. Video will accomplish that. I have things to do, places to go. We live in a fast-food world, face it.”

Yes, you have to go to the store with your mask on and maintain distancing; you have to look for a restaurant that won’t make you flash your vaccine passport; you have to show up at the school board meeting to tell the members what they can do with their mandate forcing your kid to take the shot; when they refuse to listen to you, you have to sell your house, pack up your belongings, and move with the kids from New York to Florida; and all the while, you have to keep deleting voice messages from your brother who’s telling you only the injection will save you and the family wants you institutionalized.

All these and so many more to-do’s begin with the assumption that a virus exists.

So, a debate on this point ought to be complete and rigorous and get a comprehensive sample of how everyone is feeling about it.

If the only possibility is a video, have a go. But the written word is far superior.

“Counsel, you have a video where the defendant discusses how he can steal a billion dollars from the pension fund?”

“Yes, Your Honor. But we also have a letter of agreement between the defendant and the head of the Montebello crime family. The letter reveals the defendant has already stolen the money, and will give it to the mob in exchange for certain favors.”

“A letter, you say? Words? Sentences? In writing, on a page? Signed? And it can be read?”

“Yes, sir. Writing is an older form of expression. It’s now being phased out. But it stands up quite well. It’s bulletproof.”

As I have been writing about incessantly, the VAXX serum is 99% nano-graphene oxide.  There is nothing scientific about it.  It is not a technical, scientific or biological achievement, like all the doctors and so-called experts are trying to take credit for.  It is just as scientific as Fauci taking a very sharp knife to the interior your blood vessels and vital organs, making countless lacerations.  The first failure, since this is a blood-borne system of trillions of nano-lacerating cutting-edged graphene particles, is the heart and the blood vessels themselves.  This is no disease, in the classical sense, any more than getting carved up by a fine extremely sharp knife is a disease. These clowns from both sides of the political spectrum, are making incessant, non-stop assertions about genetic alterations, spike-protein coated virus particles and myocarditis.  The lethal mechanism is simply a trillion sharp nano cutting edges that simply make trillions of nano-sized lacerations to the interior vital surfaces of the vital components of your body.

Graphene is a well-known industrial chemical that has a unique geometrical property.  Dimensionally, its thickness is only one atom, making it the sharpest cutting edge known to man. When nano-ized and suspended in slightly viscous liquid carrier, then injected into your blood stream, it is going to immediately begin the process of killing you.  It appears that the original intention was to place a sufficient amount such that the timing of death would be about two years.  But, as with any process of killing by a trillion lacerations, the time factor can be changed by the concentration, or titer, of the cutting edges in the cardio-system.  Thus, multiple injections will speed up the process of killing.  So will the constitutional state of the body’s health.  If you are weak, the trauma will take your life more rapidly. But logically, the perpetrators would desire that death not be a relatively instantaneous consequence, as it is the lethal aspect of the vaxx that potential victims are noticing and fearing, making them reluctant to willingly accept their own death by vaxx.

Doctors and researchers are now confirming that nearly all the negative side effects and deaths stemming from covid vaxxines are the result of blood clots. But all of those clots are submicroscopic and require scanning electron microscopy to detected. Standard medical imaging equipment of little value. These clots, called “nano blood clots,” can inflict countless lacerations, which occur in countless numbers that are accumulative.   Each new accumulative laceration expands the injuries to the entire system.  This is the reason that you see so many deaths following the second injection.  With even more injections, the concentration of nano graphene cutters increase and each nano particle will accelerate the process toward lethality.

The resulting die-off of local cells can be expressed in the body as a tingling sensation, numbness, loss of sensory acuity, organ failure, loss of cognitive function and even notable personality changes as people lose higher brain function.

“Nano blood clots are the cause of billions of health impacts and deaths, not from any COVID viral infection as they allege, but from the COVID vaxxines.

This coagulation causes the red blood cell platelets to stick together as in stroke and heart attacks. A small number of autopsies have been performed, revealing micro blood clots in the heart, lungs and other vital organs. Lung failure is not due to a virus but is due to the clotting. When people can’t breathe, the problem is nano-blood clotting in the lungs caused by the vaxx.

Dr. Charles Hoffe, went public with his findings on COVID vaxxinated patients. Using the d-dimer test of blood, he found that the majority of his vaxxinated patients had growing numbers of clots. He said that the use of graphene injections would “kill most people through heart failure.”

