What i do see are many vaxxed people just shrugged their shoulders non-chalantly, because they can only and will only relate to themselves not having problems or side effects from the jab and in the process had to accept their own safety as scientific proof that this illness does not exist. Assuming their body not being affected as the one and only sign of pure science. Almost as if the human empathy and sympathy just vanished into thin air. Many are just look the other way.
I have a close friend in the crematoria world. He threatened to stop cremations because the death certificates were being filled out by anyone in the nursing homes while the doctors hid under their desks. Many death certificates are meaningless.
Even death certificates filled in by doctors in hospitals can be meaningless. My godmother tested negative for covid while in hospital. Despite this, they decided to “treat her as a covid patient” which in effect meant very little treatment for a frail 88 yr old. Her death cert states Covid19 as cause of death.
Just had this thought that I’ll share in comments on all the SUBSTACKS I subscribe to…
Howzabout a daytime rally of the jab-injured in baseball/football stadiums across the nation. There are enough ambulatory jab-injured in the various cities to fill the stadiums, and they need visibility and a voice. A millionaire or 2 (perhaps jab-injured themselves) would be needed for the rentals, or perhaps a crowd-sourcing campaign.
I think you made it much more complex than it really is. Your mathematical approach though very simple can be simplified using all cause mortality on a yearly basis. This eliminates the sinusoidal wave of months and averaging multiple years also straightens the graph.
We can then see that all cause mortality across the whole as showing a substantial increase from 2020 to now versus 2017-2019.
Yes it would be great to see actual deaths, ages etc and compare them to when the vax was rolled out. But the powers that be don’t want laymen like us to see the correlation.
In order to calculate the seasonality-adjusted trend, I first take a 15-day moving average of the daily data. I then do linear regression to fit a line to the data for 2015-2019. Then for each 366 days of the year, I calculate the difference from the linear trend during the prediction interval in 2015-2019, and I then add the difference to the linear trend for the corresponding day of each subsequent year. So for example in the age group 30-39 on September 26th, when using the 15-day moving average, the average number of deaths in 2015-2019 was about 0.54 higher than the linear trend, so I add about 0.54 to the linear trend on September 26th for each subsequent year.
I think my method produces a more accurate estimate of seasonal variation in mortality than the method of fitting a sine wave that you used earlier, because the waveform of seasonal variation in mortality doesn't look anything like a sine wave in most countries: https://i.ibb.co/wzsvzZT/1.png (R code: https://pastebin.com/raw/2psmRf40).
Thanks for detailing the methodology. I had trouble understanding the Step 5: "fit a polynomial to the cumulative data. Again, in the absence of cohort depletion, this curve is expected to be exponential if the weekly data fit is linear." Could you expand on this step?
Also question on whether you think it helps to also adjust for the population in each age band? Or that sort of comes out in the excess calculation anyway.
1. If the periodic data is linear, the cumulative data will be exponential. Therefore, some sort of exponential curve, e.g. a polynomial is more appropriate for the cumulative data. A simple 2-order polynomial may not be the best fit so I also try a 3-order and choose the one that produces the most consistent results with the periodic model.
2. Using year-of-birth data and trend implicitly captures population changes.
I'm bothered by the lack of mathematical precision in the language:
"Exponential" is what you get when there is compounding, like bank interest or population growth.
If the base data is linear, the cumulative data will be quadratic, a 2-order polynomial (Calculus 101).
Pandemic curves are generally described by a wave shape which is exponentially increasing at first, reverses and then exponentially decreasing as it dissipates. This pattern can be seen by using a logarithmic scale in which the start and end of the wave will be marked by linear sections. For example:
Another way of looking at the "Covid" data and avoiding some of the mathematical sophistry is to look for a relatively clean impulse response. I suggest South Korea National Day, August 2020 meets that criteria.
There is a built-in problem. The "pandemic" quickly dissapates once the Korean holiday is over. With that insight, oddities in other data appear.
You can't avoid the basic mathematics. If there is compounding, there will be exponential growth; if there isn't compounding, there won't be exponential growth. I'm not quibbling over the misuse of the word; how the mortality rate increases indicates the underlying process.
Because the more there are people who have a contagious disease, the faster the disease will spread. This causes exponential growth.
This is not true for the vaccination rate and a vaccination program cannot lead to exponential growth in deaths. Period. End of story.
Your first three steps provide a consistent view of the underlying data. After that, I believe that what we care about is the shape of the waves in the data ... when was the peak of the wave and what was the excess mortality during the wave. I think that plotting rolling averages of a period of a few weeks would locate the peaks and valleys.
Joel, can you take a look at excess deaths in Scottish infants <1yo before and after COVID vax ? (Vax start 'at risk' 6mon-4yo May 29th-2023) https://ibb.co/KmDTMD9
I calculate a very noteable 30% increase in mortality post jab.
What i do see are many vaxxed people just shrugged their shoulders non-chalantly, because they can only and will only relate to themselves not having problems or side effects from the jab and in the process had to accept their own safety as scientific proof that this illness does not exist. Assuming their body not being affected as the one and only sign of pure science. Almost as if the human empathy and sympathy just vanished into thin air. Many are just look the other way.
Every death certificate should be released
I have a close friend in the crematoria world. He threatened to stop cremations because the death certificates were being filled out by anyone in the nursing homes while the doctors hid under their desks. Many death certificates are meaningless.
