Dashboards reflecting reality or manufacturing it? Acting in ‘journalist mode’ Martin recently wrote an article for the Conservative Woman online magazine, summarising an original article, The Dashboard that Ruled the World, written by Thomas Verduyn, about the John Hopkins University (JHU)
>"Even ‘averaging’ the results of many models looks fruitless given the wide uncertainties in the inherent inaccuracy of outputs produced by each."
It’s never a good idea to average the results of models unless you can be confident regarding the representativeness of the sample. If I may quote Prof. Professor Eric Winsberg of the University of South Florida (a philosopher who specialises in the treatment of uncertainty in mathematical models):
“Ensemble methods assume that, in some relevant respect, the set of available models represent something like a sample of independent draws from the space of possible model structures. This is surely the greatest problem with ensemble statistical methods. The average and standard deviation of a set of trials is only meaningful if those trials represent a random sample of independent draws from the relevant space—in this case the space of possible model structures. Many commentators have noted that this assumption is not met by the set of climate models on the market…Perhaps we are meant to assume, instead, that the existing models are randomly distributed around the ideal model, in some kind of normal distribution, on analogy to measurement theory. But modeling isn’t measurement, and so there is very little reason to think this assumption holds.”
He was talking about climate model ensembles but the problem is no less relevant with epidemiological models.
The problem with models is that when they prove inaccurate, the believers in "the model" usually don't want to admit that the model is worthless, and instead keep adding tweaks that never predict reality. In a former employment, I was once tasked with deriving such things as activation energies to fit Arrhenius equations to measured aging data so as to predict future aging of a specific parameter. When a single Arrhenius equation did not match the data, the model believers said that there must be more than one reaction occurring, so add another Arrhenius equation to the model. When that didn't match, they added a third. The model never matched the actual experimental data, but I was told we had to use it because that is the theory and the customer expected us to use that theory to predict future performance. I laughed to myself and gave them this huge complicated model that "best" fit the data to their theory. In reality, it could have been much more accurate and simple. The actual experimental data was that the output parameter being modelled when plotted versus time was simply a straight line! A simple linear straight line fit the data perfectly! But no one wanted to use a simple straight line as a model. That didn't match their theory.
What about this statement in an article* about AstraZeneca withdrawing its COVID-19 vaccine?
QUOTE
In a statement the company said: “We are incredibly proud of the role Vaxzevria played in ending the global pandemic. According to independent estimates, over 6.5 million lives were saved in the first year of use alone and over three billion doses were supplied globally.
END OF QUOTE
How do they work out “over 6.5 million lives were saved in the first year of use alone”?
Excellent article. Logically presented and well-written.
Now the pushback, questions, and requests. :)
1. The New York data - The theory that models were used to guide the reporting of COVID deaths but were never reflections of what was occurring on the ground is one I've proffered numerous times as one possible explanation for the obviously-manipulated all-cause death curve. For that reason, more than anyone, I would love to be able to embrace the method Thomas Verduyn applied to NYC DOH and the JHU dashboard that led him to claim the results as proof the deaths weren't happening in real time and a model was used.
However, any analysis of the New York data must be mindful of what was being reported by officials at time -- not because the officials were telling the truth, but because it sheds light on the manipulation AND sets parameters for what constitutes proof.
My request is NOT that you adjudicate the conversation between he & myself, but that you offer your own “take” on the implications of what was reported at the time so that I can understand how his method - and your implicit endorsement thereof - is "almost irrefutable proof that the probable death numbers were artificially generated on a computer."
If it is such proof, I am prepared to accept it humbly include it in a letter I’m in the process of writing to relevant elected and appointed officials.
Much more importantly, I believe the adding of “the probables” is representative of something that happened on a much larger scale in locations worldwide, if only to a lesser degree. (NYC is the exception that proves the rules.)
2. FYI, in at least two press conferences in April 2020, Governor Andrew Cuomo said that the COVID death numbers being reported (up until a certain point) were hospital deaths only. I believe this is critical to understanding what may have been done in numerous places worldwide.
3. You cited this link to Dr. Levitt’s DP work. https://heatherrenkel.github.io The name on the site is Heather Renkel. Who is Heather Renkel, i.e., what is her relationship to Dr. Levitt? (grad student?) Is this analysis published elsewhere?
