I replied earlier thinking you were referring to the December 2020 wave and wanted to remove that comment, but removed yours instead. Sorry about that.
Anyway, you are right, I assume the 2022 deaths are caused by sequesters from spike exposure. But those who are vaccinated become susceptible. The variants are adapted to their deceptively imprinted immune systems. So the vaccinated tend to get reinfected and have to keep vaccinating against new variants, because the virus keeps adapting to them. Otherwise they have no neutralizing antibodies at all anymore. Then there's the issue of immune tolerance as well. It's like they always say: "It helps against symptomatic disease"
The higher your spike dose and the higher the exposure time, the higher the chance of sequesters, adverse events, a net reduction in life expectancy. Pure, unadulterated evil.
It's disturbing indeed. My home state peaked at 42% in the last reporting week, but the steady uptrend we saw throughout the year is more worrying to me.
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?
Keep me updated on your friend. Every single person who stops taking these drugs is a victory the way I see it.
No influencers. I am really bad at selling or promoting anything. Since I plan to make this my job for the next few years at least, I am very grateful for any advice you can give me.
I hope these 3 articles from a fellow German on the devastation done by the injections to the German people will convince you the injections are not only not safe and effective but rather are overwhelmingly unsafe to the point of massive injuries and death and only effective as a depopulation tool which I believe there is much evidence for.
Addendum: my question above about who you might be targeting with your work, I gather you have not done that? So we can get together if you want and discuss this and other allied Covid1984 matters to help bring about a more free and peaceful world together. Email me: Jack at: responsiblyfree@protonmail.com
His estimate is 4.4% higher than mine (1713 doses per death).
I am very familiar with vaccine pharmacovigilance data. My estimate has always between 1000-5000, best guess 2000 shots per death. You could deduce that my new estimate is a classical case of confirmation bias and I could not prove you wrong.
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?
Oh and one person reported my findings from another article to multipolar-magazin.de (after liking my article a few weeks earlier), so I'm not the only one. That guy is sort of an influencer, but apparently isn't very creative, so he had to steal my work.
The thing is: I am not paid very well for doing this (less than 100 bucks per month as of now) and it takes a lot of time. It's a full-time job for me. There aren't many people who are willing to live the way I live now (without money) in order to give others a better idea of what happened. Many people used to work on it, but are now frustrated, because they received such harsh criticism from all sides. Many have just given up.
Well it's a little over 0.1% of the population. Nobody notices because these people are mostly old and frail. Many of them were in the last months of their life already.
Nobody else has done this as far as I know. The other month I correlated vaccination rates with excess mortality. Nobody had done that either.
Reasons I can think of:
- The data is hard to process for someone without any coding skills. The way it is presented makes things a bit difficult
- People don't expect to find anything worthwhile because everyone has access to the data
- Nobody will finance any studies of this kind, so it's just hobbyists
- People are too busy saying things that have been said a million times in order to get more clicks, likes, subscriptions, whatever. Processing data with due diligence takes time.
Around 1 million people are dying in Germany each year. Trend is upward, because the population is ageing.
1. Death rates are always increasing roughly exponentially with age. The younger age groups (say, 0-60) constitute around 70% of the population but only account for 8% of deaths. If something is killing people in this group, this will usually not be detectable by statistical methods but only by careful analysis (and reporting) of single cases.
2. The older age groups (say, 60+) account for almost all deaths, therefore statistical methods can be put to use. On the other hand, the general vulnerability of the elderly means that you rarely get actual excess deaths because, basically, causes of death are competing with each other. Put another way: humanity has gone a long way eradicating (or at least curtailing) many causes of death (the plague, tuberculosis, smallpox, whatever) so that we are now at a point where almost all deaths are either due to heart failure or cancer. Finding a cure for one disease leads to a short surge in life expectancy, until a new equilibrium has emerged. Even 50,000 excess deaths (that's my estimate for 2021 and 2022 combined) is compatible with 100,000 vaccine-related deaths.
