Empirical analysis of symptomatic COVID and COVID deaths in England reveals no positive impact of any interventions but 100% concurrency between vaccinations and COVID "outbreaks" in late 2021.
It is quite extraordinary that pretty much the entire sequence can be explained by expressing viral waves using Gompertz distributions, which itself tells us that each wave is strictly self-limiting (and, as you show, immune to practical intervention - what a surprise for an airborne virus when there is already a high degree of natural immunity ), and recognising that vaccines are, in fact, key explanatory variables.
Even allowing for the distortion of many of the target variables, for example, "infections" and "CV-19 deaths", the analysis is compelling.
One thought; with regard to the full impact of the vaccines on mortality, it would be interesting to use all-cause deaths as the target variable, not least because that is less easily manipulated than flakily defined (manipulated) "CV-19 deaths". That would also make it easy to trace sequential vaccine impact by age group since that is how they were rolled out.
Finally, it might be interesting to include the data for the widespread use of the euthanasia drug of choice, Midazolam (something like two years supply used up in just three months, largely targeting the elderly), one of the other great scandals in this very sad saga.
Thanks for your comments, Roger. As per the link to my previous work, COVID deaths in England do pretty much coincide exactly with all cause mortality so I don't think the latter would change much, not for this analysis at any rate. Also, an important point is that I don't think the Gompertz function characterises the virus. It characterises the host. It is still self-limiting in the sense that once all the infected vulnerable have become ill, and/or hospitalised and/or died, that's it for that particular group. Where a new Gompertz emerges it is because there is a new susceptible/vulnerable population. Either because the virus is introduced to them (the care home hypothesis) or because the vulnerability of the host was affected (the vaccine hypothesis).
Yes, that's a good point about Gompertz - the waves relate to infections (in any sane world, symptomatic cases - remember long ago when we used to call asymptomatic people healthy?) which of course tells us something about the potential host population at any point in time.
Ordinarily, of course, we might expect to see a whole lot of variants, possibly more easily transmitted, but milder in terms of impact. Instead, I'm pretty sure what we're actually seeing now is the testing of the vaccine hypothesis (and you don't need CV-19 for this, any old virus will do).
All difficult to disentangle in the midst of respiratory illness season, but I suspect it will get a lot clearer as the year progresses.
First off, thank you for writing out this unfolding disaster. There is so much here to think about but the most important thing I wish people paid attention to right now is taking this hypothesis very seriously because it has consequences right now!
"The youth are also much more at risk of damage to their innate immune system due to vaccination and they rely on that more than their under-developed adaptive system⁶."
I posted just a little bit of "evidence" (raw data abused through regression modeling) from Nick Andrew et al.'s Supplementary (suppressed) Appendix showing epidemic spread during vaccination of 12-15 and 16-17.
You know the really awful thing, which to my mind proves evil criminality in its intent, is that everything that needed to be known about the so-called vaccines was known at the time of approval.
The biodistribution studies alone, showing that vaccine-induced spike proteins rapidly entered the bloodstream and circulated freely throughout the body, collecting in major organs, the brain, lymph nodes, etc, should have been enough to ensure that they were never approved.
Add to that the data from the likes of Pfizer which, we now know, showed chronic direct (not side) effects of the vaccines, including more all-cause deaths in the vaccine group versus the placebos, and the obvious manipulations which were needed to fake efficacy, it becomes apparent that the vaccines were approved despite the evidence, not because of it.
Independent experts were not guessing when they warned of the outcomes; they had the evidence and ample studies to justify the warnings. All we are seeing now with the actual data (and we are still only seeing a small part of it) confirms an unfolding catastrophe which is only now getting started.
Sadly, the vaccination of children is already blowing up; we see it in the surge of heart problems, adding to the other crippling side effects. We will soon see it in diminished immunity, which is only a function of time post injection.
Quite simply, it is already too late to reverse or undo the catastrophe for many; soon it will be too late for hundreds of thousands of children.