4. Can you provide a direct link to the “Winter Scenarios” graph? I can't find it (which may be user error).
5. As Prof Neil is aware, I agree with your conclusion that “epidemiological models of respiratory pathogens produce hopelessly exaggerated estimates based on assumptions about parameters that are either unknowable or are so difficult to measure that their credibility is highly suspect.” While not the purpose of your article, can at least one of you (the authors) comment on the implications of this conclusion on all-cause death curves for 2020 and beyond? Do you agree with the assertion/assumption of Denis Rancourt & colleagues are necessarily valid & reliable?
Proof is perhaps too strong a word. Strong suspicions? What strength of support do you think his arguments merit? Credible/plausible/possible/.....?
I agree there remain a lot of gaps and questions as you would expect in an ongoing detective story. It's not a closed book by a long chalk. I keenly await to here new views and discoveries.
Winter scenarios....let me try and dig but I'm about to jump on a plane so might forget.
Rancourts work (and that of others) on excess deaths....I've scanned it but haven't had the time to do a deep dive review so can't really comment. I've not posted on it here or anywhere else. Whether I ever find time to do so I'm not too sure to be honest. There are only so many hours in the day.
I'm also happy to hear skepticism about official mortality data. After all if other data is manipulated why not that? Without citizen led inspection of death certificates and double checking with families there will be some room for doubt - how much doubt is warranted I'm not too sure. I know neither Norman nor Scott has looked at it.
Levitt website. I'm assuming it is an honest representation of his work. Who Renkel is I don't know and have not dug deeper. Do you have reason to believe it is not what it purports to be?
The official story is that JHU was following/trying to follow NYC, not vice versa. How then is the method Verduyn used near-proof of a model being used?
I'm asking because I'm not clear on what you believe the implications are of what you're showing about dashboards—either for COVID or all-cause death curves. Were dashboards too zealous and deaths force-fitted to the models? What is the relationship between the dashboard COVID curves and the official/final COVID and all-cause daily death curves in respective jurisdictions?
Two things stick out - 4/3 proportion and smoothness of the curve and it being too close a match to the abstract/perfect theoretical representation.
I don't know the mechanisms. You call fraud on NYC without knowing the mechanism. Yes?
Absolute knowledge isnt a prerequisite here.
I've acknowledged that mortality statistics could be manipulated. I might acknowledge financial fraud in the interbank trading market is a real possibility based on observed market moves but I dont think it would be beholden on me to say how trades and prices were being exactly manipulated.
Not sure why I'm being bracketed with Rancourt.
Can you accept I've no fully formed view of some issues and dont claim anything close to omniscience?
Re: "4/3 proportion and smoothness of the curve and it being too close a match to the abstract/perfect theoretical representation" - I don't disagree with that and have made similar observations about the all-cause curve, especially the rise. The cardiac arrest curve looks engineered to me as well. Indeed, the whole event does.
But - unless I am mistaken - Verduyn's JHU dashboard article used NYC as illustrative of JHU reporting numbers were using models. What I am trying to show you is that NYC isn't a good example of that because ("officially") it was JHU catching up with NYC, not vice versa.
Plus, the 2020 vital statistics report from NYC DOHMH provides a explanation that could further be used to excuse the fact that JHU had a hard time "retro-fitting" the probables into the NYC DOHMH COVID death curve.
I wasn't bracketing you w/Rancourt. I realize I inadvertently left out words in my initial reply. My question should have been, "Do you agree with the assertion/assumption of Denis Rancourt & colleagues that all-cause death numbers are necessarily valid & reliable?"
My intent was to cite Rancourt as an example of someone who (like you) doesn't believe there was a pandemic and doesn't accept the WHO's baloney about a new cause of death, yet holds the view that all-cause death data is legitimate because it isn't susceptible to manipulation.
I believe there is ACM fraud, not just COVID death fraud. The nonsense with the dashboards absolutely helped convince the world that "something wicked this way comes" to a city near you but it was not/is not clear in the present article that and your co-authors hold that view. Thank you for clarifying that you agree with me, i.e., that ACM fraud is possible, not impossible. :)
Math is not my subject but graphs are visual enough to be helpful. What's been most striking is we were told this deadly, novel virus spread so fast it covered the globe in a few months. But the biggest spike in cases followed "safe & effective" countermeasures.
My sympathies lie with folks whose eyes glaze over when numbers become the focus but between your NYC mass casualty chart and the World Data chart at top soaring in 2022 even the dimmest bulbs should be able to look at the big picture & see the spectacular lies.
