Thursday, 10 February 2022 — Dr Malcolm Kendrick
Some observations on the infection fatality rate of COVID19
[Mainly that it does not really exist]
When COVID struck the world two years ago, or thereabouts, the first thing that happened was rather unfortunate. Namely, the instant and widespread distortion, nay destruction, of data. This happened so fast that it became almost impossible to know what on earth was going on. Who to believe … what to believe?
I have never been so naïve as to think that we are not constantly subjected to certain ‘truths’, which may or may not be true. After all, I have been battling against the dreaded ‘cholesterol hypothesis’ for decades. In doing so I have become something of an expert in recognising seriously distorted data when I see it.
I have learned to search for things not said, which are usually far more important than the things that are. I have also learned to treat the words used with great distrust. Words such as ‘fact’ for example. Facts have a disturbing tendency to crumble under pressure … note to the dreaded dementors, sorry fact checkers.
However, I felt I had become pretty expert in navigating the games played. I had learned to sail the stormy waters of scientific truths, or facts, reasonably well. Then came COVID, and the world of fact distortion achieved warp drive. Alleged facts flashed past so fast, and in such great numbers, that it all became a blur.
In this blog I will attempt to remove some of the blur surrounding the issue which became key to ‘The Great COVID Wars’. This is the Infection fatality rate (IFR) of COVID19.
You may not feel this was central to everything that occurred, or remains so, but I hope to convince you that it is the single most important ‘fact’ of them all. The keystone. Also, the one most jealously guarded by the fact checkers. ‘Put your weapon down, place both hands in the air, and step away from your IFR.’
To begin. There was a time when epidemiologists, with regard to infectious diseases, used two different terms. Infection fatality rate (IFR) and case fatality rate (CFR). Although it has to be said that the distinction between the two was never exactly black and white.
After all, how do you decide when someone who is ‘infected ‘with a disease, reaches the point when they become a ‘case?’ Historically this happened when someone became so unwell that they were admitted to hospital. Whereupon the disease itself would be diagnosed with a test of some sort – sometimes. Sometimes clinical signs and symptoms were all that were used.
Which means that ‘cases’ have always been somewhat easier to count and compare. However, no-one has ever really known how many people were infected in the first place. By which I mean those people who were not seen anywhere, by anyone, and so never managed to the reach the status of a ‘case.’
In general, those with a mild infection just lay in bed, for a while feeling a bit sorry for themselves. Indeed, the advice for those with ‘flu’ always used to be to stay at home, drink plenty, and take some medication to control the temp and the aches and pains. This represents the traditional three Ps management technique. ‘Take two paracetamol and piss off.’ [Paracetamol is called acetomenophin in the US – take two As and piss off … nah, doesn’t really work]
Ergo, those with few symptoms, or no symptoms, were never seen or counted. So, the Infection Fatality Rate (IFR), which represent the total number of people who become infected, who then die, has always been subject to a great deal of guesswork.
A whole series of the underlying problems with defining IFR [and also CFR] were highlighted in the paper ‘case fatality risk of influenza A (HIN1pdm09): a systemic review.’The authors looked at the Swine Flu epidemic of 2009, and also reviewed data on infection and case fatality rates from the past.
I shall paraphrasetheir main findings. ‘We haven’t a clue what the infection fatality rate was for this, or any other flu. In truth, neither does anyone else, because the data are complete rubbish.’
Their actual conclusion, couched in more scientific language:
‘A consensus is needed on how to define and measure the seriousness of infection before the next pandemic.’[1]
Did this consensus ever happen? You must be joking.
As you may have noticed, we have begun the move into very blurry waters indeed. You may ask how it is possible to compare the Infection Fatality Rate of COVID19 with previous influenza epidemics, when we have no idea what the IFR rate of previous influenza epidemics may have been.
Despite such great uncertainty, this IFR rapidly become a red line issue for the COVID wars.
On one side were the CDC, Fauci, Neil Ferguson and Imperial College London – and suchlike. The ‘establishment’ – the ‘experts’. They confidently stated, from the very beginning, that the Infection Fatality Rate of COVID19 was around one per cent. Meaning that for every one hundred people infected, one person would die (on average).