Even death certificates filled in by doctors in hospitals can be meaningless. My godmother tested negative for covid while in hospital. Despite this, they decided to “treat her as a covid patient” which in effect meant very little treatment for a frail 88 yr old. Her death cert states Covid19 as cause of death.
Very true.
Just had this thought that I’ll share in comments on all the SUBSTACKS I subscribe to…
Howzabout a daytime rally of the jab-injured in baseball/football stadiums across the nation. There are enough ambulatory jab-injured in the various cities to fill the stadiums, and they need visibility and a voice. A millionaire or 2 (perhaps jab-injured themselves) would be needed for the rentals, or perhaps a crowd-sourcing campaign.
Joel
I think you made it much more complex than it really is. Your mathematical approach though very simple can be simplified using all cause mortality on a yearly basis. This eliminates the sinusoidal wave of months and averaging multiple years also straightens the graph.
We can then see that all cause mortality across the whole as showing a substantial increase from 2020 to now versus 2017-2019.
Yes it would be great to see actual deaths, ages etc and compare them to when the vax was rolled out. But the powers that be don’t want laymen like us to see the correlation.
Thanks for your reporting
I did indeed already do this! https://metatron.substack.com/p/mortality-outcomes-by-age-in-england
I posted R code here for calculating seasonality-adjusted excess mortality in the same dataset for daily deaths in England and Wales: https://mongol-fi.github.io/stat.html#Make_a_heatmap_of_weekly_excess_mortality_percentage_by_age_group.
In order to calculate the seasonality-adjusted trend, I first take a 15-day moving average of the daily data. I then do linear regression to fit a line to the data for 2015-2019. Then for each 366 days of the year, I calculate the difference from the linear trend during the prediction interval in 2015-2019, and I then add the difference to the linear trend for the corresponding day of each subsequent year. So for example in the age group 30-39 on September 26th, when using the 15-day moving average, the average number of deaths in 2015-2019 was about 0.54 higher than the linear trend, so I add about 0.54 to the linear trend on September 26th for each subsequent year.
I think my method produces a more accurate estimate of seasonal variation in mortality than the method of fitting a sine wave that you used earlier, because the waveform of seasonal variation in mortality doesn't look anything like a sine wave in most countries: https://i.ibb.co/wzsvzZT/1.png (R code: https://pastebin.com/raw/2psmRf40).
Figure 2 (Mar 20) - See, the choking bronchial spasms could have killed me; apparently did kill some.
Thanks for detailing the methodology. I had trouble understanding the Step 5: "fit a polynomial to the cumulative data. Again, in the absence of cohort depletion, this curve is expected to be exponential if the weekly data fit is linear." Could you expand on this step?
Also question on whether you think it helps to also adjust for the population in each age band? Or that sort of comes out in the excess calculation anyway.
And great insights on the age structure.
1. If the periodic data is linear, the cumulative data will be exponential. Therefore, some sort of exponential curve, e.g. a polynomial is more appropriate for the cumulative data. A simple 2-order polynomial may not be the best fit so I also try a 3-order and choose the one that produces the most consistent results with the periodic model.
2. Using year-of-birth data and trend implicitly captures population changes.
I'm bothered by the lack of mathematical precision in the language:
"Exponential" is what you get when there is compounding, like bank interest or population growth.
If the base data is linear, the cumulative data will be quadratic, a 2-order polynomial (Calculus 101).
Pandemic curves are generally described by a wave shape which is exponentially increasing at first, reverses and then exponentially decreasing as it dissipates. This pattern can be seen by using a logarithmic scale in which the start and end of the wave will be marked by linear sections. For example:
https://ourworldindata.org/explorers/coronavirus-data-explorer?yScale=log&time=2021-06-03..2021-08-03&facet=none&country=~GBR&Metric=Confirmed+deaths&Interval=7-day+rolling+average&Relative+to+Population=true&Color+by+test+positivity=false
Another way of looking at the "Covid" data and avoiding some of the mathematical sophistry is to look for a relatively clean impulse response. I suggest South Korea National Day, August 2020 meets that criteria.
There is a built-in problem. The "pandemic" quickly dissapates once the Korean holiday is over. With that insight, oddities in other data appear.
You can't avoid the basic mathematics. If there is compounding, there will be exponential growth; if there isn't compounding, there won't be exponential growth. I'm not quibbling over the misuse of the word; how the mortality rate increases indicates the underlying process.
Because the more there are people who have a contagious disease, the faster the disease will spread. This causes exponential growth.
This is not true for the vaccination rate and a vaccination program cannot lead to exponential growth in deaths. Period. End of story.
Your first three steps provide a consistent view of the underlying data. After that, I believe that what we care about is the shape of the waves in the data ... when was the peak of the wave and what was the excess mortality during the wave. I think that plotting rolling averages of a period of a few weeks would locate the peaks and valleys.
Joel, can you take a look at excess deaths in Scottish infants <1yo before and after COVID vax ? (Vax start 'at risk' 6mon-4yo May 29th-2023) https://ibb.co/KmDTMD9
I calculate a very noteable 30% increase in mortality post jab.
Can you graph this out ?
cheers
Where is the Scottish data? Is it by date of occurrence? Are there registration delays?
Just usual excess deaths data.
https://www.nrscotland.gov.uk/statistics-and-data/statistics/statistics-by-theme/vital-events/general-publications/weekly-deaths-registered-in-scotland