Big time kudos and thanks for tackling the data translating to impactful images for all! <3
Be wary, as 'OurWorldInData' is not reliable (see: https://thedailybeagle.substack.com/p/fudgegate-cdc-caught-fudging-vaccine). Their charts are ultimately a front-end facade making use of *heavily* cherry picked datasets (often with repeat singular source citations) and it is strongly recommended to pull the data the charts refer to rather than using the facade presented data.
In the above CDC case, 'OurWorldInData' essentially obfuscated the CDC's fudging of mortality statistics where they used erroneous population numbers with floating points, which in Jikkyleaks' view was evidence of them using fabricated computer modelling rather than accurate representative datasets.
The other source to avoid quoting is Statistica. It acts in a similar capacity to OurWorldInData, but it is arguably worse - the sources are often paywalled, which means the veracity of the claims cannot be verified publicly.
Without knowing what OWID was actually quoting as a source for the 'confirmed cases', the comparison is almost meaningless. That said, I agree with your theory JHU were using a computer model because the numbers echo similarly to Ferguson's bunk figures.
I'm currently listening to A plague upon our House by Scott Atlas (free on Audible for another week or so, but worth buying too). It is astonishing, the way he describes how key figures in the White House Corona taskforce relied on/misinterpreted data from sites like OurWorldInData. He is particularly critical of Birx's interpretation of statistics and the way this influenced national policy (and by extension global government policy responses).
'Governments did not have an open discussion, including economists, biologists and epidemiologists, to hear different voices. In Britain, it was the voice of one person – Neil Ferguson – who has a history of coming up with projections that are a bit odd. The government did not convene a meeting with people who have different ideas, different projections, to discuss his projection. If it had done that, it could have seen where the fundamental flaw was in the so-called models used by Neil Ferguson. His paper was published eventually, in medRxiv. The assumption was that one per cent of all people who became infected would die. There is no justification anywhere for that.
Let us say the epidemic runs with a basic reproduction rate of around two. Eventually 80 per cent of the population will be immune, because they have been infected at some point in time. Eighty per cent of the British population would be something like 50million. One per cent of them dying is 500,000. That is where Ferguson’s number came from...
...And it is going on and on and on, just because governments are afraid of admitting an error. They are trying to find excuses. They say they have to do things slowly, and that they have ‘avoided 500,000 deaths’ in the UK. But that was an absurd number that had no justification. The person presenting it pretended it was based on a model. It was not a model. It was the number of one per cent of all people infected dying. And nobody was questioning it. And that is the basic problem. '
Computer models, in any science (astronomy, geology, epidemiology or whatever) are nothing more than guesswork based on stacked (i.e. compounded) assumptions. They are invariably wrong, to a lesser or greater extent (FAR greater in the case of the 'covid' fraud). Garbage in, garbage out.
Manufacturing fear. Rulers have always done this to maintain power. Now undemocratic trans-national organizations do it via their agents that used to be our elected governments and previously honest academic institutions now corrupted by money.
After decades of reacting to corporate statistical modelling, what has become glaringly obvious it that the data adjustment criteria if always, only and ever informed by motivated reasoning seeking a predetermined end point or outcome.
That predetermined outcome is usually nothing more that implementing marginal and direct expense reduction. From the perspective of the corporate entity, if the reduction target is below a Pareto neutral threshold, implementing cost transfers will do quite nicely.
The statistical legerdemain involved with the Covid prognostications fit that pattern, and they fit it like a glove. We've even seen the cost transfer corollary added into the mix. Rhetorical legerdemain is the boon companion of cost transfer. Reference the "with Covid or from Covid" cause of death reporting distortions for verifiable support of the assertion.
I’m still having trouble understanding what is ‘Covid’?
Has this been confirmed as a unique disease, verifiable by a test?
Also, please consider this statement, by ‘leading infectious diseases physician’ Peter Collignon, re the AstraZeneca vaccine and risk of blood clots, who says “…in my view…the fear and the publicity about the adverse effect caused more deaths than the actual vaccine did. If you were an 80-year-old and got Covid, you had a 1 in 10 chance of dying. Yet, if you had this vaccine, you had a 1 in 100,000 chance of dying from the effect of a complication. Yet, there were a lot of people, because we had zero Covid at the time, who said, ‘oh no, I’ll wait because the Pfizer vaccine’s better’.””*
Pick that statement apart…is it not gobbledygook? What’s the evidence for any of it?
