A frequent social media commentator, Steve Kirsch, drew my attention to recent published studies on COVID vaccines.
I have already written about one of them in which bizarre equations were used in odd ways to suggest that the authors conclusions were proven.
In this blog I am going to explain another paper which also uses odd equations in pointless ways to try to make their simplistic analysis seem more impressive.
These are examples of Hopeomathic argumentation. This word is an anagram & pun of homeopathic. It describes the use of spurious mathematical equations in the hope that the reader will skip over the hard part and just accept that something has been shown to be true.
Here is the second study that Steve Kirsch brought to my attention. It claims to be working out whether vaccines have saved lives or cost lives.
It has obscure equations showing a veneer of mathiness. It is an award winning example of hopeomathic argument.
On page three it starts to describe methods with two equations. These are detailed at the end of this page if you want to check them.
These equations, as far as I can tell, are arbitrarily setting deaths after vaccination as roughly equal to overall deaths in the population. They do this in a roundabout way which is hard to understand and is inaccurate.
The authors don’t explain what they are doing or why. They have written the equations as being equal without any explanation of why they would be equal. Perhaps they meant to present it as a null hypothesis, but they haven’t explained.
If that is what they meant they could have simply stated that if the proportion of vaccinated people who die is exactly the same as the overall death rate then the vaccine is neither saving nor costing lives.
Equation one suggests that the number who get vaccinated and die is the same as the total population. That being vaccinated neither reduces nor increases deaths.
Equation two says roughly the same thing.
Why would you want to do this?
How can that be set in an equation?
The first 2 equations are not explained I have looked at them for hours & have guessed that they are a null hypothesis.
I am guessing that the authors are merely saying that the null is when vaccines neither increase nor decrease deaths.
They could have just said that, but I think the equations are there to make it look clever.
That’s hopeomathic !
Next they use the term “proportional number” which they define as vax’d times dead divided by alive.
What is that? Why would you multiply the number vaccinated by the total dead/alive?
It is close to being a statement that the number of vax’ed who die is about the same as the overall death rate, but a bit off.
Dead/alive is almost the proportion of the total population that has died. Or do they mean something else?
vaccinated/alive = vaccinated dead / total dead
Really? Why?
Skipping to my favourite page:
It is impressive how complicated they have made their text & equations. I am especially impressed by page 6 where they start with a % of population, then multiply it by the population to get a number and then divide that big number by the population to get back to the same percentage that they started with.
Doing that with big numbers adds a good dollop of mathiness.
That’s hopeomathic !
Conclusion
I have tried to find some hidden sense in the equations in this paper. It appears to contain arbitrary equations which make little sense and are pointless.
I have to ask whether someone published this as a joke to see who falls for it.
Can anyone explain them? Perhaps this is normal math in the world of the homeopathic?
That’s hopeomathic !
This paper goes on to take superficial public data & draw a conclusion The authors know that the conclusion isn't sound because they specify the problem of mixing disparate groups together, but they publish it anyway They even write pages of pointless equations to make it look better.
Deeply hopeomathic !
Having waded through their overly complex formulae we get to the content which I summarise:
2021 65% of population were vaccinated But 77% of COVID deaths were vaccinated
I haven't finished reading, but I assume that's the key claim I am impressed by the circumlocution.
As far as I can tell, after wading through pointless equations is that they could have fitted the whole thing into a tweet:
"When half the country was vax'd more than half the COVID dead were vax'd"
Now why would that have been?
This fact was known in 2021. Most of the people who die from covid are old. Most of the old were vaccinated those weeks in 2021. Few of the young were vaccinated at that time and very few of them died of COVID.
The authors stated that the analysis needs to be done by age, but they ignored that and made their claim of Vax death anyway.
I award them a prize for turning a trivial known fact into a science paper that Steve Kirsch regarded worth publishing.
Source: https://www.preprints.org/manuscript/202301.0204/v3
Hours of messing with the equations, trying to make sense of them are written below
I wasted hours trying to find something worthwhile in this paper. The following is part of the pain. You don’t need to read it.
Here’s what they say they are trying to do, and which breaks my brain
2. Analysis and Results
For the purposes of the analysis, people who are living in a given region (county,
state or country) may be designated as “alive”. In this group, we can further delineate “vaccinated” from “unvaccinated” persons.
As people are dying within a given timeframe, we can then also estimate total deaths and include subsets based on vaccination status.