How did you reach your estimate of 50k excess deaths? You are obviously using another method than I am using. Could you explain to me why you think that method is better suited to quantify excess mortality? What I am doing is more or less intuitive.
I try to stratify where it's feasible. It seemed a waste of time with those four weird age groups destatis offers for federal states. In the safety signals on my website I even adjusted the internal age structure of each age/gender group though.
Working on the mortality charts by age group. It's 45 age/gender groups and since I am planning to update these I'll probably automate the process by writing svg's. Not sure if I should dedicate the next article to those or to misrepresentation of the vaccination data.
I rarely watch videos at all, but I remember Marcel Barz. I watched nearly the whole video back then.
So what do you think of my method of correcting for demographic trend without age adjustment?
Since you want to compare to vaccination rates, and these are only available for broad age groups, your adjustment is the best one can do. I recommend (and I think I did this before) repeating the exercise after restricting all data to 60+.
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?
If you can supply me with time series of weekly or daily data for all cause mortality I'll create the excess mortality time series and some charts for you.
Your method of bringing the earlier years into demographic alignment with present years is intriguing. Can it all be done using Collabora Office?
Have you also seen the pattern in Germany where ACM can drop, in absolute terms after a bad previous year, due to severe influenza, for example? The simple models dont seem to factor this in.
OK. That's what I want hear and prove, that OWID are obfuscating the data. It is this data that the NZ government Muppet is using in the case where we are the plaintiffs and they are the respondents.
I've just about loaded all data points in an Excel spreadsheet.
I wrote you my email address in another reply. (me@pervaers.com)
OWID is also delaying release of vaccination data somehow. It's possible that the respective governments are delaying the release and OWID is just passing it on, but either way: This obfuscates the truth.
Really I think all we need to do is prove that the official data is not very useful. Privacy laws will have to be circumvented somehow and proper data will have to be released, ideally anonymized death certificates along with the respective digital id entries. First we need to prove that the official data doesn't suffice or that there is reason to believe it misrepresents reality.
There's an example of what OWID is doing in my Omicron article.
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 absolutely agree with what you said up there. I believe all these mechanisms do play a role.
I am tackling your 2nd point in article 4 of this series. The vaccines are not just affecting COVID specific immunity, this effect on immunity is also affecting the virus itself which in turn decreases the immunity of those who received a vaccine with delay.
Alas we simply do not get data stratified by vaccination status. This should be the main objective. In order to get it done we need to data that is with the companies issueing these digital ID's. It's usually NGO's. Bundesdruckerei in Germany.
There are three estimates of excess deaths in USA, two using surveys and one where I extrapolated Fabian's deaths-to-doses ratio to USA. All come close
We will never know the exact number of the deaths and it is not even possible to agree on the precise definition. However the fact that three completely different estimates came close to each other is extremely notable.
I guess it is. I'm just curious what it indicates. What can we learn, and how certain can we be that we're recognizing/interpreting the signal correctly.
That's why we need to try and figure out the degree of uncertainty, or error.
Mistakes are part of reaserch, but we can limit them by being aware of the assumptions we make, and their limitations. And any computational model will only be as reliable as the assumption it's based on, and the data used.
Well I haven't, but you will find out if I am wrong once I find out. Everybody is
wrong all the time, so I won't make a big deal out of it.
What's important is working towards a better understanding and staying honest while we do.
It's on the to-do list for sure. Any particular way you want me to validate it? The vague idea I am having is just looking at excess mortality data of those countries that issued their vaccination data on a daily basis, the same way I did for German federal states.
Keep in mind I am not a mathematician or data scientist, just a hobbyist looking to understand what is going on. I'll gladly expand my toolset anytime, so looking forward to hearing your suggestions.
I agree, everyone has a right to be wrong. I was wondering what efforts you made to reduce that chance.
I have no specific expertise in this kind of modeling, but I'm sure you could find some protocol. Method validation is usually conducted usiya separate, independent data set, that was analysed using another method.
I agree, honesty and transparency are important. And I'm curious if you readers understand how to interpret your results? Or if they even care?
