Have you connected with Denis Rancourt yet, Joel, and are you aware of his latest excess mortality analysis putting the injection death tally at 17 million (1 death per 470 living persons)?
“We quantify the overall all-ages vDFR for the 17 countries to be (0.126 ± 0.004) %, which would imply 17.0 ± 0.5 million COVID-19 vaccine deaths worldwide, from 13.50 billion injections up to 2 September 2023. This would correspond to a mass iatrogenic event that killed (0.213 ± 0.006) % of the world population (1 death per 470 living persons, in less than 3 years), and did not measurably prevent any deaths.” (https://denisrancourt.substack.com/p/covid-19-vaccine-associated-mortality)
I had previously been using Denis’s May 2023 National Citizens Inquiry presentation along with his 894-page book of exhibits to support his calculation that 13 million had been murdered by injection to date (video and associated links included in my last article: https://margaretannaalice.substack.com/p/dissident-dialogues-margaret-anna), and now that total is up to 17 million. Gob-bloody-smacking.
I'll be honest and say I haven't fully understood what you did.
However I'd like to point out that any excess mortality measure fails to capture short-term changes if it uses pre-COVID data. People were dying of all kinds of things in the COVID-era and if I want to know the impact of vaccinations, all these paths water down the relationship between vaccinations and deaths.
The best success I've had is simply using an individual reference timeframe where all the factors impacting mortality I do not care about are already present.
For Q3/2021 in the USA this is June/2021, where mortality is at a COVID-era low. When I do that and run regressions between vaccination rates and doses, I get really impressive results.
Fair enough. If all you're interested in is vaccines. Even so, your baseline will be polluted all the same. As long as you are transparent with your method, people that use the results make the necessary adjustments or caveats as they see fit.
Yeah, right now all I am interested in is vaccinations. I implicitely alleged that you are, too, which is obviously not the case.
And yes, the baseline is polluted, but that's the idea when I want to know the impact of vaccination rates being cranked up. And ofc, always 100% transparent. It has never been my intention to mislead and part of why I barely publish anything anymore is because I'm busy invalidating everything I do. :D
I use a similar approach, comparing the mortality rate to a time when it is at its minimum then looking at cause of death data to analyze what the excess is due to.
Looking at the ONS data for the UNVACCINATED in Table 1, your reference timeframe shows ASMR of 2088 in Apr 2021 and 1721 in May 2021. Your "death wave in July" is 1688 per 100K person-years.
The reference value that I use is the low value in September 2022 of 1078.
How are you able to explain this data for the unvaccinated?
You can also look at the unvaccinated 18-39 age group in Table 2 and see that the pattern is a slightly different. The ASMR is low in April and May but was elevate in July 2021 and didn't return to this "normal" level until Sept 2022.
With the July/August wave in the US it's easy, all states are bottoming out at the same time (June), but if it weren't the case, I thought using an average of the "lowest x weeks" would work.
I'm taking a similar approach as Smalley except that I don't worry about convexity. The effect of convexity is insignificant when looking at the timeframe of a single wave. The pattern of peaks is the same no matter how much effort is taken to establish the baseline.
What matters most about the peaks is that they rise exponentially. People should be fitting curves of the form a + b * exp(r *t) to regions like Jun 1 2021 to Aug 31 2021 and seeing the close fit. Contagious diseases spread exponentially; other mechanisms that have been suggested do not.
Yeah sorry about that. I wasn't familiar with the data before and just briefly glanced at OWID.
I must've misread the chart and just realized it.
I've got all the data in my db now and it also looks like the effect that I mentioned is not present in the UK.
In the US there was an inversion of the correlation between vaccination rates and progress during Delta (cumulative doses p.c. and doses p.c.). Something about vaccinating the least vaccinated regions is highly dangerous I guess.
It can be observed on EU-level in October (particularly RO, BG and LV), but the waves are all asynchronous and the dynamics between vaccinations and waves are very complex. Vaccinating into "growing case rates" can fuel a wave much better than vaccinating into "declining case rates", due to the number of susceptibles decreasing. At least this is how it CAN be modelled.
Imho there is no other explanation besides infection enhancement (occurring in around the time of vaccination if exposed) for the delta waves that makes sense. Delta was at 100% relative prevalence almost everywhere by the end of July, but caused outbreaks much, much later in some places.