Therefore, to assess the impact of vaccination on the mortality rate, we employ the
following equation:
vaccinated(A)/total alive(B) = dead vaccinated(𝐴∗)/total deaths (𝐵∗) (1).
We calculate the proportional number of dead vaccinated as follows:
dead vaccinated proportional number
𝐴∗ = vaccinated (A)∗total deaths(𝐵∗)/total alive(B) (2).
The proportional number for a given time period should always be larger than the statistical estimate of dead vaccinated, the product obtained from the calculation. In this case, vaccines are saving lives. If the proportional number is smaller than the statistical number, however, vaccines are increasing the death rate.
What is equation 2 doing?
They seem to be defining the number of vaccinated persons who died by just assuming that it is proportional to deaths/alive. Why? That makes no sense. Why would the number of persons who were vaccinated and died be equal to some other known statistics? The thing the authors are defining it to be equal to is total dead divided by remaining alive. Why that? Why would it have any fixed relationship with that? And why
dead/alive
rather than
dead/population?
(dead / alive
is always equal to
dead / (population - dead)
This is algebra and load of us hate it & don’t understand it, so most readers will skip over it assuming that the authors know what they are doing.
Warning! I am going to do some algebra:
vaccinated(A)/total alive(B) = dead vaccinated(𝐴∗)/total deaths (𝐵∗)
A/B = A*/B* which can be shifted around to give A*/A = B*/B which in words is the claim that the proportion of vaccinated who die is exactly the same as the proportion of the entire population who died.
Which is daft, but if true then the whole paper is pointless as the authors have just destroyed their conclusion.
I need to point out that this equation is almost certain to NOT be equal. The authors don’t explain why they think it is, and don’t explain that their conclusion contradicts it.
But they then go on to predefine A* (the number of vaccinated who died). Note they are defining how many vaccinated persons have died. Not measuring this, but defining it, even though the whole point of the paper is to show how many are dying. So they are defining as an assumption the conclusion, and it contradicts their conclusion.
𝐴∗ = vaccinated (A)∗total deaths(𝐵∗)/total alive(B)
This is defining the number who died after being vaccinated as being equal to the number vaccinated multiplied by the ratio of deaths compared to living.
How does that make any sense? It is almost, but not quite, defining the number who died from vaccine as being in proportion to the total death rate. Total deaths divided by total alive is roughly the overall death rate. So again they are defining away the conclusion of the paper.
Let’s put some hypothetical numbers and see if equation 1 is equal. If the equation is universal it should work with any internally consistent collection of population, Vax rate and death rate as long as the numbers add up correctly.
Here’s an example:
population of 100,000
50,000 vaccinated
6,000 dead vaccinated
4,000 dead unvaccinated
𝐴∗ = (vaccinated (A)∗total deaths(𝐵∗))/total alive(B)
A* = (50,000 x 10,000) / 90,000 = 5,556
Putting that into equation 1
vaccinated(A)/total alive(B) = dead vaccinated(𝐴∗)/total deaths (𝐵∗)
50,000/90,000 = 6,000/10,000
0.5556 = 0.6
Something wrong there. The authors say the result has to be equal.
𝐴∗ = (vaccinated (A)∗total deaths(𝐵∗))/total alive(B) (2)
vaccinated(A)/total alive(B) = dead vaccinated(𝐴∗)/total deaths (𝐵∗) (1)
This is weird, in equation 1 A* is “dead vaccinated,” but in equation 2 it is set as being equal to vaccinated multiplied by dead divided by alive. What the hell is that? Total Deaths divided by Total alive is same as deaths divided by (population - deaths).
Total deaths is measured. Total alive is just the original population less the measured deaths.
Why would deaths in the vaccinated group be equal to this?
Let’s take some plausible examples and see what it produces:
population 1,000
total deaths 10
Number vax’d 230
Est of number of vax’d who died
230 * 10 / 990 = 2.323 (which is almost proportional)
population 850
deaths 36
number vaxxed 700
Estimate of number vaxxed who died
700 * 36 / 814 = 31 (which is a bit less proportional)
Try some numbers
Pop 10,000
Vax’d 1,000
vax’d dead 500
total dead 700
1,000 / 8,800 = 500/1,200
0.1136 = 4.1667
don’t think so
Maybe they have not explained their method clearly, perhaps they mean something else.
Again it just seems to be a very roundabout way to say they are assuming that the vaccinated die at roughly the same rate as the unvaccinated. (Why?)
This is nonsense