I am not sure why you are referring to this as a model. All I did was correlate non-COVID excess deaths with new vaccinations over 20 weeks. I did this for 16 federal states, where the Pearson correlation coefficient ranges from 0.893 to 0.981.
The only modelling happens when I calculate excess mortality. I don't see how that needs to be validated and to be frank, I would not know how.
Thank you. I will look into the VAERS data for another article. Hopefully I can take a look into the buffer substance you mentioned.
I'm a bit rusty with the code to be honest. I'd usually just add my definition of a report cohort and get age gender adjusted and stratified results, but it requires some coding every time I add groups. The code is quite monstrous and I've been busy doing other things.
The thing is, I would like to do this and had even planned to add adiuvant groups to the cohorts on my website, but it can't be half-assed and takes time.
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?
Of course some deaths were counted as COVID that weren't. When I say COVID I'm thinking of COVID and/or "bad medicine" and/or lack of care. Basically excess deaths that occur during those COVID waves. However the negative excess mortality occurs because the COVID wave had already happened during flu season. There weren't enough frail people and no flu that was going around in January and February when we would expect higher mortality. Hence it was a COVID wave that made it possible to secretly kill people with vaccines.
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?
And there is non-COVID, non-vaccine excess mortality. I don't think it makes too much sense displaying this, but you can still get an idea what happened.
EDIT: Oh and also remember that most of these people are very frail to begin with and would have died within months. It still matters if their lives were taken by something they were told was harmless.
I agree; it matters a lot if you weaken people by forcing bad medicine on them. In December of 2022 and January of 2023 alone, I counted four deaths among relatives and acquaintances. All old and frail, sure, but the accumulation tells me something.
The bad medicine sure did a "great job", but don't underestimate the impact of loneliness and fear on life expectancy at that age. The isolation alone can kill. It's hard for many people to acknowledge it because loneliness is generally not quantified.
If this topic is of interest to you at all look into the "widowhood effect".
I actually offered 3 retirement homes to go for walks with the elders, but none of them seemed to want my help during that time. What happened back then shattered my soul day by day because I have worked with old people. Everyone who has seen a person who doesn't handle the nearing end very well knows fear of death is fear of loneliness, so knowing these things were happening crushed me. It was all the more hurtful when people started criticizing the unvaxxed for their "lack of solidarity".
Usually, the old people one personally knows are not representative of the loneliness and misery that abound. I learned this during community service (Zivildienst). For some, the doctor and the Zivi were the only people to talk to, and the doctor never had much time for talking.
Absolutely. Zivis and physical therapists are often those who have the most time on their hands for getting to know the patients and seeing the tragedies unfold. You are also not an integral part of the medical establishment, so patients tend to open up more.
Here you can not only see how they exploited mortality displacement to commit genocide (by UN definition), but also how the data is delayed and how the up-trend in mortality did NOT begin in 2022, but with the vaccination campaign in early 2021.
The data forgery goes much further than just the delays. They even implemented "fake christmas breaks" to make it look real.
Hopefully I can get help with FOIA requests, because fragdenstaat.de has removed the RKI from the list of intitutions I can send requests to.
Ultimately we will need a judge to order the release of data from a very powerful German GmbH.
There is a long road ahead of us to have justice served.
Answer to 2:
I haven't looked at non-COVID, non-excess deaths. I will create a chart for Germany and send you a link.
However I can not apply this to forged data and expect good results. The data is forged, beginning during the first wave of vaccinations, but much more severely during the second wave (boosters), with some variance between federal states.
We have to keep two things in mind:
1.) Many of the COVID deaths were attributed to other causes.
2.) The vaccination data is delayed and the delay increases over time.
Know that I am not with you regarding intent. The crimes are in the uncritical application of a new treatment, the forcing of the treatment on the population, and the refusal to acknowledge these mistakes. Hanlon's razor tells me that they (whoever "they" are) are too stupid to time the vaccination campaign as you described. And why should they?
We also have to acknowledge the extent and nature of the lies we were fed by the media.
