Thank you. Yes it's a lot of work post-processing all these charts manually, but I've become faster since I started. This is only a small fraction of the charts I made yesterday. :)
When I have to process a large number of charts I just draw svg's. Lots of coding in that case, but definitely saves time. In this case I was torn.
I follow the Covid information and data like it’s my job. 😉 So I have reviewed a ton of posts like this. For me this is 💯 the best and most clear illustration of how deaths soared after the shots were implemented. Thank you.
There is a lot of talk about changing ACM baselines with the dry-tinder/pull forward effect of the old dying off. What effect, if any, do you think that plays with the post-vax bias of the young suffering from Covid?
Imho the only half-way useful measurement for mortality as of now is deaths per population.
However this too suffers from the culling effect. I would say it plays a huge role in all age groups.
Most of the dying is done by a tiny percentage of the people. When those most susceptible to adverse health events die, you are just bound to see lower mortality.
The only way I see that we could solve this is by correcting for this with complex models.
Until then, deaths/excess deaths per 100k will have to do.
Have you looked at my excess mortality by age article? I am using my own weekly population estimates for that and am getting more plausible results than with my former method (correcting for linear trend).
Yes. The unvaxed seem to get the short end of the data stick due to the fact that the denominator can be manipulated, abused or incorrectly inferred. Some stacks:
Hopefully I can soon find time to get this forum going, where we can exchange data sources, code, findings etc. (https://forum.pervaers.com - still empty).
I really need to get into R and SAS and Python and whatnot, but as of now I'm writing all my code in Javascript. Embarassing lol. It's a bit of a pain because there aren't any libraries for my purposes, so I just do all the calculations myself.
I output to CSV, then create charts with LibreOffice and post-process with Photoshop.
If I need to create a lot of charts I write svg's.
You can have all my code, but I doubt it's gonna be very useful. I have like 2200 lines of uncommented code in the CHD project.
Indeed, that could be a good idea - to learn abit of R (it's better than Python and anything else for data vis tasks that we do) and then coordinate and unite some of our efforts, including yours and those by usmortality (mortality.watch) folks.
(for the earlier version open-canada deaths tracker). These codes are ready to run - Just load, and run (you can also replace canadian data with your US data as you wish)
We aim at creating a generic set of R functions that all of us could use - with Canadian, US or any other data. - as we already do at www.OpenCanada.info tracker, that read multiple (Canadian so far) data with the same functions.
If you need help to move on with your R or would like to coordinate the efforts, please email to opencanada.info@protonmail.com. In Canada, we all need to rename anonymous....
Thanks again - excellent data analysis and Substack articles!
Any thoughts why the 10 to 14-year-olds have the lowest absolute numbers?
The older cohorts death through time distributions are curious. What leaps to mind for me is that the really weak and frail died early on, leaving fewer week and frail to die later on.
I don't have an explanation for the distribution in people younger than 24.
Doesn't code U071 have the problem of someone being labeled as having died of COVID vs someone having died with COVID?
Yes, the culling effect is a big factor for those highly susceptible, namely the 85+ cohort. Without it, the 85+ cohort might have seen higher COVID mortality in 2021, but they were much healthier at the start of 2021 than they were at the start of 2020.
It may have that problem, yes. We shouldn't be too concerned with this though.
I am writing a long article on this subject and will try to illuminate what is happening there.
The explanation for the younger cohorts:
Delta affects younger people. That's the official narrative and it seems to be true. Increases in cases/proportions by age do not correlate with vaccination of the respective cohorts, but with Delta gaining foothold.
Immune imprinting ("vaccinations") caused Delta to gain predominance. I can't stress this enough. Delta existed all throughout 2020, but vaccines allowed it to gain predominance.
By early 2021 we had reached herd immunity. However vaccines had created a "vaccination gap" (I'll just reinterpret this term here), allowing ariants adapted to this gap to spread much faster than they would without mass imprinting, namely Mu and Delta.
Delta was seeded with the initial campaign. Once it started becoming predominant (cases still very low), people were allowed to travel again if they got the vaccine. This caused another smaller first-dose vaccination wave in Q3 2021 which gave cases and COVID deaths a huge boost (another reinterpretation of neo-fascist biosec vocab).
Either there were bad batches going around at the time (30% of Q3 2021 COVID deaths reports are attributed to 2 pairs of consecutive batch ID's) or any given batch had the power to "boost" COVID cases sky-high. I will demonstrate this in the long article on this conundrum.