Quite how they knew this is beyond any real understanding? They say modelling. I say guesswork. Which, in truth, is pretty much the same thing. A brand new, never seen before disease, and they just knew what the IFR was.
This was also at a time before any accurate testing existed, and we had no idea how many people had actually been infected? Indeed, at this point, they were primarily relying on information from China … Oh well, at least we know that Chinese data are always fully reliable … thank God. Just don’t mention that pesky laboratory in Wuhan, or gain of function research. Or pretty much anything else that emanates from China, in truth.
On the other side were…. Well, there wasn’t really another side so to speak of. A rag tag bunch of researchers and epidemiologists who were fascinated by the data coming in, and what it was saying. It included those such as Professor John Ioannidis and Professor Carl Heneghan at the Centre of Evidence Based Medicine in Oxford, and suchlike.
I just watched with interest, at first. My own bias has always been to be very wary of any expert consensus that springs into life. This is because it will almost always be a slave to the inherent problems with human thinking that ride roughshod over a disinterested pursuit of the truth. Particularly in a crisis.
Problems such as: groupthink, confirmation bias, fast thinking rather than slow thinking, deference to ‘experts’, the desperate need to come up with ‘the answer’ and stick to it, and suchlike. We all know what they are. They all came into play, as expected.
Anyway, a key question here was, how did their one per cent figure compare with more common or garden influenza? This is very hard to say. I have seen figures of 0.67% for the flu epidemic of 1967. I have seen far less. ‘Spanish flu’, the big daddy of them all, was estimated to have had an IFR of around two to three per cent.
But how accurate can these figures be? In the paper I quoted above, the IFR estimates for swine flu (HIN1pdm09) ranged from less than one death, per hundred thousand infections, to more than ten thousand. Yes, from one in a hundred thousand, all the way up to ten per cent. This is what scientists call…. A pretty wide range. You could call it other things.
Cutting to the chase, the reality is that, at the start of the COVID19 epidemic we had no idea what the IFR of a severe influenza epidemic was, nor did we know the IFR of COVID19. You would think that this would make any comparison somewhat tricky.
However, the mainstream consensus rapidly coalesced around two ‘facts’.
Fact one: a severe seasonal influenza has an IFR of around point one per cent. Or, to put it another way, one death per one thousand infections.
Fact two: COVID19 has an IFR of around one per cent. Which meant that COVID19 was going to be ten times as deadly. This, then, became our starting point.
What would this mean in the real world?
The UK has a population of sixty-seven million people. Which meant that if everyone were infected, we would end up with almost exactly two thirds of a million deaths. Which is one per cent of the entire population. The population of the US is three hundred and thirty million, so there would be three point three million deaths
Hold on a minute. The models also predicted that not everyone would get COVID19. Full herd immunity would kick in once about eighty per cent of the population – or thereabouts – had been infected. This, by the way, was another thing that the experts just knew, right from the very beginning. [But what about mutations, and variants, and re-infections I hear you cry…. ‘Oh, do shut up’].
In effect, the COVID19 epidemic would come to an end when eighty per cent of people had become ill, maybe slightly less. Ergo, the overall death figure would be about five hundred thousand in the UK, and just over two million in the US. Or thereabouts.
This is a lot of deaths. Around the entire world pop: ~7.9 billion. We could see nearly seventy million deaths.
This one per cent figure, then, became the trigger for everything that followed. I think of it as the ‘justification’ figure. It was used to justify lockdowns, and everything else that went along with them. Here, after all, was a disease ten times as deadly as a bad influenza epidemic. Something must be done, or millions will die.
A further complication
Of course, things are rarely this simple. Even if the one per cent figure were true, it is essential to ask a follow up question. Who exactly is dying?
The average age of death caused by the Spanish flu was estimated at twenty-eight. Yes, twenty-eight.[2] The average age of death caused by COVID19 is around eighty-one – in the UK.[3]
I feel that the fact [and unusually this fact is almost certainly true] that COVID19 almost exclusively kills the elderly, and almost always the elderly who have many other comorbidities, had to be taken into consideration. But it wasn’t.
Instead of a disease that can wipe out young healthy people, aged twenty-eight, we had a disease on our hands that primarily kills those close to the end of their lives. Children and young adults, even middle-aged adults, even nearly old adults, have been almost remarkably unaffected by COVID19. This was known very early on.