Excellent. Clear, precise, well laid out and easy to follow for those of us not trained in statistical analysis. My personal take is malfeasance. Occam's razor gets me there.
Incredible work from Joel. Ive only now found Joels substack after missing him on Twitter (where proper access/interaction was restricted in any event after being kicked off before Joel). I am going to start putting links around to his work to make sure others come here for genuine data, and inspiration.
Fascinating and convincing - or is that just my confirmation bias? So many interesting concepts I never heard of before this pandemic.
If you have time Sir, I'd appreciate if you could clarify something in figure 5. It looks to me like the third Gompertz wave has an offset relative to 0 on the y-axis. Is that an artifact of my screen or if real, what does it mean?
I can mathematically grasp the concept that a function with just 2 parameters would be an unlikely outcome of a system exposed to impulses (like lock-down and masking) midway through the curve (well provided the impulses had any significant impact that is).
If I try to interpret the meaning of the Gompertz curve in a pandemic context its derivative would be something like: (R0-1)/k, correct? Mitigations that are instantaneous like lock-down, mask mandates etc, should (if they worked) cause a step reduction in the R0, while vaccination which is gradual should be less sudden.
There are a lot of pandemic simulators and had my SW skills been on par with my curiosity, I would have liked to see how much difference a -N% step in R0 should generate in a Gompertz curve for (N=10, 25, 50), depending on when the step is introduced. I guess one needs to account for the delay between the step and the time to symptoms.
I believe such an exercise would make the argument for using Gompertz curves as proof of the mitigations being useless even more compelling.
Thank you Sir for this and all your previous equally interesting posts!
I don't think the function is analogous to R0 because I think it is a function of the condition of the host more than the spread of the virus. But yes, it is easy to show the various changes you suggest. The first parameter is the growth from t to t+1. The second parameter is decay of that growth applied exponentially. So, instead of using a constant decay, you could use an increasing decay which would produce a shallower rise or steeper fall depending on where on the curve you placed the adjustment.
Maybe I'm confused if the symptomatic data represents new diagnosed infections per day, or ongoing infections (the integral of the former). Either way it seems to me there should be a relation between R0 and the derivative (or double derivative) of the Gompertz curve - with a lag for incubation time. So I still wondering if that wouldn't be a concrete path from mathematical concept (beautiful as it is) and winning pandemic argument.
It may well be. This is just my opinion. It would require some really deep, cross-discipline research to establish what degree is R0 and what is host susceptibility. For me, it doesn't matter, the maths obviously works and knowing exactly why doesn't change the outcome.
I'm glad you spotted that. I meant to write about it. As you can see in figure 1, the beauty of this function is that you can fit it anywhere along the curve and get the complete distribution. I typically fit to periods 3 to 5 because 1 and 2 are small numbers and can get noisy. I then back fill them. However, if I back fill no. 3 in figure 5, my aggregate curve is higher than the actual empirical data. No. 3 does not start gradually like the others. It comes in with a bang.
So this bang would be explained by the start of vaccination was really a step, with several thousands vaccinated the first day. Fair enough. I guess it should be labeled as a super-spreader event...
Great article. I guess one of the things that could be done is to map the typical flu curves for previous years as you should see the same school then university outbreaks in autumn as well as the typical winter deaths. The distributions in spring and summer themselves appear to be a break from the norm that looks likely to be vaccine induced.
Indeed. I could not find a trigger for the August 2020 resurgence and indeed, that is actually when elderly mortality begins to tick up. I was surprised that it always starts this early to be honest. Maybe university return was just a coincidence or it might also be further confirmation that schoolchildren are NOT significant transmission vectors (due to their strong innate immunity).
The fact that these overall curves can be modelled as a sum of simple functions is fascinating. The fact that these simple functions themselves can be temporally related to specific events is more fascinating still. It's almost like you've found the fundamental "modes of oscillation" here - akin to being able to resolve a complex sound into a set of simpler fundamental "pure" oscillations.