Somehow cumulative doses are hiding the effect of doses, possibly some rolling pull-forward effect. Natural immunity through infection enhancement in highly vaccinated regions.
I have a feeling that you are laboring under some presumptions that have been pushed in this substack.
It is true that Delta became predominant in the UK in May 2021 and the variant's prevalence remained low until July. It was later in the USA, not becoming predominant until July with the outbreak arriving in August. This is not unexpected for outbreaks. It takes time for a contagion to spread.
If you look at the "Reproduction rate" metric in OWID, it is easy to see the timing and extents of outbreaks (R > 1).
Are you saying that if there is a vaccination program while cases are rising, cases continue to rise and if while cases are falling, cases continue to fall? Isn't that the same thing as saying that there is no immediate impact of vaccinations, one way or the other?
No. In a SIV model where freshly vaccinated individuals (e.g. in the first week) have a higher ß than the remaining infected population, an increase in size of the pool of freshly vaccinated individuals will have the biggest impact early on.
If you vaccinate in the phase of exponential growth where susceptibles are near 100%, you'll see the wave become much steeper and peak earlier. The effect on timing would ofc be mitigated by cross-border travel, causing the pathogen to be seeded everywhere.
Depending on the beta of the remaining infected population there might never be a wave at all without freshly vaccinated individuals.
This type of model fits what I am seeing in the data. In the US data there is strong correlation between cases and doses towards the end of the exponential growth of the case rate (from Alpha through BA.2).
During delta this correlation extends to deaths (ACM, but particularly U07.1 deaths, even stronger in UCOD=U07.1 deaths).
If this is actually an effect of the vaccines, it might be hidden behind a data collection bias in early 2021. Trial data would suggest this effect existed in 2020 variants as well. Alas wastewater data is only available for a handfull of states in early 2021. It only starts rising to 49 states in late 2022. Positive PCR tests and cases are subject to bias and unreliable for other reasons.
Any effect of "doses" is also an effect of cumulative doses. So if vaccinations promote cases, this effect will have the strongest impact on highly vaccinated regions, causing them to have higher levels of natural immunity, creating the illusion of vaccine immunity.
There is no data on individuals who have an early infection after their dose. I can not find one account of amplification cycles of such individuals.
VAERS strongly supports this notion, with the number of infections reported to VAERS decreasing to ~10% after a week (compared to inoculation day).
I know this sounds like the gibberish of a maniac, but I've been sitting over the data for a year and it's the only explanation I can come up with for what I am seeing.
I stared at CDC Wonder data, worldometer and CDC data by weekday for 30 minutes just now (cases, doses, deaths) for Jan-Jun and Jul-Aug 2021) and I don't think there's much to go on.
I think you may be right. There was a commenter on the video over at bitchute that commented that all this video illustrates is that the more people in the sample then the more deaths in the sample. The rates of death are consistent over large numbers of people so would expect more deaths with a large sample and fewer deaths in a smaller sample. I saw something that was not there.
Frank I just refreshed my browser and I get sound. Double check that your little speaker icons are not crossed out. I sometimes think something is fishy but I see that the speaker is set to mute. Try to mute unmute your speaker.
I see the sound icon on my screen, that has an X through it. When I press unmute nothing happens and I still have no sound. When I access Youtube I still have sound.
Yes, I listened to the narrator this morning (I fixed the problem I had with the sound by just moving the sound control to the right) I must be getting paranoid :-) Now I can send the Craig / Bitchute onwards. Craig is a genius when it comes to statistics and he has such a pleasant accent from somewhere in southern England I guess.
Gosh but I have *absolutely* no idea what you have been doing. I can honestly say I don't understand anything! How embarrassing to have to admit this when I see all your commentators chatting away about things I totally don't understand! I think you are looking at deaths by year of birth but I'm not sure quite why - does it matter when you were born if you die without having been ill but only from having a novel injection? Surely all the people who were healthy, took the jabs and subequently died were killed by the only new thing in their lives?
Anyway, keep up the good work - just remember to stick a little idiot's explanation at the bottom for me!