Take for example the notion of unvaccinated "breeding out new variants". This is precisely what the vaccinations did. Since every vaccination wave co-occurred with covid waves, the breakthrough infections put uniform immune pressure on the virus, so new variants gaining predominance was inevitable.
Instead of acknowledging this very simple truth that I would think every virologist understands we ended up being presented with the exact opposite of the truth.
And that is what keeps puzzling me. To tell not just a lie, but one that claims the exact opposite of what is true, some knowledge of the truth is required. So who fed these lies to the ones who fed them to us?
Oh, at some point they *will* acknowledge that the vaccines have been responsible for new variants. And then they will praise the vaccines again, because the new variants are so much milder...
You sure do have a point. I don't think there is a large scheme that everyone is in on either (as in a conspiracy). However I am absolutely convinced that there are many people with a much better understanding of the data than me who should have seen what was happening. Instead of blowing the whistle, we ended up with forged data. The data is forged and I will prove it.
Ultimately, the motives of each actor have to be looked at individually. Some may have had a better understanding than others what they were helping to accomplish.
I am extremely skeptical that anyone timed the rollouts -- the vaccines were available when the vaccines were available, the boosters became de riguer when the vaccines waned, etc. @fabian, what you are seeing is uncritical thinking.
Oh and I think I'm only guilty of uncritical thinking when I sell it as a fact. I am not trying to do that. It warrants further investigation is all I'm saying. What lead to the decision to open schools BEFORE the vaccines were available? Wasn't the whole idea of the lockdowns at that point to bridge the gap until the vaccines became available?
There was considerable pressure on politicians to re-open schools. Another full year of home-schooling would have done incredible damage (I know; my wife is a teacher, and we have three kids at school).
That said, secondary schools around here (Hessen) only re-opened in 2021 (in our example: February for 5th grade, May for 8th grade). The December wave can not be blamed on that. My current opinion: it would have been better to completely re-open the schools, and everything else, in the summer of 2020. There is no way of stopping a respiratoy virus, and every measure tried (lockdowns, masks, vaccines) will only make it worse. Treat and care for sick people as we always did, and do not change the protocols during bad times.
I am not saying I think that lockdowns were a good idea here. However I am convinced they did affect how the epidemic progressed.
We did temporarily stop the respiratory virus in summer 2020. There were 0 deaths on some days I think. Cases were nearing zero anyway.
I wasn't aware that Hesse didn't open schools in 2020. However virus particles don't care what state they are in. I will look at case data for Hesse and compare it to other federal states. If the wave occurred with delay, I would still blame the school openings in the other states for the surge in cases.
But I do acknowledge that you could be right here. Maybe there is no apparent reason for the surge. Maybe it just happened.
Next article will probably tackle the issue of data forgery. I have crystal clear evidence.
Pressure from the people is not an explanation though, since there was pressure the whole time to do lots of things that were not done, but instead lead to the clubbing down of protestors. So how is it that the school openings were considered a good idea a few months before vaccines were deployed? It makes no sense whatsoever to me, not from a public health perspective.
Maybe, yes. Someone else remarked that. Btw I didn't mean the rollout was timed, but the regulations were relaxed to provoke the winter wave to occur before the rollout.
Without the December wave we would have seen the vaccines are killing people left and right. It may have been a coincidence, but no knowledge was ever generated from blaming things on incompetence and/or coincidence.
So I will still look into this, because I know for a fact the RKI is misrepresenting the data. This is a fact to me and I will be able to at least prove that. The data is forged, even if that may not be the correct legal term. It is being delayed intentionally.
The RKI is also deleting all bivalent booster doses that were administered before the approval date, so BQ.1 pops up before the bivalent boosters were administered. It says so on their Github. It's forgery and it occludes the truth. The truth being that BQ.1 is the variant adapted to Pfizer's BA4/5 imprinting.
Btw since you seem to be familiar with mortality data: I downloaded some data from the DWD (Deutscher Wetterdienst), but just from one location (Aaachen if I remember correctly). It turns out week 51/2022 saw a weekly average of daily average temperature below zero, while the last week of 2022 did not. How likely do you think it is that the excess deaths that occurred in the same week can be blamed on temperature?