Really…do any of these ‘experts’ have a clue what they’re talking about?
*Paywalled article: AstraZeneca Covid vaccine withdrawn over side effects but ‘fear more deadly, The Australian, 9 May 2024.
My hypothesis regarding case numbers: those early models were not that far off in terms of final incidence levels claimed, only the assumed mechanism is bogus. At the turn of 2023/2024, I compared different methods for estimating case numbers for Germany:
If it is possible that official figures (obtained by testing) became meaningless at the beginning of 2023, it is equally possible that official figures were already meaningless in 2020/2021. Unfortunately, back then neither cohort testing nor random testing were performed. I wrote about this on March 18, 2020 (https://dochdoch.substack.com/p/was-kommt-vor-dem-weltuntergang), and then three years of frustration followed...
...and didn't Feynman also say something to the effect that no matter how fancy your models are, if the don't reflect reality, they're wrong.
>"Even ‘averaging’ the results of many models looks fruitless given the wide uncertainties in the inherent inaccuracy of outputs produced by each."
It’s never a good idea to average the results of models unless you can be confident regarding the representativeness of the sample. If I may quote Prof. Professor Eric Winsberg of the University of South Florida (a philosopher who specialises in the treatment of uncertainty in mathematical models):
“Ensemble methods assume that, in some relevant respect, the set of available models represent something like a sample of independent draws from the space of possible model structures. This is surely the greatest problem with ensemble statistical methods. The average and standard deviation of a set of trials is only meaningful if those trials represent a random sample of independent draws from the relevant space—in this case the space of possible model structures. Many commentators have noted that this assumption is not met by the set of climate models on the market…Perhaps we are meant to assume, instead, that the existing models are randomly distributed around the ideal model, in some kind of normal distribution, on analogy to measurement theory. But modeling isn’t measurement, and so there is very little reason to think this assumption holds.”
He was talking about climate model ensembles but the problem is no less relevant with epidemiological models.
Please excuse the typo and strike out the 'Prof.'
The problem with models is that when they prove inaccurate, the believers in "the model" usually don't want to admit that the model is worthless, and instead keep adding tweaks that never predict reality. In a former employment, I was once tasked with deriving such things as activation energies to fit Arrhenius equations to measured aging data so as to predict future aging of a specific parameter. When a single Arrhenius equation did not match the data, the model believers said that there must be more than one reaction occurring, so add another Arrhenius equation to the model. When that didn't match, they added a third. The model never matched the actual experimental data, but I was told we had to use it because that is the theory and the customer expected us to use that theory to predict future performance. I laughed to myself and gave them this huge complicated model that "best" fit the data to their theory. In reality, it could have been much more accurate and simple. The actual experimental data was that the output parameter being modelled when plotted versus time was simply a straight line! A simple linear straight line fit the data perfectly! But no one wanted to use a simple straight line as a model. That didn't match their theory.
Speaking of exaggerated estimates…
What about this statement in an article* about AstraZeneca withdrawing its COVID-19 vaccine?
QUOTE
In a statement the company said: “We are incredibly proud of the role Vaxzevria played in ending the global pandemic. According to independent estimates, over 6.5 million lives were saved in the first year of use alone and over three billion doses were supplied globally.
END OF QUOTE
How do they work out “over 6.5 million lives were saved in the first year of use alone”?
*AstraZeneca withdrawing Covid vaccine worldwide: https://www.telegraph.co.uk/news/2024/05/07/astrazeneca-withdrawing-covid-vaccine/
No need to explain…simply lie repeatedly
Here’s some more recent propaganda in The Lancet:
Contribution of vaccination to improved survival and health: modelling 50 years of the Expanded Programme on Immunization - The Lancet
https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(24)00850-X/fulltext
Check out the Declaration of interests and Acknowledgments…
Yep, a pure “inside job”
Excellent article. Logically presented and well-written.
Now the pushback, questions, and requests. :)
1. The New York data - The theory that models were used to guide the reporting of COVID deaths but were never reflections of what was occurring on the ground is one I've proffered numerous times as one possible explanation for the obviously-manipulated all-cause death curve. For that reason, more than anyone, I would love to be able to embrace the method Thomas Verduyn applied to NYC DOH and the JHU dashboard that led him to claim the results as proof the deaths weren't happening in real time and a model was used.
However, any analysis of the New York data must be mindful of what was being reported by officials at time -- not because the officials were telling the truth, but because it sheds light on the manipulation AND sets parameters for what constitutes proof.