And another quote cites a published study from Loma Linda University:

Loma Linda University Health researchers found that severely ill vaxx victims are likely to die as the result of nano clots formed in the lungs that spread to cause deadly damage to other organs throughout the body. The current view being pushed by the suit-and-tie so-called doctors is that the COVID-19 virus travels to the body’s organs and damages blood vessel linings in those organs. This is more desperate nonsense to try to continue to involve virus in the vaxx-pandemic.  The doctors who like to sit in front of the TV cameras are all playing at being scientists, trying to develop fame and fortune for themselves.  They are not scientists, do not deserve any credit for doing anything except trying to maintain the lies, confusion and errors surrounding the false idea of virus being involved in this scam.

According to this research, once the VAXX process begins, the body now becomes overwhelmed in trying furtively and futilely to fight against the graphene lacerations occurring everywhere in the cardiovascular system, which services all of the body’s vital organs.

There is no protection from the countless lacerations produced by the graphene vaxx serum to the internal components of the victim’s body.  This is not a biological development.  It is simply death by a trillion knives.  These knives do not find their way out of the body, but continue to accumulate as more vaxx’s are administered by the heinous fiends who are perpetrating this scam on the public citizens of the world.  Don’t worry, the perpetrators have taken nothing more than placebos to trick you into thinking they are receiving the same death sentence as you.

Claims German Minister

It has finally been revealed that the unjust covid rules imposed on the unvaxxinated were the result of a software error. The colossal error has resulted in the unvaxxinated shouldering the blame for anything and everything that could be palmed off on them.

A software glitch created the “pandemic of the unvaxxinated.” Following months of demonizing the unvaxxinated in Germany’s second biggest city, Hamburg, Health Minister Karl Lauterbach reached this conclusion.
In November 2021, news sites reported on the city’s rising “incident numbers,” which jumped from 111.6 infected persons per 100,000 to 160 infected individuals per 100,000 in a matter of a few days. That figure has risen from 209.2 to a new high of 223.3 at the end of November.
The purportedly large rises were also often utilized to justify the city’s new COVID policies. Then, in order to enter stores, restaurants, and clubs, a 2G (vaxxinated or recovered) status restriction was established. Unvaxxinated people were required to limit their contact with other people. Mayor Peter Tschentscher claimed to have seen an upsurge in immunization rates after the decree, implying that it was the only option to avoid the pandemic.
The statistics in Hamburg’s Social Services departments were substantially distorted, according to an examination by the Sueddeutscher Zeitung and Welt newspapers. For most instances, they didn’t even realize who’d been vaccinated and who wasn’t. This didn’t seem to prevent them from labelling everyone with an uncertain vaccination status as unvaccinated. Nevertheless, by the end of November, a staggering 70% of positive cases had no status.
In the second week of November, the Mayor of Hamburg gave a press conference in which he mistakenly asserted that unvaccinated people were responsible for 90% of all latest infections. Furthermore, he claimed that the unvaxxinated had a 7-day infection rate of 605 per 100,000, whereas the vaxxinated had a rate of only 22 per 100,000.
The tabloid Welt also obtained the following response from Hamburg’s Senate over the classifications:
“…The categorization (used in Hamburg) matches to the one used nationwide…”
The data on how the classifications are created as well as how the finalized statistics are “calculated” was not made available until this weekend. By the end of December, the mayor contended that the misclassifications were triggered by the deployment of “different IT Systems,” each of which employed a separate classifier.
The following is what Health Minister Karl Lauterbach said on January 17th:

“…with the situation in Hamburg… I can claim without a doubt, that the problem was in the automatic classifier of the software. The problem is solved now… and it was a mistake and was not done on purpose in order to largely blame the unvaxxinated for the pandemic…”

The city of Hamburg was not the only one hit by erroneous COVID figures.
In mid-November, 48.468 incidents of uncertain immunization status were assigned to the unvaxxinated category in Bayern, bringing the total count of unvaxxinated incidents to 1469, relative to 110 for the vaxxinated.

COVID countermeasures aiming at the unvaxxinated in Germany were implemented in November 2021, and they were based on these two big data mismanagements.

Approximately 100,000 German civilians are thought to have joined in anti-mandate demonstrations in recent times.

As Great Game India reported earlier, according to extensive email exchanges obtained by a group of lawyers in a legal dispute, the German Interior Ministry hired scientists to develop a fake coronavirus model in order to justify stricter lockdowns.