4th September 2020
COVID – why terminology really, really matters
[And the consequences of getting it horribly wrong]
When is a case not a case?
Since the start of the COVID pandemic I have watched almost everyone get mission critical things wrong. In some ways this is not surprising. Medical terminology is horribly imprecise, and often poorly understood. In calmer times such things are only of interest to research geeks like me. Were they talking about CVD, or CHD?
However, right now, it really, really, matters. Specifically, with regards to the term COVID ‘cases.’
Every day we are informed of a worrying rise in COVID cases in country after country, region after region, city after city. Portugal, France, Leicester, Bolton. Panic, lockdown, quarantine. In France the number of reported cases is now as high as it was at the peak of the epidemic. Over 5,000, on the first of September.
But what does this actually mean? Just to keep the focus on France for a moment. On March 26th, just before their deaths peaked, there were 3,900 ‘cases’. Fourteen days later, there were 1,400 deaths. So, using a widely accepted figure, which is a delay of around two weeks between diagnoses and death, 36% of cases died.
In stark contrast, on August 16th, there were 3,000 cases. Fourteen days later there were 26 deaths. Which means that, in March, 36% of ‘cases’ died. In August 0.8% of ‘cases’ died. This, in turn, means that COVID was 45 times as deadly in March, as it was in August?
This seems extremely unlikely. In fact, it is so unlikely that it is, in fact, complete rubbish. What we have is a combination of nonsense figures which, added together, create nonsense squared. Or nonsense to the power ten.
To start with, we have the mangling of the concept of a ‘case’.
Previously, in the world of infectious diseases, it has been accepted that a ‘case’ represents someone with symptoms, usually severe symptoms, usually severe enough to be admitted to hospital. Here, from Wikipedia…. yes, I know, but on this sort of stuff they are a good resource.
‘In epidemiology, a case fatality rate (CFR) — sometimes called case fatality risk or disease lethality — is the proportion of deaths from a certain disease compared to the total number of symptomatic people diagnosed with the disease.’ 1
Note the word symptomatic i.e. someone with symptoms.
However, now we stick a swab up someone’s nose, who feels completely well, or very mildly ill. We find that they have some COVID particles lodged up there, and we call them a case of COVID. Sigh, thud!
A symptomless, or even mildly symptomatic positive swab is not a case. Never, in recorded history, has this been true. However, now we have an almost unquestioned acceptance that a positive swab represents a case of COVID. This is then parroted on all the news channels as if it were gospel.
I note that, at last, some people are beginning to question how it can be that, whilst cases are going up and up, deaths are going down, and down.
This is even the case in Sweden, which seems to be the final bastion of people with functioning brains. However, even they seem surprised by this dichotomy. In the first two weeks of August they had 4,152 positive swabs. Yet, in the last two weeks of August, they had a mere 14 deaths (one a day, on average).
That represents 1 death for every 300 positive swabs or, as the mainstream media insists on calling them, positive ‘cases’. Which, currently, represent a case fatality rate of 0.33%. Just to compare that with something similar, the case fatality rate of swine flu (HIN1), was 0.5%. 2
Thus, lo and behold, COVID is a less severe infection than swine flu – the pandemic that never was. That’s what these figures appear to tell us. They tell us almost exactly the same in France where they ‘appear’ to have a current case fatality rate of 0.4%.
On the other hand, if you look at the figures from around the world, they are very different. As I write this there have been, according to the WHO, 25 million cases and 850,000 deaths. That is a case fatality rate of more than 3%. Ten times as high.
Why are these figures so all over the place? It is because we are using horribly inaccurate terminology. We are comparing apples with pomegranates to tell us how many bananas we have. Our experts are, essentially, talking gibberish, and the mainstream media is lapping it up. They are defining asymptomatic swabs as cases, and no-one is calling them out on it. Why?
Because… because they are frightened of looking stupid? Primarily, I believe, because they also have no idea what a case might actually be So, it all sounds quite reasonable to them.
The good news
However, moving on from that nonsense, there is some extremely good news buried in here. Which I am going to try and explain. It goes as follows.
At the start of the epidemic, the only people being tested were those who were being admitted to hospital, who were seriously ill. Many of them died. Which is why, in France, there was this very sharp, initial case fatality rate of 35%. In the UK the initial case fatality rate was I think 14%. Last time I looked at the UK figures, the case fatality was 5%, and falling fast.
This fall has occurred, and will occur everywhere in the World, because as you increase your testing, you pick up more and more people with less severe symptoms. People who are far less likely to die. The more you test, the more the case fatality rate falls.