Very simply because the cohort sizes are different every year. If there are twice as many births one year compared to the previous, when you look at deaths by age, you will inevitably have twice as many one year after the next even with everything else remaining the same. You will naively assume that the death rate doubled! even those who try to make population adjustments will likely fail. Look at the estimates - they vary nothing like the births did back in the day.
So, if, say, 1960 was a bumper baby year you would expect more people to die around 80 years later than on average? Bit like working out how many classrooms you will need in 1965 to accommodate all the extra 5 year olds. But surely people die at all sorts of ages, even in normal times, whereas all babies generally end up in school at aged 5. (I was so bad at maths I had to take an arithmetic O level and barely scrapped through that!).
If an age group was dying at a rate of about 550 per day in the days before Covid and the rate goes up to over 1100 a few months later, there is no great need to worry about an exact estimate of the population change. We know that the population didn't change that much in a few months. It's just a distraction.
We shouldn't be interested in comparing one year to another since the excess deaths occur in short periods within the year. We can compare short periods like the low period in 2021 (Apr - Jun) to the high period (Nov-Dec-Jan) without worrying that the population has changed in 7 months.
Yes, there are usually more virus deaths in winter than summer, probably influenced by levels of vitamin C in the blood stream. That should translate in differences to the death rate in the northern and southern hemispheres, where the seasons are reversed.
I keep track by noting death and disability of friends and family. They are almost all dead or extremely ill. So then I can watch the suffering and death of unknown fellow men. It does not appear things are going to improve.
I followed EUROMOMO during the scamdemic and The numbers were always interesting to what they were saying and the reality of mortality. I have read a bit about the % that died in hospitals is what is the deviation from the norm was most apparent. Indicating that covid wasn't the cause.
I used to use Euromomo too, back in the day. But, like most things, they adopt much the same methodologies as everyone else, which are full of fundamental flaws as I have tried to articulate through this mini series!
Interesting that you picked my age 88 (1935). Your chart suggests that Covid itself was responsible for the peak deaths in May 2020. Presumably that would have been during lockdown.
March 2021 must have been following the first two jabs, making vaccine the main culprit?
Undercover Epicenter Nurse blows the lid off the COVID-19 pandemic. What would you do if you discovered that the media and the government were lying to us .
Do you happen to have a first dose timeseries with infranational resolution for the UK?
Would be really nice if you could send me@pervaers.com one along with COVID deaths or all-cause deaths per 100k, so I can show you what I'm talking about.
Nice work Joel and interesting use of Gompertz. Wanted to add that I am obtaining a very similar excess mortality curve using a single XGBoost model on stacked data with three continuous predictors: Year of Birth, Month, and 3-Year Lag. The results appear robust. Happy to collaborate as needed.
Have you connected with Denis Rancourt yet, Joel, and are you aware of his latest excess mortality analysis putting the injection death tally at 17 million (1 death per 470 living persons)?
“We quantify the overall all-ages vDFR for the 17 countries to be (0.126 ± 0.004) %, which would imply 17.0 ± 0.5 million COVID-19 vaccine deaths worldwide, from 13.50 billion injections up to 2 September 2023. This would correspond to a mass iatrogenic event that killed (0.213 ± 0.006) % of the world population (1 death per 470 living persons, in less than 3 years), and did not measurably prevent any deaths.” (https://denisrancourt.substack.com/p/covid-19-vaccine-associated-mortality)
I had previously been using Denis’s May 2023 National Citizens Inquiry presentation along with his 894-page book of exhibits to support his calculation that 13 million had been murdered by injection to date (video and associated links included in my last article: https://margaretannaalice.substack.com/p/dissident-dialogues-margaret-anna), and now that total is up to 17 million. Gob-bloody-smacking.
Yes, he pops up here from time to time and I have summarised some of his research.
Excellent! I must have missed it in the backlog of 40,000+ Substack articles I'm behind on ;-)
Only fair to the rest of us who are behind 40,000 you caught first & then some! <3
If that's for me please paste a full URL not compressed link thanks.
Madame, once again I feel like a male Cassandra. It still makes me angry, to a degree, but mostly just incredibly sad.
I know, Mrhounddog. Me, too :-(
I'll be honest and say I haven't fully understood what you did.