What are the actual figures here? Turning attention specifically to the UK, we have had, at the time of writing, around one hundred and fifty thousand COVID19 deaths. Defined as… those deaths with COVID19 mentioned on the death certificate [whatever this actually means – another whole can of worms].
On the other hand, the number of people under the age of sixty, who have died from COVID19, with no other disease mentioned on the death certificate, is five hundred and forty-two. That was, by the 1st of February 2022.[4]
This is slightly under one per day during the epidemic. Or, to frame it another way, the risk of dying, for a healthy (or at least believed to be healthy – who knows for sure if they are or not) person under the age of sixty has been one in 79,131. [UK pop < 60 = 42,869,306].5
This risk, however, has been over very nearly two years. So, the yearly risk of death from COVID19 per years is 1:158,263. Or ~ 0.0075% … for this population. Just to give a comparator. The risk of dying from a road traffic accident in the UK, per year, is around eight times higher. [6]
1 in 20,000 per year vs 1 in 160,000
Thus, for more than two thirds of the population, the risk of dying from COVID19 has been 0.0075%. Instead of one per cent… it has been seven thousandths of one per cent.
Some people will say that this doesn’t matter. All deaths are of equal importance, we cannot discriminate on grounds of age, illness etc. In which case, taking the UK population as a whole, we have had 158,000 deaths with COVID19 mentioned on the death certificate. This represents a total risk of death of 1 in 424. Or ~ 0.25%.
Again, this has been over two years, so the total risk of death, per year [which is how risk is normally presented] has been 1 in 848, or ~ 0.125% per year. Which, as you may have noted, is around seven times less than one per cent.
Strangely, with COVID19, we have not stopped counting at the end of one year, and then started again. We have just kept on adding the figures year upon year – and will continue to do so? We have also continued to add in people who have been infected more than once…Double, treble, nay quadruple counting.
If we keep doing this, the IFR of COVID19 will eventually reach one. Not one per cent, but one. As in the entire population of the world will end up dying of COVID19. Although, at 0.125% per year this, as you may have worked out yourself, will take about seven hundred years. You may want to go and lie down and think about this.
Of course, the rough figures I have calculated above do not represent the Infection Fatality Rate. Instead, they represent the population fatality rate (PFR) i.e., forgetting about IFR and CFR, how many people, in total, have actually died. The population fatality has to be significantly lower than the infection fatality rate because not everyone has been infected… or have they?
The terrain is all?
We must now venture into yet another layer of complication. Yes, this onion has many layers. Most of which, you may be glad to know, I am not going to consider, or else this blog becomes a book. But the next layer is critical.
What does being ‘infected’ actually mean, and can we even know that it has happened?
At the risk of terrible oversimplification, historically there are two camps in the infectious disease world.
- Camp one: the microbe is all (The germ theory).
- Camp two: the terrain is all (The terrain theory).
Camp one believes that if you become exposed to an infectious agent you will inevitably become ‘infected’. You will then inevitably suffer (at least some) symptoms from the infection. You will then become unwell – maybe very unwell – and may even die. The germ theory. The severity of the disease is almost entirely dependent on the ‘viral load’ that you encounter.
Camp two states that the ‘terrain’ of the human body is far more important. We are surrounded by, and harbour microorganisms in our bodies. When exposed to pathogens ‘germs’ we become ill if our defences are weakened by deficiencies or toxicities. The germ itself is pretty much unimportant.
This is the ‘terrain’ theory. It means that many/most people, may not become ‘infected’ at all. Or that they may not even notice it – they simply shrug the infection off.
Historically, the two camps were led by Louis ‘the germ’ Pasteur and Claude ‘the terrain’ Bernard. It is said that, on his death bed Pasteur admitted. ‘Bernard was right, the pathogen is nothing, the terrain is everything.’
Well, yes and no. It is difficult to suffer any symptoms from a disease if you are never exposed to the germ. Which means that the pathogen clearly is something, not nothing. But … but we are making a greater mistake if we think that everyone is going to respond the same way to a germ. An assumption upon which our response has been predicated.