I draw your attention to what I showed Mr. Verduyn along those lines. https://open.substack.com/pub/pandauncut/p/the-dashboard-that-ruled-the-world?r=jjay2&utm_campaign=comment-list-share-cta&utm_medium=web&comments=true&commentId=54174650
https://open.substack.com/pub/pandauncut/p/the-dashboard-that-ruled-the-world?r=jjay2&utm_campaign=comment-list-share-cta&utm_medium=web&comments=true&commentId=54404997
My request is NOT that you adjudicate the conversation between he & myself, but that you offer your own “take” on the implications of what was reported at the time so that I can understand how his method - and your implicit endorsement thereof - is "almost irrefutable proof that the probable death numbers were artificially generated on a computer."
If it is such proof, I am prepared to accept it humbly include it in a letter I’m in the process of writing to relevant elected and appointed officials.
Much more importantly, I believe the adding of “the probables” is representative of something that happened on a much larger scale in locations worldwide, if only to a lesser degree. (NYC is the exception that proves the rules.)
2. FYI, in at least two press conferences in April 2020, Governor Andrew Cuomo said that the COVID death numbers being reported (up until a certain point) were hospital deaths only. I believe this is critical to understanding what may have been done in numerous places worldwide.
3. You cited this link to Dr. Levitt’s DP work. https://heatherrenkel.github.io The name on the site is Heather Renkel. Who is Heather Renkel, i.e., what is her relationship to Dr. Levitt? (grad student?) Is this analysis published elsewhere?
4. Can you provide a direct link to the “Winter Scenarios” graph? I can't find it (which may be user error).
5. As Prof Neil is aware, I agree with your conclusion that “epidemiological models of respiratory pathogens produce hopelessly exaggerated estimates based on assumptions about parameters that are either unknowable or are so difficult to measure that their credibility is highly suspect.” While not the purpose of your article, can at least one of you (the authors) comment on the implications of this conclusion on all-cause death curves for 2020 and beyond? Do you agree with the assertion/assumption of Denis Rancourt & colleagues are necessarily valid & reliable?
Thank you kindly.
You can call me Martin!
Proof is perhaps too strong a word. Strong suspicions? What strength of support do you think his arguments merit? Credible/plausible/possible/.....?
I agree there remain a lot of gaps and questions as you would expect in an ongoing detective story. It's not a closed book by a long chalk. I keenly await to here new views and discoveries.
Winter scenarios....let me try and dig but I'm about to jump on a plane so might forget.
Rancourts work (and that of others) on excess deaths....I've scanned it but haven't had the time to do a deep dive review so can't really comment. I've not posted on it here or anywhere else. Whether I ever find time to do so I'm not too sure to be honest. There are only so many hours in the day.
I'm also happy to hear skepticism about official mortality data. After all if other data is manipulated why not that? Without citizen led inspection of death certificates and double checking with families there will be some room for doubt - how much doubt is warranted I'm not too sure. I know neither Norman nor Scott has looked at it.
Levitt website. I'm assuming it is an honest representation of his work. Who Renkel is I don't know and have not dug deeper. Do you have reason to believe it is not what it purports to be?
Re: NYC/JHU - It isn't new views that need attention; it's knowledge of the "old" events as they occurred and were reported. :)
Key events for review here: https://substack.com/profile/32813354-jessica-hockett/note/c-56073901?utm_source=notes-share-action&r=jjay2
The official story is that JHU was following/trying to follow NYC, not vice versa. How then is the method Verduyn used near-proof of a model being used?
Rancourt - the question isn't about his work; it's about the assertion that ACM data are necessarily reliable and not subject to manipulation. Do you think ACM data isn’t susceptible to and cannot be altered by observational or reporting biases? (FWIW, yesterday, Rancourt indicated to me that he still holds that view: https://woodhouse.substack.com/p/denis-rancourt-on-the-nyc-spring?r=jjay2&utm_campaign=comment-list-share-cta&utm_medium=web&comments=true&commentId=55981139_)
I'm asking because I'm not clear on what you believe the implications are of what you're showing about dashboards—either for COVID or all-cause death curves. Were dashboards too zealous and deaths force-fitted to the models? What is the relationship between the dashboard COVID curves and the official/final COVID and all-cause daily death curves in respective jurisdictions?