However I'd like to point out that any excess mortality measure fails to capture short-term changes if it uses pre-COVID data. People were dying of all kinds of things in the COVID-era and if I want to know the impact of vaccinations, all these paths water down the relationship between vaccinations and deaths.
The best success I've had is simply using an individual reference timeframe where all the factors impacting mortality I do not care about are already present.
For Q3/2021 in the USA this is June/2021, where mortality is at a COVID-era low. When I do that and run regressions between vaccination rates and doses, I get really impressive results.
Fair enough. If all you're interested in is vaccines. Even so, your baseline will be polluted all the same. As long as you are transparent with your method, people that use the results make the necessary adjustments or caveats as they see fit.
Yeah, right now all I am interested in is vaccinations. I implicitely alleged that you are, too, which is obviously not the case.
And yes, the baseline is polluted, but that's the idea when I want to know the impact of vaccination rates being cranked up. And ofc, always 100% transparent. It has never been my intention to mislead and part of why I barely publish anything anymore is because I'm busy invalidating everything I do. :D
For this study in particular, I am trying to remain as agnostic as possible, concentrating on presenting irrefutable fact, free from any bias.
I use a similar approach, comparing the mortality rate to a time when it is at its minimum then looking at cause of death data to analyze what the excess is due to.
Looking at the ONS data for the UNVACCINATED in Table 1, your reference timeframe shows ASMR of 2088 in Apr 2021 and 1721 in May 2021. Your "death wave in July" is 1688 per 100K person-years.
The reference value that I use is the low value in September 2022 of 1078.
How are you able to explain this data for the unvaccinated?
You can also look at the unvaccinated 18-39 age group in Table 2 and see that the pattern is a slightly different. The ASMR is low in April and May but was elevate in July 2021 and didn't return to this "normal" level until Sept 2022.
How do you explain this?
I was actually gonna try your approach already.
With the July/August wave in the US it's easy, all states are bottoming out at the same time (June), but if it weren't the case, I thought using an average of the "lowest x weeks" would work.
Can you explain how exactly you are doing it?
I'm taking a similar approach as Smalley except that I don't worry about convexity. The effect of convexity is insignificant when looking at the timeframe of a single wave. The pattern of peaks is the same no matter how much effort is taken to establish the baseline.
What matters most about the peaks is that they rise exponentially. People should be fitting curves of the form a + b * exp(r *t) to regions like Jun 1 2021 to Aug 31 2021 and seeing the close fit. Contagious diseases spread exponentially; other mechanisms that have been suggested do not.
Yeah sorry about that. I wasn't familiar with the data before and just briefly glanced at OWID.
I must've misread the chart and just realized it.
I've got all the data in my db now and it also looks like the effect that I mentioned is not present in the UK.
In the US there was an inversion of the correlation between vaccination rates and progress during Delta (cumulative doses p.c. and doses p.c.). Something about vaccinating the least vaccinated regions is highly dangerous I guess.
It can be observed on EU-level in October (particularly RO, BG and LV), but the waves are all asynchronous and the dynamics between vaccinations and waves are very complex. Vaccinating into "growing case rates" can fuel a wave much better than vaccinating into "declining case rates", due to the number of susceptibles decreasing. At least this is how it CAN be modelled.
Imho there is no other explanation besides infection enhancement (occurring in around the time of vaccination if exposed) for the delta waves that makes sense. Delta was at 100% relative prevalence almost everywhere by the end of July, but caused outbreaks much, much later in some places.
Somehow cumulative doses are hiding the effect of doses, possibly some rolling pull-forward effect. Natural immunity through infection enhancement in highly vaccinated regions.
I have a feeling that you are laboring under some presumptions that have been pushed in this substack.
It is true that Delta became predominant in the UK in May 2021 and the variant's prevalence remained low until July. It was later in the USA, not becoming predominant until July with the outbreak arriving in August. This is not unexpected for outbreaks. It takes time for a contagion to spread.
If you look at the "Reproduction rate" metric in OWID, it is easy to see the timing and extents of outbreaks (R > 1).
Are you saying that if there is a vaccination program while cases are rising, cases continue to rise and if while cases are falling, cases continue to fall? Isn't that the same thing as saying that there is no immediate impact of vaccinations, one way or the other?