You may not think it, but the thinking behind all the actions taken is that COVID19 will inevitably ‘infect’ anyone who comes into contact with it. It will spread from person to person in a predicable manner, it will cause illness in everyone, and suchlike. In effect therefore, we have acted as if the terrain truly is nothing. Therefore, we must do everything possible to reduce contact, in order to reduce morbidity and mortality. In essence, the microbe is all.
The next assumption, following on from this, is that those who have not demonstrated any signs of symptoms have simply not been exposed to it, or not exposed to a sufficient ‘viral load’.
Personally, I find this impossible to believe. My daughter, a junior doctor who caught COVID19 working on a COVID ward in Wales, stayed at our house, suffered anosmia, and was diagnosed with COVID19 – with a PCR test, no less. No-one else got a snuffle.
At one point during the first couple of months of the epidemic, in May 2020 to be precise, I was standing next to two unmasked nurses in a small treatment room (we were not allowed to wear masks at this time) who were both coughing repeatedly in my face. Both were diagnosed with COVID19 the very next day and went off ill.
Every working day for six months, I went into nursing homes and an intermediate care centre. During which time, thirty-six patients died of – probably – COVID19. All of whom I saw and examined at least once. However, I did not become infected, and I never have. I also showed no antibodies – in a test in Autumn 2020.
If anyone tries to tell me that I was not exposed to the virus, or a sufficient viral load to cause infection, I can only laugh. I would reckon myself to be amongst an elite ‘most exposed to SARS-Cov2 virus in the world’ workforce. For at least two months I was working with no PPE – at all. Surrounding by staff and patients – many of whom died of COVID19 [no staff members, only patients].
If I was not infected, and officially I have not been, it raises the question. What, exactly, does infected mean? I speak as someone who also had to have seven Hepatitis B injections before I was able to raise a feeble, and pretty transient, antibody response. A friend and colleague had, if memory serves, over thirty Hep B vaccinations, and never raised a single antibody.
What does this, in turn, mean? That neither of us has any immunity to Hep B? That antibody tests are hopelessly flawed. That ‘immunity’ exists in ways that we have no idea how to measure – my current view.
Looking more specifically at COVID19, what happens if someone is found to be infected, as part of routine testing, yet has no symptoms, and produces no antibodies. Can you state that they were ‘infected’?
You may want to have a look at ‘The Flawed Science of Antibody Testing for SARS-CoV-2 Immunity.’[7]
It quotes this FDA statement:
‘…results from currently authorized SARS-CoV-2 antibody tests should not be used to evaluate a person’s level of immunity or protection from COVID-19 at any time, and especially after the person received a COVID-19 vaccination.’
So, antibody tests cannot tell us if someone has been infected, or effectively, vaccinated, nor if they are immune to SARS-Cov2. Just run that idea round your head for a while. Then see what answer pops out.
One small study further suggested that if you were diagnosed with [had a positive test for], COVID19, but suffered no symptoms, there was a 92% chance that you would show no measurable immune response post infection.[8]
These people, with a positive test, yet no symptoms, and no antibodies, were clearly ‘infected’ – they had a positive test after all [another can of worms]. However, these people must represent the most immune population of all. COVID19 hit them but was simply shrugged off. Leaving behind no sign that it was ever there.
Before I spin off down another hundred complications and side-issues – all of which are fascinating in themselves – I will attempt to highlight one immutable fact.
We have no idea how many people have been infected with SARS-Cov2, primarily because we have no idea how many people have been ‘infected’ yet demonstrate no sign of contact with the virus (unless they were coincidentally tested at the time). People such as, to pluck an example from the air … me.
It follows, therefore, that we cannot know what IFR rate might be. All we really have to go on (for all its further myriad flaws) is the Population Fatality Rate. Namely, how many people have actually died of COVID19.
In this end, this is the key figure. The one that counts [even if I have serious doubts about how this figure is created].
Thus far, across the world, over a period of very nearly two years, we have officially had five point seven million deaths from COVID19.
The total population of the world is seven point nine billion. Therefore:
- The total population fatality rate is 0.072%
- The total population fatality rate per year is 0.036%
This is a long, long way from the IFR of one per cent. Indeed, per year, it is around thirteen times less.