Just a thought here - the Swedish mortality data does appear to be quite tightly "correlated" with the UK data - at least on a cursory visual examination. Could you use the comparison of the "fundamental modes" for the 2 countries to shed light on whether the lifting of lockdown 2 (2nd dec 2020) was in fact responsible for the triggering of another infection (another "mode")?
Sweden (I think) wouldn't have had any triggering due to either lifting of lockdown (they didn't lockdown), or vaccines (I think they started vaccinating later). Comparing the fundamental "modes" of different countries - and tying them into various measures could be a really powerful way of understanding what's gone on - especially if a consistent causality pattern is found across countries who adopt different measures at different times.
I'm not sure how seasonality will play into any of this though?
Life insurance company OneAmerica’s ceo told the Indiana Chamber of Commerce January 3rd that deaths among working age people is off the charts.
A $100 billion Indiana based life insurance co. saw 3rd quarter and now 4th quarter 2021, death rates up 40% ages 18-64. “Most of the filed claims are not Covid deaths” the ceo said. “40% is unheard of”. Covid policy, adverse events post vaxx, suicides?
This is another deadly sin that will prove even more expensive when the truth comes out.
"Conversely, in addition to the death distribution that follows the infection wave triggered by the start of mass vaccination (which prioritised healthcare workers), we can observe a new distribution contained entirely within it which starts exactly when the most elderly and frail were vaccinated. "
I have evidence from Denmark, New York, UK, Kerala (Indian State) of strong suggestion hospital infections after vaccinations started in the respective countries.
Good work Joel.
It is quite extraordinary that pretty much the entire sequence can be explained by expressing viral waves using Gompertz distributions, which itself tells us that each wave is strictly self-limiting (and, as you show, immune to practical intervention - what a surprise for an airborne virus when there is already a high degree of natural immunity ), and recognising that vaccines are, in fact, key explanatory variables.
Even allowing for the distortion of many of the target variables, for example, "infections" and "CV-19 deaths", the analysis is compelling.
One thought; with regard to the full impact of the vaccines on mortality, it would be interesting to use all-cause deaths as the target variable, not least because that is less easily manipulated than flakily defined (manipulated) "CV-19 deaths". That would also make it easy to trace sequential vaccine impact by age group since that is how they were rolled out.
Finally, it might be interesting to include the data for the widespread use of the euthanasia drug of choice, Midazolam (something like two years supply used up in just three months, largely targeting the elderly), one of the other great scandals in this very sad saga.
Thanks for your comments, Roger. As per the link to my previous work, COVID deaths in England do pretty much coincide exactly with all cause mortality so I don't think the latter would change much, not for this analysis at any rate. Also, an important point is that I don't think the Gompertz function characterises the virus. It characterises the host. It is still self-limiting in the sense that once all the infected vulnerable have become ill, and/or hospitalised and/or died, that's it for that particular group. Where a new Gompertz emerges it is because there is a new susceptible/vulnerable population. Either because the virus is introduced to them (the care home hypothesis) or because the vulnerability of the host was affected (the vaccine hypothesis).
Yes, that's a good point about Gompertz - the waves relate to infections (in any sane world, symptomatic cases - remember long ago when we used to call asymptomatic people healthy?) which of course tells us something about the potential host population at any point in time.
Ordinarily, of course, we might expect to see a whole lot of variants, possibly more easily transmitted, but milder in terms of impact. Instead, I'm pretty sure what we're actually seeing now is the testing of the vaccine hypothesis (and you don't need CV-19 for this, any old virus will do).
All difficult to disentangle in the midst of respiratory illness season, but I suspect it will get a lot clearer as the year progresses.
First off, thank you for writing out this unfolding disaster. There is so much here to think about but the most important thing I wish people paid attention to right now is taking this hypothesis very seriously because it has consequences right now!
"The youth are also much more at risk of damage to their innate immune system due to vaccination and they rely on that more than their under-developed adaptive system⁶."
I posted just a little bit of "evidence" (raw data abused through regression modeling) from Nick Andrew et al.'s Supplementary (suppressed) Appendix showing epidemic spread during vaccination of 12-15 and 16-17.
https://www.gettr.com/comment/cfky3g2daf
The vaccination of children is going to blow up everything very soon. UK will find out, India will find out, so will Israel.