Unlikely. Heat waves during summer are having a certain impact on deaths (mainly due to dehydration or heart failure, I guess), but as long as we are allowed to turn on the heating, we will be fine.
Yeah I know of summer deaths. The RKI said there were 4500. It looks to me as if there were more during the hottest weeks in July, closer to 10k. It's not entirely impossible these spike protein injuries are causing issues in these conditions. People also tend to be more active in summer time which should increase the risk for cardiovascular incidents.
Heating is an issue in Germany right now due to the high prices and possibly even the slightly delusional conviction that we have to "save on energy to save the planet". Just yesterday a friend told me she is getting sick "but it's [her] own fault because [she] tried to save on the gas bill".
Oh yes, the energy propaganda is another successful attempt at driving us crazy. However, I think that if people were actually freezing to death, mainstream media could not resist reporting. Effects of constant freezing on an immune system are harder to assess.
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?
I replied earlier thinking you were referring to the December 2020 wave and wanted to remove that comment, but removed yours instead. Sorry about that.
Anyway, you are right, I assume the 2022 deaths are caused by sequesters from spike exposure. But those who are vaccinated become susceptible. The variants are adapted to their deceptively imprinted immune systems. So the vaccinated tend to get reinfected and have to keep vaccinating against new variants, because the virus keeps adapting to them. Otherwise they have no neutralizing antibodies at all anymore. Then there's the issue of immune tolerance as well. It's like they always say: "It helps against symptomatic disease"
The higher your spike dose and the higher the exposure time, the higher the chance of sequesters, adverse events, a net reduction in life expectancy. Pure, unadulterated evil.
Thank you, I think likewise, and I am very disturbed that deaths are getting WORSE instead of slowly declining.
It's disturbing indeed. My home state peaked at 42% in the last reporting week, but the steady uptrend we saw throughout the year is more worrying to me.
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.
Keep me updated on your friend. Every single person who stops taking these drugs is a victory the way I see it.
No influencers. I am really bad at selling or promoting anything. Since I plan to make this my job for the next few years at least, I am very grateful for any advice you can give me.
Here is what I just emailed him.
"Hi XXXXXX
I know you value statistics, hard facts, math.
I hope these 3 articles from a fellow German on the devastation done by the injections to the German people will convince you the injections are not only not safe and effective but rather are overwhelmingly unsafe to the point of massive injuries and death and only effective as a depopulation tool which I believe there is much evidence for.
Here
German excess mortality (Part 1)
https://vigilance.pervaers.com/p/german-excess-mortality-part-1
German excess mortality (Part 2)
https://vigilance.pervaers.com/p/german-excess-mortality-part-2
German excess mortality (Part 3)
https://vigilance.pervaers.com/p/german-excess-mortality-part-3
Stay safe and free, Jack"
Will keep you updated, Fabian.
Addendum: my question above about who you might be targeting with your work, I gather you have not done that? So we can get together if you want and discuss this and other allied Covid1984 matters to help bring about a more free and peaceful world together. Email me: Jack at: responsiblyfree@protonmail.com
Igor chudov just linked your article! sometimes a helping hand comes along at the right time :)
Yes, he's the best. Promoted me on christmas already.
Great article and I mentioned you here:
https://igorchudov.substack.com/p/covid-vaccines-killed-278000-americans
Thank you.
Btw Igor, the article you promoted on christmas was gonna be my last. Just to give you an idea of the impact you had. :)
Wow! No kidding. I have a job too, I write in my spare time. Good luck with your job.
I quit before I started. ;) Gonna live the Bohemian life for a bit and focus on this.
My opinion, substack is not really a substitute for having a job
Maybe not, but I won't become a better data journalist by developing video games. I'll see where it goes from here.
The thing is, Covid vaccine is on the way out and it is happening quickly.
So it is time to think about our place in the post-Covid-vaccine world.
Well one of my last anyway. I was gonna focus on a new job starting January 1st, but decided I'd try to become better at this instead.