No. In a SIV model where freshly vaccinated individuals (e.g. in the first week) have a higher ß than the remaining infected population, an increase in size of the pool of freshly vaccinated individuals will have the biggest impact early on.
If you vaccinate in the phase of exponential growth where susceptibles are near 100%, you'll see the wave become much steeper and peak earlier. The effect on timing would ofc be mitigated by cross-border travel, causing the pathogen to be seeded everywhere.
Depending on the beta of the remaining infected population there might never be a wave at all without freshly vaccinated individuals.
This type of model fits what I am seeing in the data. In the US data there is strong correlation between cases and doses towards the end of the exponential growth of the case rate (from Alpha through BA.2).
During delta this correlation extends to deaths (ACM, but particularly U07.1 deaths, even stronger in UCOD=U07.1 deaths).
If this is actually an effect of the vaccines, it might be hidden behind a data collection bias in early 2021. Trial data would suggest this effect existed in 2020 variants as well. Alas wastewater data is only available for a handfull of states in early 2021. It only starts rising to 49 states in late 2022. Positive PCR tests and cases are subject to bias and unreliable for other reasons.
Any effect of "doses" is also an effect of cumulative doses. So if vaccinations promote cases, this effect will have the strongest impact on highly vaccinated regions, causing them to have higher levels of natural immunity, creating the illusion of vaccine immunity.
There is no data on individuals who have an early infection after their dose. I can not find one account of amplification cycles of such individuals.
VAERS strongly supports this notion, with the number of infections reported to VAERS decreasing to ~10% after a week (compared to inoculation day).
I know this sounds like the gibberish of a maniac, but I've been sitting over the data for a year and it's the only explanation I can come up with for what I am seeing.
This a little long to respond to in someone else's article. Is there an article in your own blog where you lay this out in detail?
Working on it, but I won't get into detail with any infectious disease models. They can do whatever you want them to do really.
Hi Fabian. Have you seen this over at bitchute? Pretty amazing to me.
SEVEN DAY CYCLE OF DEATH
https://www.bitchute.com/video/7Z6aJ0add9SH/
I stared at CDC Wonder data, worldometer and CDC data by weekday for 30 minutes just now (cases, doses, deaths) for Jan-Jun and Jul-Aug 2021) and I don't think there's much to go on.
I think you may be right. There was a commenter on the video over at bitchute that commented that all this video illustrates is that the more people in the sample then the more deaths in the sample. The rates of death are consistent over large numbers of people so would expect more deaths with a large sample and fewer deaths in a smaller sample. I saw something that was not there.
I know the feeling :)
Hadn't seen it.
Someone has sabotaged the sound :-(
Frank I just refreshed my browser and I get sound. Double check that your little speaker icons are not crossed out. I sometimes think something is fishy but I see that the speaker is set to mute. Try to mute unmute your speaker.
I see the sound icon on my screen, that has an X through it. When I press unmute nothing happens and I still have no sound. When I access Youtube I still have sound.
Well I suppose stranger things have happened.
Look up CRAIG-PAARDEKOOPER bitchute. Interesting studies.
Yes, I listened to the narrator this morning (I fixed the problem I had with the sound by just moving the sound control to the right) I must be getting paranoid :-) Now I can send the Craig / Bitchute onwards. Craig is a genius when it comes to statistics and he has such a pleasant accent from somewhere in southern England I guess.
So...
I was wondering how many months extra aging is each jab producing in terms on Gompertz risk?
If you were selling annuities you could make a financial killing by killing people off slightly earlier.
When I get the full set of data going back to WWII, I will be able to work all this out.
According to the excess death statistics, life expectancy has decreased by an average of two years.
So 6 months or so life expectancy lost each jab...
then they booster the gullible every 6 months...
Fool me once, shame on you.
Fool me twice, shame on me.
You can fool all the people some of the time, and some of the people all the time, but you cannot fool all the people all the time.
Gosh but I have *absolutely* no idea what you have been doing. I can honestly say I don't understand anything! How embarrassing to have to admit this when I see all your commentators chatting away about things I totally don't understand! I think you are looking at deaths by year of birth but I'm not sure quite why - does it matter when you were born if you die without having been ill but only from having a novel injection? Surely all the people who were healthy, took the jabs and subequently died were killed by the only new thing in their lives?