Is this because only one thirteenth of the world’s population have been infected? This is extraordinarily unlikely. The recent REACT study in the UK, found that 65% of those infected with the Omicron variant in January 2022 had previously been diagnosed with COVID-19.[9]
Seven per cent more had symptoms strongly suggestive of previous infection but had not had a confirmatory test at the time. Ergo, very nearly three quarters of those getting COVID19 in January 2022 had been infected before.
The authors are now attempting to backtrack from this finding. Why? Because, if it is correct that the vast majority of people infected represent re-infections, it means that the infection rate must be extremely high, much higher than anyone admits.
It also follows that exposure and transmission is extremely high. This, in turn, means that the IFR is significantly lower than anyone admits – or indeed can admit.
It is no surprise then to find that those running the REACT study are based in Imperial College London. Which is where all the original IFR estimates came from. The lair of Neil Ferguson et all. The originator of the ‘justification’ figure. Those who are now doing all they can to suggest that the number of people who have been infected with COVID19 remains low.
Even more telling, although this is less easy to confirm, we have cases of people with three, or even four, infections. How can anyone get infected four times, when people around them have not been infected once? Are they dancing naked around a flagpole, breathing in deeply from an inverted loudhailer in a COVID19 ward?
No, they are not. The explanation is that those getting re-infected are those who are unable to simply shrug of COVID19, for whatever reasons. Their terrain was different. Which means that they will likely keep on getting infected as new variants appear. Hopefully in milder and milder versions.
On the other hand, if we look at those individuals who show no evidence of infection – those who have never suffered symptoms and developed no antibodies – it is not that they have never been exposed, or ‘infected’. It is that they have more robust defences. As Claude Bernard argued, the most important thing here is not the germ, it is the terrain. It always was, and always will be.
As you may have gathered, I am convinced that we have all been exposed to and ‘infected’ with COVID19, probably all within the first year [even if I don’t know how you determine being infected]. Which means, in turn, that the PFR and the IFR – after two years of the virus spreading around – will have will be very much the same.
Can I prove this. No. If a large number of people develop no symptoms, and there is no test used that can accurately determine infection/exposure, I cannot possibly prove this. Equally, no-one can prove anything in the opposite direction.
A bit of a standout clue, however, is that three quarters of those found to be infected had been infected before. This could only have happened if people have been repeatedly exposed to a sufficient ‘viral load’ to get COVID19. And if they have, so has everyone else.
Of course, if we cannot accurately define what we mean by ‘infected’ no prediction can have been right. In turn, this means that we bet the house on an outcome measure so deeply flawed as to be virtually meaningless.
A strong clue that has been more widely recognised to be meaningless, is that it no longer exists. How so, I hear you cry? Well, it was decreed fairly early on that any positive COVID19 test represents a ‘case’ of COVID19. Something that kind of slipped through, without anyone noticing.
It was a worldwide thing, but the text below is taken from an NHS press briefing conference, using data from coronavirus.data.gov.uk. The bit in bold is most important.
Number of people who have had at least one positive COVID-19 test result, either lab-reported or lateral flow device (England only), by date reported – the date the case was first included in the published totals. COVID-19 cases are identified by taking specimens from people and testing them for the presence of the SARS-CoV-2 virus. If the test is positive, this is referred to as a case.[10]
Once this happened, any historical comparison of IFRs, or CFRs, became impossible. If everyone who is infected is also a ‘case’ then everyone is an I/C, (infected/case). There is no longer an IFR. Nor can there be a CFR. There is a combination I/CFR.
This, in turn, means that the IFR rate has been artificially boosted. Case fatality rates will always higher than IFRs [people who become very ill from a disease are always more likely to die from the disease, than those who suffer no symptoms].
Add them together and the IFR jumps up. Or at least it does if no-one notices that you are flipping between, and combining IFR and CFR, at speed, and continue talking about the IFR as if it remains the same thing. Oh, the tricks that are played to inflate the IFR and ‘prove’ that the experts were right all along.
Despite the fact that it is now devoid of any meaning, the one per cent IFR for COVID19 remains the most fiercely guarded figure of all. Dare to state the IFR is significantly lower than the one per cent ‘justification’ figure and the dreaded dementors, sorry fact-checkers, descend from on high.