How accurate do you think that this estimate is?
https://igorchudov.substack.com/p/covid-vaccines-killed-278000-americans
Igor links the following study in that article:
https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-023-07998-3
His estimate is 4.4% higher than mine (1713 doses per death).
I am very familiar with vaccine pharmacovigilance data. My estimate has always between 1000-5000, best guess 2000 shots per death. You could deduce that my new estimate is a classical case of confirmation bias and I could not prove you wrong.
Just to clarify, you only way of validating your results, is to us an even more problematic data set?
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?
Oh and one person reported my findings from another article to multipolar-magazin.de (after liking my article a few weeks earlier), so I'm not the only one. That guy is sort of an influencer, but apparently isn't very creative, so he had to steal my work.
The thing is: I am not paid very well for doing this (less than 100 bucks per month as of now) and it takes a lot of time. It's a full-time job for me. There aren't many people who are willing to live the way I live now (without money) in order to give others a better idea of what happened. Many people used to work on it, but are now frustrated, because they received such harsh criticism from all sides. Many have just given up.
Well it's a little over 0.1% of the population. Nobody notices because these people are mostly old and frail. Many of them were in the last months of their life already.
Nobody else has done this as far as I know. The other month I correlated vaccination rates with excess mortality. Nobody had done that either.
Reasons I can think of:
- The data is hard to process for someone without any coding skills. The way it is presented makes things a bit difficult
- People don't expect to find anything worthwhile because everyone has access to the data
- Nobody will finance any studies of this kind, so it's just hobbyists
- People are too busy saying things that have been said a million times in order to get more clicks, likes, subscriptions, whatever. Processing data with due diligence takes time.
Around 1 million people are dying in Germany each year. Trend is upward, because the population is ageing.
1. Death rates are always increasing roughly exponentially with age. The younger age groups (say, 0-60) constitute around 70% of the population but only account for 8% of deaths. If something is killing people in this group, this will usually not be detectable by statistical methods but only by careful analysis (and reporting) of single cases.
2. The older age groups (say, 60+) account for almost all deaths, therefore statistical methods can be put to use. On the other hand, the general vulnerability of the elderly means that you rarely get actual excess deaths because, basically, causes of death are competing with each other. Put another way: humanity has gone a long way eradicating (or at least curtailing) many causes of death (the plague, tuberculosis, smallpox, whatever) so that we are now at a point where almost all deaths are either due to heart failure or cancer. Finding a cure for one disease leads to a short surge in life expectancy, until a new equilibrium has emerged. Even 50,000 excess deaths (that's my estimate for 2021 and 2022 combined) is compatible with 100,000 vaccine-related deaths.
I see. Thank you for your assessment.
How did you reach your estimate of 50k excess deaths? You are obviously using another method than I am using. Could you explain to me why you think that method is better suited to quantify excess mortality? What I am doing is more or less intuitive.
Guys, thanks so much for having this discussion. We need to help each other not overlook something.
1. Because I have been Simpsoned way too often, I always stratify by age group.
2. Excess has to relate to a reference point ("excess over what?"). In this case, for example, to a previous year, corrected for the expected change.
My current thinking is like this:
https://cm27874.substack.com/p/excess-deaths-in-germany-2022-update
- 2020 had no excess mortality (although, of course, deaths mainly happened towards the end of the year because of Covid)
- 2021 had excess mortality of around 17,000 over 2020 (after correcting for expected increase in mortality due to ageing population)
- 2022 had excess mortality of around 17,000 over 2021 (again, after correction)
Note also that Marcel Barz seems to have been the first to do the analysis for 2020 (and he had to take quite a shitstorm for this):
https://www.youtube.com/watch?v=nEPiOEkkWzg
Again, excess deaths are only the conversation starter. They indicate that there is a problem, but more in the sense of the tip of the iceberg.
I try to stratify where it's feasible. It seemed a waste of time with those four weird age groups destatis offers for federal states. In the safety signals on my website I even adjusted the internal age structure of each age/gender group though.