Anyway, keep up the good work - just remember to stick a little idiot's explanation at the bottom for me!
Very simply because the cohort sizes are different every year. If there are twice as many births one year compared to the previous, when you look at deaths by age, you will inevitably have twice as many one year after the next even with everything else remaining the same. You will naively assume that the death rate doubled! even those who try to make population adjustments will likely fail. Look at the estimates - they vary nothing like the births did back in the day.
So, if, say, 1960 was a bumper baby year you would expect more people to die around 80 years later than on average? Bit like working out how many classrooms you will need in 1965 to accommodate all the extra 5 year olds. But surely people die at all sorts of ages, even in normal times, whereas all babies generally end up in school at aged 5. (I was so bad at maths I had to take an arithmetic O level and barely scrapped through that!).
Yes, of course, the rate at which they die will also have an impact which is why I model just that... Age-stratified analyses will always fail.
Not necessarily if you adjust for number of deaths per capita.
A lot of mathematical sophistry is going on here.
If an age group was dying at a rate of about 550 per day in the days before Covid and the rate goes up to over 1100 a few months later, there is no great need to worry about an exact estimate of the population change. We know that the population didn't change that much in a few months. It's just a distraction.
We shouldn't be interested in comparing one year to another since the excess deaths occur in short periods within the year. We can compare short periods like the low period in 2021 (Apr - Jun) to the high period (Nov-Dec-Jan) without worrying that the population has changed in 7 months.
Yes, there are usually more virus deaths in winter than summer, probably influenced by levels of vitamin C in the blood stream. That should translate in differences to the death rate in the northern and southern hemispheres, where the seasons are reversed.
It is really quite a simple matter when doing a statistical analysis, to check the number of deaths per capita.
I keep track by noting death and disability of friends and family. They are almost all dead or extremely ill. So then I can watch the suffering and death of unknown fellow men. It does not appear things are going to improve.
I followed EUROMOMO during the scamdemic and The numbers were always interesting to what they were saying and the reality of mortality. I have read a bit about the % that died in hospitals is what is the deviation from the norm was most apparent. Indicating that covid wasn't the cause.
I used to use Euromomo too, back in the day. But, like most things, they adopt much the same methodologies as everyone else, which are full of fundamental flaws as I have tried to articulate through this mini series!
Okay, after reading all articles, I finally understood.
Outstanding, Joel!
I'd like to see it side-by-side with other methods. :)
Hi Joel, Thanks for another set of analysis.
FYI I just heard that there will be a debate in Parliament on excess deaths on 20 October. We might even have more than 5 MPs in attendance.
h ttps://twitter.com/ABridgen/status/1707385764240134343
Interesting that you picked my age 88 (1935). Your chart suggests that Covid itself was responsible for the peak deaths in May 2020. Presumably that would have been during lockdown.
March 2021 must have been following the first two jabs, making vaccine the main culprit?
COVID was a bystander to mistreated pneumonia. It didn't kill hardly anyone.
But the ventilators did kill people deliberately.
https://youtu.be/UIDsKdeFOmQ
Undercover Epicenter Nurse blows the lid off the COVID-19 pandemic. What would you do if you discovered that the media and the government were lying to us .
Do you happen to have a first dose timeseries with infranational resolution for the UK?
Would be really nice if you could send me@pervaers.com one along with COVID deaths or all-cause deaths per 100k, so I can show you what I'm talking about.
Only if you have one available.
Yes, you can get it from here - https://coronavirus.data.gov.uk/details/download.
Would you be so kind to also tell me where I can get population estimates for those Upper Tier areas?
EDIT: Never mind, got it
Awesome thank you
They often include their population numbers, together with their rates. Glad you already found it!
Nice work Joel and interesting use of Gompertz. Wanted to add that I am obtaining a very similar excess mortality curve using a single XGBoost model on stacked data with three continuous predictors: Year of Birth, Month, and 3-Year Lag. The results appear robust. Happy to collaborate as needed.
GREECE. TOTAL NUMBER OF EXCESS DEATHS AND TIMING OF VACCINATION ROLL OUT https://www.facebook.com/photo/?fbid=1036984280790222&set=a.129822118173114&mibextid=CHbtyg