It falls even more dramatically when you start to test people who have no symptoms at all. In fact, as you broaden your testing net, something else very important happens. You gradually move from looking at the case fatality rate to the infection fatality rate.
The infection fatality rate is the measure of how many people who are infected [even those without symptoms, or very mild symptoms] who then die. This is the critical figure to know because it gives you an accurate assessment of the total number of deaths you are likely to see.
IFR x population of a country x % of population infected = total number of deaths (total mortality)
So, where have we got to. Well, although the case fatality rate in the UK still currently stands at 5%, because it is dragged up by the 14% rate we had at the start. If we look at the more recent figures things have changed very dramatically.
In the first two weeks of August there were 13,996 positive swabs in the UK. In the second two weeks of August there were 129 deaths. If you consider every positive swab to be a case, this represents a case fatality rate of 0.9%. Around one fifteenth of that seen at the start.
I think you can clearly see a direction of travel here.
- At the start on the pandemic we had a, brief, 35% fatality rate in France
- It was 14% in the UK at the start
- It now sits at 5% in the UK – over the whole pandemic
- In August, in the UK, it was down to 0.9%
- It is currently 0.47% in Germany
- It is currently 0.4% in France
- It is currently 0.33% in Sweden
It is falling, falling, everywhere. Where does it end up, this hybrid case/infection fatality rate? Remember, we are still only testing a fraction of the population, so we are missing the majority of people who have been infected, mainly those who do not have symptoms. Which means that these rates must fall further, as they always do in any pandemic.
To quote the Centre for Evidence Base Medicine on the matter:
‘In Swine flu, the IFR (infection fatality rate) ended up as 0.02%, fivefold less than the lowest estimate during the outbreak (the lowest estimate was 0.1% in the 1st ten weeks of the outbreak).’ 3
The best place to estimate where we may finally end up with COVID, is with the country that has tested the most people, per head of population. This is Iceland. To quote the Centre for Evidence Based Medicine once more:
‘In Iceland, where the most testing per capita has occurred, the IFR lies somewhere between 0.03% and 0.28%.’ 3
Sitting in the middle of 0.03% and 0.28% is 0.16%. As you can see, Iceland, having tested more people than anywhere else, has the lowest IFR of all. This is not a coincidence. This is an inevitable result of testing more people.
I am going to make a prediction that, in the end, we will end up with an IFR of somewhere around 0.1%. Which is about the same as severe flu pandemics we have had in the past. Remember that figure. It is one in a thousand.
It may surprise you to know that I am not the only person to have made this exact same prediction. On the 28th February, yes that far back, the New England Journal of Medicine published a report by the National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD (A.S.F., H.C.L.); and the Centers for Disease Control and Prevention, Atlanta. 4
In this paper ‘Covid-19 — Navigating the Uncharted’ they stated the following:
‘On the basis of a case definition requiring a diagnosis of pneumonia, the currently reported case fatality rate is approximately 2%. In another article in the Journal, Guan et al. report mortality of 1.4% among 1099 patients with laboratory-confirmed Covid-19; these patients had a wide spectrum of disease severity. If one assumes that the number of asymptomatic or minimally symptomatic cases is several times as high as the number of reported cases, the case fatality rate (my underline) may be considerably less than 1%. This suggests that the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza.’
A case fatality rate considerably less than 1%. Their words, not mine. As they also added, ‘the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza.’
At this point, you may well be asking. Why the hell did we lockdown if COVID was believed to be no more serious than influenza? Right from the start by the most influential infectious disease organisations in the World.
It is because of the mad mathematical modellers. The academic epidemiologists. Neil Ferguson, and others of his ilk. When they were guessing (sorry estimating, sorry modelling) the impact of COVID they used a figure of approximately one per cent as the infection fatality rate. Not the case fatality rate. In so doing, they overestimated the likely impact of COVID by, at the very least, ten-fold.
How could this possibly have happened?
When they put their carefully constructed model together on the 16th of March, if they had been reading the research, they must have been aware that they were looking at a maximum case fatality rate of just over 1% in China, right at the start, where the figures are always at their highest.
Which means that, unless COVID was going to turn out nearly 100% fatal, we could never get anywhere near 1%, for the infection fatality rate. Even Ebola only kills 50%.
But they went with it, they went with 1%. Actually, Imperial College reduced it slightly to 0.9%, for reasons that are opaque.
From this, all else flowed.
If the INFECTION fatality rate truly were 0.9%, and 80% of the population of the UK became infected, there would have been/could have been, around 500,000 deaths.
0.9% x 80% x 67million = 482,000
However, if the case fatality rate is around 1%, then the infection fatality rate will be about one tenth of this, maybe less. So, we would see around 50,000 deaths, about the same as was seen in previous bad flu pandemics.
