Thank you, Fabian, for your sustained effort on these three articles. Let me see if my German friend with a PhD in Chemistry, will be moved to reconsider his approval of the injections which has continued even to now.
Question: who—what kinds of persons in Germany (All levels of government? What professions?)--are you directing these articles to in hopes, I guess, of persuading them to seriously investigate the safety of these injections? Have you sent your articles to any of these potential influencers? If so, any responses?
This latest article is super interesting as are your previous ones. You state: "It is highly unlikely that less than 100,000 Germans were killed as a direct consequence of receiving these drugs." It seems like this 100,000 deaths would be noticed by more people. I am not familiar with Germany's pandemic outcomes: what percentage does the 100,000 represent of the total deaths? Is this a tiny percentage, and so not noticeable?
Because I am not familiar with the discussions of the German data, who else is reporting similar finding to yours? If no one else is, why do you think you are the only one who is? And, from a different perspective, why are there so many analyses that would contradict what you are showing?
I mentioned in one my posts at the end one of your previous posts that the charts you presented looked too perfect. How do you know that you are not overfitting the data cleanup?
I have a question. How did you conduct the correlation between the new vaccinations and non-covid excess? did you run the correlation on trending time-series data? My understanding is that when you run a correlation (or a regression) on time-series datasets that are both trending in the same direction then this will produce a strong correlation coeficient regardless of an effect.
I'm currently conducting a similar analysis for the whole of the EU, however, I've filtered out all the non-mRNA doses from the vaccination datasets for all countries. In order to avoid running a correlation on time-series data and in order to have an in-built control mechanism I have decided to instead run the correlation on vaccination/mortality data across all countries for each week. It is my understanding that this should serve as an element of self-control since as long as the vaccination rates and mortality don't rise in all countries at the same time, which they don't. When i ran this process for the all-cause mortality the results for the whole of 2021-2022 seem very similar to your results for germany.
I have yet to compile a non-covid excess mortality dataset for all countries and i'm afraid it will be really tedious work, could you provide me with any tips as to how to do this efficiently?
Jan 30, 2023·edited Jan 30, 2023Liked by Fabian Spieker
Thank you for this high quality analysis.
Just some thoughts on factors which may be combining to increase ACM.
Five (preliminary) major contributors : 1. Acute toxicity from mRNA vaccines, 2. Deterioration of vaccine recipient immune system (all be it following a brief period of protection) specifically in relation to Covid 19, 3. Deterioration of vaccine recipient immune system in relation to general threats - cancer etc, 4. Accumulation of spike protein following each additional dose, and infection, 5. Cumulative damage accruing from each additional dose, or infection - eg damage to the myocardium until a critical level is reached causing a severe adverse event.
All of these play out over different time lines, and will therefore, not be likely to correlate, or at least, weaken the correlation with shots.
Intuitively, what we would expect to see is a diminishing correlation with any one particular factor over time, despite a growing rate of increasing ACM.
To my untrained eye, the answer to ascertaining why this is occurring (is it the 'vaccines') will lie in the analysis of ACM in vaccinees v unvaccinated.
I am of the view the aggressive attempt to obliterate any control group, first in the trial participants, then more broadly, is to eliminate this avenue of causal confirmation.
In the interim, other suggestive correlations, such as number of shots to deaths, can be strategically obfuscated (it's just Covid, lack of health care etc), thereby throwing the masses off the scent.
I really appreciate you work. Here i wonder why non COVID excess death is negative during your study period. Could it be that some death were counted as COVID whereas it was not really COVID? So the series are not indépendant?
1. You have chosen, as your calibration period, basically the period that stretches from the minimum of excess mortality to zero excess mortality. Since I would expect excess mortality to revert to zero anyway, what would the curve have looked like without vaccination?
2. If you apply your model to the period after your calibration period, what does the "non-Covid and non-vaccine excess deaths" curve look like?
Very interesting. So the trends align extremely well for initial vaccinations, right? Q1, Q2 2021
But then, they diverge and do not seem to follow each other any more. For example in Q4 2021-Q1 2022. Or in December 2022.
Right?
Thank you, Fabian, for your sustained effort on these three articles. Let me see if my German friend with a PhD in Chemistry, will be moved to reconsider his approval of the injections which has continued even to now.