Nice try. I still think there is no real accurate way to determine who has or had covid...the PCR test certainly leaves much to be desired. And how can you trust death certificates? I would expect at least 50% to be fudged for the purpose of pushing the fear agenda and monetary gain from the government.
I have done the same with SARS and various other medical concepts related to severe COVID, regardless of whether or not the death certificate contains the diagnostic code and the proportions are nearly the same. Absolute numbers do differ, but they develope in a very similar manner over time.
Ultimately this is about people dying of respiratory disease long before reaching the age at which people typically die of respiratory disease.
I will tackle this in my next long article.
You'll have to elaborate on the "nice try". What did I try and how did I fail?
Thank yuo for posting this Fabian.
Lot of hard work & time into getting the numbers graphed.
Horrendous facts emerging all the time.
Thank you. Yes it's a lot of work post-processing all these charts manually, but I've become faster since I started. This is only a small fraction of the charts I made yesterday. :)
When I have to process a large number of charts I just draw svg's. Lots of coding in that case, but definitely saves time. In this case I was torn.
I follow the Covid information and data like it’s my job. 😉 So I have reviewed a ton of posts like this. For me this is 💯 the best and most clear illustration of how deaths soared after the shots were implemented. Thank you.
Oh wow, thank you. :)
Starting to feel all warm and fuzzy. I hope you'll like my next article, gonna be a big one. :)
There is a lot of talk about changing ACM baselines with the dry-tinder/pull forward effect of the old dying off. What effect, if any, do you think that plays with the post-vax bias of the young suffering from Covid?
Imho the only half-way useful measurement for mortality as of now is deaths per population.
However this too suffers from the culling effect. I would say it plays a huge role in all age groups.
Most of the dying is done by a tiny percentage of the people. When those most susceptible to adverse health events die, you are just bound to see lower mortality.
The only way I see that we could solve this is by correcting for this with complex models.
Until then, deaths/excess deaths per 100k will have to do.
It's a culling.
Yeah. The youth stats are tough to use sometimes, anyway, because of their low ACM in general, much less something that is in the .00 range for CFR.
Great work on this data set tho.
Thank you.
Have you looked at my excess mortality by age article? I am using my own weekly population estimates for that and am getting more plausible results than with my former method (correcting for linear trend).
https://vigilance.pervaers.com/p/usa-preview-age-stratified-excess
Yes. I think this is your way of getting around #denominatorgate right?
I just had an idea how we could correct for the culling effect! I'm gonna have to put it in the queue though.
I might be able to use certain diagnostic keys on death certificates to identify the extent of "health improvement" within each age group.
Don't know which ones I'll use, but I'm pretty optimistic this could work. Sometimes you just gotta talk about stuff.
Nice. Look forward to it!
I've never heard of that. Searched for it, something about vaccine efficacy bias. Care to explain?
I just used to method in order to get a better idea of what is going on.
Yes. The unvaxed seem to get the short end of the data stick due to the fact that the denominator can be manipulated, abused or incorrectly inferred. Some stacks:
https://open.substack.com/pub/hold2/p/denominatorgate?r=kutjh&utm_medium=ios&utm_campaign=post
https://open.substack.com/pub/boriquagato/p/the-new-uk-ons-data-is-out-and-its?r=kutjh&utm_medium=ios&utm_campaign=post
https://open.substack.com/pub/boriquagato/p/if-you-dont-like-the-data-stop-reporting?r=kutjh&utm_medium=ios&utm_campaign=post
https://open.substack.com/pub/drclarecraig/p/how-many-ghosts-die-with-covid?r=kutjh&utm_medium=ios&utm_campaign=post
Why are there 2 CHARTS for the 10-14 YR old age group?
Am I missing something?
Great Work.
No you are not, you're great, thank you! :)
Fixed.
Great charts. Horrible and stunningly clear.
Wondering if "Monthly Number of Deaths" second graph of 55-64 is meant to be 65-74?
Thank you. Both for the compliment and for pointing out my mistake.
What would I do without readers like you?
Great Visualizations! Go you have r codes for it? Would love to the same ones for our Canadian data -
https://opencanada.substack.com/p/covid-deaths-by-vaccination-status
Hopefully I can soon find time to get this forum going, where we can exchange data sources, code, findings etc. (https://forum.pervaers.com - still empty).
I really need to get into R and SAS and Python and whatnot, but as of now I'm writing all my code in Javascript. Embarassing lol. It's a bit of a pain because there aren't any libraries for my purposes, so I just do all the calculations myself.