Working on the mortality charts by age group. It's 45 age/gender groups and since I am planning to update these I'll probably automate the process by writing svg's. Not sure if I should dedicate the next article to those or to misrepresentation of the vaccination data.
I rarely watch videos at all, but I remember Marcel Barz. I watched nearly the whole video back then.
So what do you think of my method of correcting for demographic trend without age adjustment?
Since you want to compare to vaccination rates, and these are only available for broad age groups, your adjustment is the best one can do. I recommend (and I think I did this before) repeating the exercise after restricting all data to 60+.
Kuhbandner and Reitzner produced a paper on excess mortality in Germany:
https://www.researchgate.net/publication/362777743_Excess_mortality_in_Germany_2020-2022
I had heard of the study, took a glance, but never took the time to sit down and look at what they did.
In Figure 13 they are doing excactly what I did in this article. A commentor on researchgate criticized that:
"Fig.13 does not show anything that could be reasonably described as a "strong covariation" of excess deaths with vaccines dosage"
I hope I filled this gap. 95% of the variance are explained by vaccinations.
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?
Or this https://substack.pervaers.com/MC_Bad_Cat/78-95%3BTRIVARIATE_MODEL%3B20sec%3B1681837157143.mp4
(Beta is just normalized to abs1, not -1; 1)
I'd just stretch your timeseries or mine too the same length and subtract covid deaths. Very simple
Well I could do that for you using covid deaths if you send me your mortality timeseries
Me@pervaers.com
Also check out this, where I did something similar to what you are doing for US states.
https://q3deathwave.pervaers.com/video/3a;78-95;Cases_Accu_PerUninfected_Perc;D1_18-64_Accu_Perc;20sec.mp4
When you're done we could visualize whatever you come up with this way if you're interested.
Brilliant work Fabian. I just wish I could click my fingers and have it replicated for New Zealand. I'll give it a go.
Having a hard time finding weekly data.
If you can supply me with time series of weekly or daily data for all cause mortality I'll create the excess mortality time series and some charts for you.
I'll do that for you if you want me to. Shouldn't take too long.
I have weekly ACM for NZ from Wk1 2011 to week 52 2022. Is that all you need? Have you studied the methodology at OWID, described here?
https://ourworldindata.org/excess-mortality-covid
Your method of bringing the earlier years into demographic alignment with present years is intriguing. Can it all be done using Collabora Office?
Have you also seen the pattern in Germany where ACM can drop, in absolute terms after a bad previous year, due to severe influenza, for example? The simple models dont seem to factor this in.
Send an email with the data to me@pervaers.com
Could you also please tell me where you got the data?
That's perfect. You could do it with any spreadsheet program.
I could create a LibreOffice spreadsheet.
OWID are a bunch of crooks. It's financed by Bill Gates and Susanne Klatten. All the data there is presented in a way that obfuscates what happened.
But it would be interesting to look at their method and how it is inappropriate.
The way I see it any excess mortality measurement that compares 2022 with 2020 or 2021 should be ignored.
OK. That's what I want hear and prove, that OWID are obfuscating the data. It is this data that the NZ government Muppet is using in the case where we are the plaintiffs and they are the respondents.
I've just about loaded all data points in an Excel spreadsheet.
How can I get it to you? Gmail?
I wrote you my email address in another reply. (me@pervaers.com)
OWID is also delaying release of vaccination data somehow. It's possible that the respective governments are delaying the release and OWID is just passing it on, but either way: This obfuscates the truth.
Really I think all we need to do is prove that the official data is not very useful. Privacy laws will have to be circumvented somehow and proper data will have to be released, ideally anonymized death certificates along with the respective digital id entries. First we need to prove that the official data doesn't suffice or that there is reason to believe it misrepresents reality.
There's an example of what OWID is doing in my Omicron article.
https://vigilance.pervaers.com/p/boosters-caused-the-omicron-outbreak
Brilliant Fabian. I'll get that data to you. And thanks for all your help. I want to nail these bastards.
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.
I absolutely agree with what you said up there. I believe all these mechanisms do play a role.