Question: who—what kinds of persons in Germany (All levels of government? What professions?)--are you directing these articles to in hopes, I guess, of persuading them to seriously investigate the safety of these injections? Have you sent your articles to any of these potential influencers? If so, any responses?
Will let you know my German friend’s response.
Stay safe and free.
Great article and I mentioned you here:
https://igorchudov.substack.com/p/covid-vaccines-killed-278000-americans
This latest article is super interesting as are your previous ones. You state: "It is highly unlikely that less than 100,000 Germans were killed as a direct consequence of receiving these drugs." It seems like this 100,000 deaths would be noticed by more people. I am not familiar with Germany's pandemic outcomes: what percentage does the 100,000 represent of the total deaths? Is this a tiny percentage, and so not noticeable?
Because I am not familiar with the discussions of the German data, who else is reporting similar finding to yours? If no one else is, why do you think you are the only one who is? And, from a different perspective, why are there so many analyses that would contradict what you are showing?
I mentioned in one my posts at the end one of your previous posts that the charts you presented looked too perfect. How do you know that you are not overfitting the data cleanup?
I have a question. How did you conduct the correlation between the new vaccinations and non-covid excess? did you run the correlation on trending time-series data? My understanding is that when you run a correlation (or a regression) on time-series datasets that are both trending in the same direction then this will produce a strong correlation coeficient regardless of an effect.
I'm currently conducting a similar analysis for the whole of the EU, however, I've filtered out all the non-mRNA doses from the vaccination datasets for all countries. In order to avoid running a correlation on time-series data and in order to have an in-built control mechanism I have decided to instead run the correlation on vaccination/mortality data across all countries for each week. It is my understanding that this should serve as an element of self-control since as long as the vaccination rates and mortality don't rise in all countries at the same time, which they don't. When i ran this process for the all-cause mortality the results for the whole of 2021-2022 seem very similar to your results for germany.
I have yet to compile a non-covid excess mortality dataset for all countries and i'm afraid it will be really tedious work, could you provide me with any tips as to how to do this efficiently?
Brilliant work Fabian. I just wish I could click my fingers and have it replicated for New Zealand. I'll give it a go.
Thank you for this high quality analysis.
Just some thoughts on factors which may be combining to increase ACM.
Five (preliminary) major contributors : 1. Acute toxicity from mRNA vaccines, 2. Deterioration of vaccine recipient immune system (all be it following a brief period of protection) specifically in relation to Covid 19, 3. Deterioration of vaccine recipient immune system in relation to general threats - cancer etc, 4. Accumulation of spike protein following each additional dose, and infection, 5. Cumulative damage accruing from each additional dose, or infection - eg damage to the myocardium until a critical level is reached causing a severe adverse event.
All of these play out over different time lines, and will therefore, not be likely to correlate, or at least, weaken the correlation with shots.
Intuitively, what we would expect to see is a diminishing correlation with any one particular factor over time, despite a growing rate of increasing ACM.
To my untrained eye, the answer to ascertaining why this is occurring (is it the 'vaccines') will lie in the analysis of ACM in vaccinees v unvaccinated.
I am of the view the aggressive attempt to obliterate any control group, first in the trial participants, then more broadly, is to eliminate this avenue of causal confirmation.
In the interim, other suggestive correlations, such as number of shots to deaths, can be strategically obfuscated (it's just Covid, lack of health care etc), thereby throwing the masses off the scent.
Cheers.
How did you validate you model?
Shared on Gettr
I really appreciate you work. Here i wonder why non COVID excess death is negative during your study period. Could it be that some death were counted as COVID whereas it was not really COVID? So the series are not indépendant?
Thanks you for your work!
Two questions (I like counterfactual thinking):
1. You have chosen, as your calibration period, basically the period that stretches from the minimum of excess mortality to zero excess mortality. Since I would expect excess mortality to revert to zero anyway, what would the curve have looked like without vaccination?
2. If you apply your model to the period after your calibration period, what does the "non-Covid and non-vaccine excess deaths" curve look like?
We observe similar correlation between excess deaths and boosters in Canada -
https://opencanada.substack.com/
https://opencanada.substack.com/p/high-correlation-between-excess-deaths
And we also code in R...
Please reach us at OpenCanada.info@protonmail.com for some technical questions. Thank you