They will attack you, your personal habits, your professionalism, your motivations, your clear ‘anti-vaxx’ stance, your lack of being an expert – and anything else they can think of to personally denigrate and humiliate.
They will countenance no arguments, no discussion. It will be determined that you are simply wrong, certainly stupid, and unable to understand The Science and probably in the pay of someone evil cabal, or other. It is somewhat irritating. I want to discuss science, if not ‘THE SCIENCE’. They want me crushed and silenced.
End thoughts
We understand far less about infections than we like to think. We are simply scratching at the surface at present. It is all extremely complex. If you are up for it, you may wish to read this paper. ‘Pathogenesis of COVID-19 described through the lens of an undersulfated and degraded epithelial and endothelial glycocalyx.’[11]
This paper represents a monstrously complex discussion of ‘the terrain.’ Namely, why do some people shrug off COVID19, whereas others may become so seriously ill that they may die?
According to this paper, it has nothing whatsoever to do with the things that we think of as part of the classic ‘immune response.’ T-cells, B-cells, cytokines, antibodies and suchlike. It is almost entirely to do with the ability of cells in our body to prevent viral entry.
Keeping things as simple as possible. If COVID19 (or other viruses) cannot get into an endothelial cell – or find it very difficult to do so – because the glycocalyx is healthy and robust, then SARS-Cov2 simply bounces off, and you will not become seriously ‘infected’. Yes, you will ‘shrug’ the virus off. It may enter your bloodstream, but that is about as far as it is going to get.
I mean, I have always been aware of the importance of cell entry in viral diseases. Both HIV and the Ebola virus enter a cell by hijacking a protein called CCR5 attached to cell membranes. There are a few people who have a thing called the CCR5delta32 mutation. If you have this mutation, it means that HIV and Ebola cannot attach themselves to the protein. Neither virus can then get into the cell and ‘infection’ cannot occur. The terrain is all.
Have any of those on SAGE, or Fauci, or Ferguson, or the CDC paid any heed to such things? I would be very surprised if any of them had even heard of the glycocalyx. A perfect example of the Dunning-Kruger* effect, I feel.
Yet, despite their stunning ignorance about such things, certain individuals and organisations grabbed the reins of influence in order to convince those in power that they had the answer.
The most important ‘answer’ being that COVID19 has an IFR of one per cent, which is at least ten times that of a serious influenza epidemic. Then, as the ‘germ’ is obviously everything, the only way to prevent hundreds of thousands, nay millions, of deaths was through lockdown, mask wearing, societal control, and suchlike.
We must stop spread, the ‘the germ is everything brigade’ cried. Although, with a 75% re-infection rate it is hard to argue that we have managed anything of the sort.
If this IFR figure was grossly inflated, which certainly seems to be the case, then all that we did was to create untold damage – for no good reason. I shall leave you with a post that I put up in a WhatsApp group recently. It followed a study from John Hopkins which estimated that COVID19 lockdowns only reduced deaths by 0.2%. [A study that will be attacked remorselessly, no doubt].[12]
‘Did lockdown work? No, the difference it made was marginal, at best. Were the models that we relied on accurate? No, they were bloody useless. Are the vaccines safe and effective? – Jury is out. Is there anything that was done justifiable by the evidence, in so much as it can be relied upon. I do not believe so. What we certainly did was to explode the economy, pile vast debt on UK Plc. create a massive backlog of work for the NHS. Fail to diagnose and treat hundreds of thousands of cases of cancer, and suchlike and create a tsunami of mental health problems. We also ran roughshod over incredibly important human rights, that have taken centuries to take hold and grow. In my opinion, almost everything that was done has caused more harm than good. What is the counter-argument? If we hadn’t done all these things, it would have been far worse. The evidence to support this position is sadly lacking.’
*Dunning-Kruger effect is, in psychology, a cognitive bias whereby people with limited knowledge or competence in a given intellectual or social domain greatly overestimate their own knowledge or competence in that domain relative to objective criteria or to the performance of their peers or of people in general.
References
1: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3809029/
2: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3734171/
5: https://www.statista.com/statistics/281208/population-of-the-england-by-age-group/
7: https://jamanetwork.com/journals/jama/fullarticle/2785530
The New Dark Age 