You know the really awful thing, which to my mind proves evil criminality in its intent, is that everything that needed to be known about the so-called vaccines was known at the time of approval.
The biodistribution studies alone, showing that vaccine-induced spike proteins rapidly entered the bloodstream and circulated freely throughout the body, collecting in major organs, the brain, lymph nodes, etc, should have been enough to ensure that they were never approved.
Add to that the data from the likes of Pfizer which, we now know, showed chronic direct (not side) effects of the vaccines, including more all-cause deaths in the vaccine group versus the placebos, and the obvious manipulations which were needed to fake efficacy, it becomes apparent that the vaccines were approved despite the evidence, not because of it.
Independent experts were not guessing when they warned of the outcomes; they had the evidence and ample studies to justify the warnings. All we are seeing now with the actual data (and we are still only seeing a small part of it) confirms an unfolding catastrophe which is only now getting started.
Sadly, the vaccination of children is already blowing up; we see it in the surge of heart problems, adding to the other crippling side effects. We will soon see it in diminished immunity, which is only a function of time post injection.
Quite simply, it is already too late to reverse or undo the catastrophe for many; soon it will be too late for hundreds of thousands of children.
Thanks, Joel.
I have "briefed" my pen pal the NSW Australia Health Minister on it.
Excellent. Clear, precise, well laid out and easy to follow for those of us not trained in statistical analysis. My personal take is malfeasance. Occam's razor gets me there.
This is an excellent piece of work Joel, thank you.
Incredible work from Joel. Ive only now found Joels substack after missing him on Twitter (where proper access/interaction was restricted in any event after being kicked off before Joel). I am going to start putting links around to his work to make sure others come here for genuine data, and inspiration.
Thank you, John. Your kind comments are much appreciated.
Thank you, Russell. Appreciation keeps me going!
Fascinating and convincing - or is that just my confirmation bias? So many interesting concepts I never heard of before this pandemic.
If you have time Sir, I'd appreciate if you could clarify something in figure 5. It looks to me like the third Gompertz wave has an offset relative to 0 on the y-axis. Is that an artifact of my screen or if real, what does it mean?
I can mathematically grasp the concept that a function with just 2 parameters would be an unlikely outcome of a system exposed to impulses (like lock-down and masking) midway through the curve (well provided the impulses had any significant impact that is).
If I try to interpret the meaning of the Gompertz curve in a pandemic context its derivative would be something like: (R0-1)/k, correct? Mitigations that are instantaneous like lock-down, mask mandates etc, should (if they worked) cause a step reduction in the R0, while vaccination which is gradual should be less sudden.
There are a lot of pandemic simulators and had my SW skills been on par with my curiosity, I would have liked to see how much difference a -N% step in R0 should generate in a Gompertz curve for (N=10, 25, 50), depending on when the step is introduced. I guess one needs to account for the delay between the step and the time to symptoms.
I believe such an exercise would make the argument for using Gompertz curves as proof of the mitigations being useless even more compelling.
Thank you Sir for this and all your previous equally interesting posts!
I don't think the function is analogous to R0 because I think it is a function of the condition of the host more than the spread of the virus. But yes, it is easy to show the various changes you suggest. The first parameter is the growth from t to t+1. The second parameter is decay of that growth applied exponentially. So, instead of using a constant decay, you could use an increasing decay which would produce a shallower rise or steeper fall depending on where on the curve you placed the adjustment.
Maybe I'm confused if the symptomatic data represents new diagnosed infections per day, or ongoing infections (the integral of the former). Either way it seems to me there should be a relation between R0 and the derivative (or double derivative) of the Gompertz curve - with a lag for incubation time. So I still wondering if that wouldn't be a concrete path from mathematical concept (beautiful as it is) and winning pandemic argument.