Re: Renkel - I don't know who it is in real life or what the relationship is to Dr. Levitt. Earliest DP article I can find by Dr. Levitt himself is this one https://medium.com/@michael.levitt/the-excess-burden-of-death-from-coronavirus-covid-19-is-closer-to-a-month-than-to-a-year-83fca74455b4
Thanks.
Two things stick out - 4/3 proportion and smoothness of the curve and it being too close a match to the abstract/perfect theoretical representation.
I don't know the mechanisms. You call fraud on NYC without knowing the mechanism. Yes?
Absolute knowledge isnt a prerequisite here.
I've acknowledged that mortality statistics could be manipulated. I might acknowledge financial fraud in the interbank trading market is a real possibility based on observed market moves but I dont think it would be beholden on me to say how trades and prices were being exactly manipulated.
Not sure why I'm being bracketed with Rancourt.
Can you accept I've no fully formed view of some issues and dont claim anything close to omniscience?
Re: "4/3 proportion and smoothness of the curve and it being too close a match to the abstract/perfect theoretical representation" - I don't disagree with that and have made similar observations about the all-cause curve, especially the rise. The cardiac arrest curve looks engineered to me as well. Indeed, the whole event does.
But - unless I am mistaken - Verduyn's JHU dashboard article used NYC as illustrative of JHU reporting numbers were using models. What I am trying to show you is that NYC isn't a good example of that because ("officially") it was JHU catching up with NYC, not vice versa.
Plus, the 2020 vital statistics report from NYC DOHMH provides a explanation that could further be used to excuse the fact that JHU had a hard time "retro-fitting" the probables into the NYC DOHMH COVID death curve.
I wasn't bracketing you w/Rancourt. I realize I inadvertently left out words in my initial reply. My question should have been, "Do you agree with the assertion/assumption of Denis Rancourt & colleagues that all-cause death numbers are necessarily valid & reliable?"
My intent was to cite Rancourt as an example of someone who (like you) doesn't believe there was a pandemic and doesn't accept the WHO's baloney about a new cause of death, yet holds the view that all-cause death data is legitimate because it isn't susceptible to manipulation.
I believe there is ACM fraud, not just COVID death fraud. The nonsense with the dashboards absolutely helped convince the world that "something wicked this way comes" to a city near you but it was not/is not clear in the present article that and your co-authors hold that view. Thank you for clarifying that you agree with me, i.e., that ACM fraud is possible, not impossible. :)
Math is not my subject but graphs are visual enough to be helpful. What's been most striking is we were told this deadly, novel virus spread so fast it covered the globe in a few months. But the biggest spike in cases followed "safe & effective" countermeasures.
My sympathies lie with folks whose eyes glaze over when numbers become the focus but between your NYC mass casualty chart and the World Data chart at top soaring in 2022 even the dimmest bulbs should be able to look at the big picture & see the spectacular lies.
Big time kudos and thanks for tackling the data translating to impactful images for all! <3
"OurWorldInData"
Be wary, as 'OurWorldInData' is not reliable (see: https://thedailybeagle.substack.com/p/fudgegate-cdc-caught-fudging-vaccine). Their charts are ultimately a front-end facade making use of *heavily* cherry picked datasets (often with repeat singular source citations) and it is strongly recommended to pull the data the charts refer to rather than using the facade presented data.
In the above CDC case, 'OurWorldInData' essentially obfuscated the CDC's fudging of mortality statistics where they used erroneous population numbers with floating points, which in Jikkyleaks' view was evidence of them using fabricated computer modelling rather than accurate representative datasets.
The other source to avoid quoting is Statistica. It acts in a similar capacity to OurWorldInData, but it is arguably worse - the sources are often paywalled, which means the veracity of the claims cannot be verified publicly.
Without knowing what OWID was actually quoting as a source for the 'confirmed cases', the comparison is almost meaningless. That said, I agree with your theory JHU were using a computer model because the numbers echo similarly to Ferguson's bunk figures.
The first rule of models is that all models are wrong; some are still useful.
I'm currently listening to A plague upon our House by Scott Atlas (free on Audible for another week or so, but worth buying too). It is astonishing, the way he describes how key figures in the White House Corona taskforce relied on/misinterpreted data from sites like OurWorldInData. He is particularly critical of Birx's interpretation of statistics and the way this influenced national policy (and by extension global government policy responses).