DO NOT LOCKDOWN
What Imperial College London did was to use a model that overestimated the infection fatality rate by a factor of ten.
We now know, as the IFR rates of various countries falls and falls, that the Imperial College estimated IFR was completely wrong. The UK, for example, has seen 42,000 deaths so far, which is 0.074% of population. The US has seen about 200,000 deaths 0.053%. Sweden, which did not lockdown down, has seen about 6,000 deaths, which is an infection fatality rate of 0.06%. All three countries are opening up and opening up. Whilst the ‘cases’ are rising and rising, the deaths continue to fall. They are, to all intents and purposes, flatlining.
In Iceland it is around 0.16% and falling. In other words…
Stop panicking – it’s over
Whilst everyone is panicking about the ever-increasing number of cases, we should be celebrating them. They are demonstrating, very clearly, that COVID is far, far, less deadly then was feared. The Infection Fatality Rate is most likely going to end up around 0.1%, not 1%.
So yes, it does seem that ‘the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza.’
Wise words, wise words indeed. Words that were written by one Anthony S Fauci on the 28th of February 2020. If you haven’t heard of him, look him up.
Critically though, eleven days after this, he rather blotted his copybook, because he went on to say this “The flu has a mortality rate of 0.1 percent. This (COVID) has a mortality rate of 10 times that. That’s the reason I want to emphasize we have to stay ahead of the game in preventing this.” 5
The mortality rate Dr Fauci? Could it possibly be that he failed to understand that there is no such thing as a mortality rate? Did he mean the case fatality rate, or the infection fatality rate? If he meant the Infection mortality rate of influenza, he was pretty much bang on. If he meant the case fatality rate, he was wrong by a factor of ten.
The reality is that, no matter what Fauci went on to say, severe influenza has a case fatality rate of 1%, and so does COVID. They also have approximately the same infection fatality fate of 0.1%.
It seems that Dr Fauci just got mixed up with the terminology. Because in his Journal article eleven days earlier, he did state… ‘This suggests that the overall clinical consequences of Covid-19 may ultimately be more akin to those of a severe seasonal influenza… [and here is the kicker at the end] (which has a case fatality rate of approximately 0.1%).’
You see, he did say the case fatality rate of influenza was approximately 0.1%. Wrong, wrong, wrong, wrong… wrong.
Oh dear, oh dear, oh dear. With influenza, Dr Fauci, the CDC, his co-authors, the National Institute of Allergy and Infectious Diseases and the National Institutes of Health and the New England Journal of Medicine got case fatality rate and infection fatality rate mixed up with influenza. Easy mistake to make. Could have done it myself. But didn’t.
You want to know where Imperial College London really got their 1% infection fatality rate figure from? It seems clear that they got it from Anthony S Fauci and the New England Journal of Medicine. The highest impact journal in the world – which should have the highest impact proof-readers in the world. But clearly does not.
Imperial College then used this wrong NEJM influenza case fatality rate 0.1%. It seems that they then compared this 0.1% figure to the reported COVID case fatality rate, estimated to be 1% and multiplied the impact of COVID by ten – as you would. As you probably should.
So, we got Lockdown. The US used the Fauci figure and got locked down. The world used that figure and got locked down.
That figure just happens to be ten times too high.
I know it is going to be virtually impossible to walk the world back from having made such a ridiculous, stupid, mistake. There are so many reputations at stake. The entire egg production of the world will be required to supply enough yolk to cover appropriate faces.
Of course, it will be denied, absolutely, vehemently, angrily, that anyone got anything wrong. It will be denied that a simple error, a mix up between case fatality and infection fatality led to this. It will even more forcefully stated that COVID remains a deadly killer disease and that all Governments around the world have done exactly the right thing. The actions were right, the models were correct. We all did the RIGHT thing. Only those who are stupid, or incompetent cannot see it.
When wrong, shout louder, get angry, double-down, attack your critics in any way possible. Accuse them of being anti-vaxx, or something of the sort. Dig for the dirt. ‘How to succeed in politics 101, page one, paragraph one.’
However, just have a look, at the figures. Tell me where they are wrong – if you can. The truth is that this particular Emperor has no clothes on and is, currently, standing bollock naked, right in front of you. Hard to believe, but true.
I would like to thank Ronald B Brown for pointing out this catastrophic error, in his article ‘Public health lessons learned from biases in coronavirus mortality overestimation.’ 6
I had not spotted it. He did. All credit is his. I am simply drawing your attention to what has simply been – probably the biggest single mistake that has ever been made in the history of the world.