It may well be. This is just my opinion. It would require some really deep, cross-discipline research to establish what degree is R0 and what is host susceptibility. For me, it doesn't matter, the maths obviously works and knowing exactly why doesn't change the outcome.
Check out this comment from Dr Paul Alexander - "You are being routinely exposed give (sic) virus is all around." https://palexander.substack.com/p/the-vaccinated-are-at-substantial
In other words R0 is a function of host susceptibility not transmission of the virus from host to host.
I'm glad you spotted that. I meant to write about it. As you can see in figure 1, the beauty of this function is that you can fit it anywhere along the curve and get the complete distribution. I typically fit to periods 3 to 5 because 1 and 2 are small numbers and can get noisy. I then back fill them. However, if I back fill no. 3 in figure 5, my aggregate curve is higher than the actual empirical data. No. 3 does not start gradually like the others. It comes in with a bang.
So this bang would be explained by the start of vaccination was really a step, with several thousands vaccinated the first day. Fair enough. I guess it should be labeled as a super-spreader event...
Yes. It's quite ironic that everything they claim is the opposite of what really happens.
Thanks Joel. Brilliant as always. Although horrendous at the same time.
Great article. I guess one of the things that could be done is to map the typical flu curves for previous years as you should see the same school then university outbreaks in autumn as well as the typical winter deaths. The distributions in spring and summer themselves appear to be a break from the norm that looks likely to be vaccine induced.
Indeed. I could not find a trigger for the August 2020 resurgence and indeed, that is actually when elderly mortality begins to tick up. I was surprised that it always starts this early to be honest. Maybe university return was just a coincidence or it might also be further confirmation that schoolchildren are NOT significant transmission vectors (due to their strong innate immunity).
All Hail Metatron!
Another fabulous piece of work Joel.
The fact that these overall curves can be modelled as a sum of simple functions is fascinating. The fact that these simple functions themselves can be temporally related to specific events is more fascinating still. It's almost like you've found the fundamental "modes of oscillation" here - akin to being able to resolve a complex sound into a set of simpler fundamental "pure" oscillations.
Just a thought here - the Swedish mortality data does appear to be quite tightly "correlated" with the UK data - at least on a cursory visual examination. Could you use the comparison of the "fundamental modes" for the 2 countries to shed light on whether the lifting of lockdown 2 (2nd dec 2020) was in fact responsible for the triggering of another infection (another "mode")?
Sweden (I think) wouldn't have had any triggering due to either lifting of lockdown (they didn't lockdown), or vaccines (I think they started vaccinating later). Comparing the fundamental "modes" of different countries - and tying them into various measures could be a really powerful way of understanding what's gone on - especially if a consistent causality pattern is found across countries who adopt different measures at different times.
I'm not sure how seasonality will play into any of this though?
Sweden % of population registering new cases vs newly vaccinated rate:
2020-51: 0.5% vs 0.0%
2020-52: 0.3% vs 0.0%
2020-53: 0.4% vs 0.2%
2021-01: 0.5% vs 0.4%
2021-02: 0.3% vs 0.9%
So, Sweden uptick is 3 weeks later than England, as is their start of vaccinating. Just another coincidence?
Israel vs Palestine is even more compelling. https://metatron.substack.com/p/correlation-causation
Good idea.
Life insurance company OneAmerica’s ceo told the Indiana Chamber of Commerce January 3rd that deaths among working age people is off the charts.
A $100 billion Indiana based life insurance co. saw 3rd quarter and now 4th quarter 2021, death rates up 40% ages 18-64. “Most of the filed claims are not Covid deaths” the ceo said. “40% is unheard of”. Covid policy, adverse events post vaxx, suicides?
This is another deadly sin that will prove even more expensive when the truth comes out.
"Conversely, in addition to the death distribution that follows the infection wave triggered by the start of mass vaccination (which prioritised healthcare workers), we can observe a new distribution contained entirely within it which starts exactly when the most elderly and frail were vaccinated. "
I have evidence from Denmark, New York, UK, Kerala (Indian State) of strong suggestion hospital infections after vaccinations started in the respective countries.