I output to CSV, then create charts with LibreOffice and post-process with Photoshop.
If I need to create a lot of charts I write svg's.
You can have all my code, but I doubt it's gonna be very useful. I have like 2200 lines of uncommented code in the CHD project.
Indeed, that could be a good idea - to learn abit of R (it's better than Python and anything else for data vis tasks that we do) and then coordinate and unite some of our efforts, including yours and those by usmortality (mortality.watch) folks.
They have a bunch of R codes at https://github.com/USMortality/charts. And We have some here: https://github.com/open-canada/vitals (in R folder)
(for the earlier version open-canada deaths tracker). These codes are ready to run - Just load, and run (you can also replace canadian data with your US data as you wish)
And we'll be putting more codes on our new github repo: https://github.com/opencanada-info/R.
We aim at creating a generic set of R functions that all of us could use - with Canadian, US or any other data. - as we already do at www.OpenCanada.info tracker, that read multiple (Canadian so far) data with the same functions.
If you need help to move on with your R or would like to coordinate the efforts, please email to opencanada.info@protonmail.com. In Canada, we all need to rename anonymous....
Thanks again - excellent data analysis and Substack articles!
Very nice projects. I'm looking forward to cooperate, but I won't get into R before I'm done with the US project.
I've written some Python, a lot of C and some other script languages, so R shouldn't be too difficult to learn either.
Interesting... delta or mistreatment? As several doctors say they treated seven thousand plus patients with no hospitalisation?
Are the two mutually exclusive? :)
Any thoughts why the 10 to 14-year-olds have the lowest absolute numbers?
The older cohorts death through time distributions are curious. What leaps to mind for me is that the really weak and frail died early on, leaving fewer week and frail to die later on.
I don't have an explanation for the distribution in people younger than 24.
Doesn't code U071 have the problem of someone being labeled as having died of COVID vs someone having died with COVID?
Yes, the culling effect is a big factor for those highly susceptible, namely the 85+ cohort. Without it, the 85+ cohort might have seen higher COVID mortality in 2021, but they were much healthier at the start of 2021 than they were at the start of 2020.
It may have that problem, yes. We shouldn't be too concerned with this though.
I am writing a long article on this subject and will try to illuminate what is happening there.
The explanation for the younger cohorts:
Delta affects younger people. That's the official narrative and it seems to be true. Increases in cases/proportions by age do not correlate with vaccination of the respective cohorts, but with Delta gaining foothold.
Immune imprinting ("vaccinations") caused Delta to gain predominance. I can't stress this enough. Delta existed all throughout 2020, but vaccines allowed it to gain predominance.
By early 2021 we had reached herd immunity. However vaccines had created a "vaccination gap" (I'll just reinterpret this term here), allowing ariants adapted to this gap to spread much faster than they would without mass imprinting, namely Mu and Delta.
Delta was seeded with the initial campaign. Once it started becoming predominant (cases still very low), people were allowed to travel again if they got the vaccine. This caused another smaller first-dose vaccination wave in Q3 2021 which gave cases and COVID deaths a huge boost (another reinterpretation of neo-fascist biosec vocab).
Either there were bad batches going around at the time (30% of Q3 2021 COVID deaths reports are attributed to 2 pairs of consecutive batch ID's) or any given batch had the power to "boost" COVID cases sky-high. I will demonstrate this in the long article on this conundrum.
Explains a lot. Is there additional data you would like to have beyond what you have now?
Yes absolutely.
1) I am desperate for sequencing data from GISAID, who are not approving my registration.
2) New disability claims
Nice try. I still think there is no real accurate way to determine who has or had covid...the PCR test certainly leaves much to be desired. And how can you trust death certificates? I would expect at least 50% to be fudged for the purpose of pushing the fear agenda and monetary gain from the government.
A PCR test is not necessary to diagnose COVID-19. In fact, no laboratory test was necessary to diagnose COVID-19.
Look at the first picture in this article: https://vigilance.pervaers.com/p/usa-preview-age-stratified-excess.
I have done the same with SARS and various other medical concepts related to severe COVID, regardless of whether or not the death certificate contains the diagnostic code and the proportions are nearly the same. Absolute numbers do differ, but they develope in a very similar manner over time.
Ultimately this is about people dying of respiratory disease long before reaching the age at which people typically die of respiratory disease.
I will tackle this in my next long article.
You'll have to elaborate on the "nice try". What did I try and how did I fail?