In this interview in 2020 Knut Wittkowski suggests that Ferguson's 500K projection wasn't based on a model, but was actually a back of an envelope calculation: https://www.spiked-online.com/2020/05/15/we-could-open-up-again-and-forget-the-whole-thing/
'Governments did not have an open discussion, including economists, biologists and epidemiologists, to hear different voices. In Britain, it was the voice of one person – Neil Ferguson – who has a history of coming up with projections that are a bit odd. The government did not convene a meeting with people who have different ideas, different projections, to discuss his projection. If it had done that, it could have seen where the fundamental flaw was in the so-called models used by Neil Ferguson. His paper was published eventually, in medRxiv. The assumption was that one per cent of all people who became infected would die. There is no justification anywhere for that.
Let us say the epidemic runs with a basic reproduction rate of around two. Eventually 80 per cent of the population will be immune, because they have been infected at some point in time. Eighty per cent of the British population would be something like 50million. One per cent of them dying is 500,000. That is where Ferguson’s number came from...
...And it is going on and on and on, just because governments are afraid of admitting an error. They are trying to find excuses. They say they have to do things slowly, and that they have ‘avoided 500,000 deaths’ in the UK. But that was an absurd number that had no justification. The person presenting it pretended it was based on a model. It was not a model. It was the number of one per cent of all people infected dying. And nobody was questioning it. And that is the basic problem. '
Computer models, in any science (astronomy, geology, epidemiology or whatever) are nothing more than guesswork based on stacked (i.e. compounded) assumptions. They are invariably wrong, to a lesser or greater extent (FAR greater in the case of the 'covid' fraud). Garbage in, garbage out.
When the pandemic justifies the model, you know the results will be pure fear porn to justify relentless expansion of our metastasizing Total State.
Manufacturing fear. Rulers have always done this to maintain power. Now undemocratic trans-national organizations do it via their agents that used to be our elected governments and previously honest academic institutions now corrupted by money.
After decades of reacting to corporate statistical modelling, what has become glaringly obvious it that the data adjustment criteria if always, only and ever informed by motivated reasoning seeking a predetermined end point or outcome.
That predetermined outcome is usually nothing more that implementing marginal and direct expense reduction. From the perspective of the corporate entity, if the reduction target is below a Pareto neutral threshold, implementing cost transfers will do quite nicely.
The statistical legerdemain involved with the Covid prognostications fit that pattern, and they fit it like a glove. We've even seen the cost transfer corollary added into the mix. Rhetorical legerdemain is the boon companion of cost transfer. Reference the "with Covid or from Covid" cause of death reporting distortions for verifiable support of the assertion.
No Amnesty
I’m still having trouble understanding what is ‘Covid’?
Has this been confirmed as a unique disease, verifiable by a test?
Also, please consider this statement, by ‘leading infectious diseases physician’ Peter Collignon, re the AstraZeneca vaccine and risk of blood clots, who says “…in my view…the fear and the publicity about the adverse effect caused more deaths than the actual vaccine did. If you were an 80-year-old and got Covid, you had a 1 in 10 chance of dying. Yet, if you had this vaccine, you had a 1 in 100,000 chance of dying from the effect of a complication. Yet, there were a lot of people, because we had zero Covid at the time, who said, ‘oh no, I’ll wait because the Pfizer vaccine’s better’.””*
Pick that statement apart…is it not gobbledygook? What’s the evidence for any of it?
Really…do any of these ‘experts’ have a clue what they’re talking about?
*Paywalled article: AstraZeneca Covid vaccine withdrawn over side effects but ‘fear more deadly, The Australian, 9 May 2024.
It's WHO ICD code U07.1
That's it.
The sooner people realize it, the better
Of course, part of the problem is that there never WAS any Pandemic?
My hypothesis regarding case numbers: those early models were not that far off in terms of final incidence levels claimed, only the assumed mechanism is bogus. At the turn of 2023/2024, I compared different methods for estimating case numbers for Germany:
https://cm27874.substack.com/p/virus-victor
If it is possible that official figures (obtained by testing) became meaningless at the beginning of 2023, it is equally possible that official figures were already meaningless in 2020/2021. Unfortunately, back then neither cohort testing nor random testing were performed. I wrote about this on March 18, 2020 (https://dochdoch.substack.com/p/was-kommt-vor-dem-weltuntergang), and then three years of frustration followed...
Making the facts fit the theory.
The soring jabberwocky is underway in my small town and the faithful are duly lining up; not a doubting face to be seen.😵💫😵💫
I am resisting the temptation to quote "Jabberwocky" from memory. :)
spring jabberwocky-typo..