I am tackling your 2nd point in article 4 of this series. The vaccines are not just affecting COVID specific immunity, this effect on immunity is also affecting the virus itself which in turn decreases the immunity of those who received a vaccine with delay.
Alas we simply do not get data stratified by vaccination status. This should be the main objective. In order to get it done we need to data that is with the companies issueing these digital ID's. It's usually NGO's. Bundesdruckerei in Germany.
How did you validate you model?
There are three estimates of excess deaths in USA, two using surveys and one where I extrapolated Fabian's deaths-to-doses ratio to USA. All come close
https://igorchudov.substack.com/p/covid-vaccines-killed-278000-americans
Don't invest too much energy in this guy. I have probably spent close to an hour replying to him, but it turned out he hasn't even read my article.
And? How accurate should we consider any of them?
We will never know the exact number of the deaths and it is not even possible to agree on the precise definition. However the fact that three completely different estimates came close to each other is extremely notable.
I guess it is. I'm just curious what it indicates. What can we learn, and how certain can we be that we're recognizing/interpreting the signal correctly.
Well I welcome you to join in. The more perspectives we have the better we can further our understanding of the situation.
We can never be completely certain about almost anything however we're just doing our best and Publishing what we find and learn from our mistakes
That's why we need to try and figure out the degree of uncertainty, or error.
Mistakes are part of reaserch, but we can limit them by being aware of the assumptions we make, and their limitations. And any computational model will only be as reliable as the assumption it's based on, and the data used.
Well I haven't, but you will find out if I am wrong once I find out. Everybody is
wrong all the time, so I won't make a big deal out of it.
What's important is working towards a better understanding and staying honest while we do.
It's on the to-do list for sure. Any particular way you want me to validate it? The vague idea I am having is just looking at excess mortality data of those countries that issued their vaccination data on a daily basis, the same way I did for German federal states.
Keep in mind I am not a mathematician or data scientist, just a hobbyist looking to understand what is going on. I'll gladly expand my toolset anytime, so looking forward to hearing your suggestions.
I agree, everyone has a right to be wrong. I was wondering what efforts you made to reduce that chance.
I have no specific expertise in this kind of modeling, but I'm sure you could find some protocol. Method validation is usually conducted usiya separate, independent data set, that was analysed using another method.
I agree, honesty and transparency are important. And I'm curious if you readers understand how to interpret your results? Or if they even care?
I am not sure why you are referring to this as a model. All I did was correlate non-COVID excess deaths with new vaccinations over 20 weeks. I did this for 16 federal states, where the Pearson correlation coefficient ranges from 0.893 to 0.981.
The only modelling happens when I calculate excess mortality. I don't see how that needs to be validated and to be frank, I would not know how.
The question isn't the 'what', but the 'how' you did it, and 'why' you did it the way you did.
At this point I'll have to ask you if you even read the article, because I am providing all the code and data to the readers in the Methods section.
The code is pretty lean, too. It's not hard to understand at all, very basic.
I have, and I kept looking for the section that explains the basic assumptions used for your analysis.
I understand that you evaluated the correlation between mortality and vaccine doses.
What other work did you do? Did you evaluate any other factors? Do you have any information regarding the vaccination status of the people who died?
Shared on Gettr
Thank you. I will look into the VAERS data for another article. Hopefully I can take a look into the buffer substance you mentioned.
I'm a bit rusty with the code to be honest. I'd usually just add my definition of a report cohort and get age gender adjusted and stratified results, but it requires some coding every time I add groups. The code is quite monstrous and I've been busy doing other things.
The thing is, I would like to do this and had even planned to add adiuvant groups to the cohorts on my website, but it can't be half-assed and takes time.
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!
I will cover this in the next article. That's what made the whole crime possible.
https://en.wikipedia.org/wiki/Mortality_displacement
Of course some deaths were counted as COVID that weren't. When I say COVID I'm thinking of COVID and/or "bad medicine" and/or lack of care. Basically excess deaths that occur during those COVID waves. However the negative excess mortality occurs because the COVID wave had already happened during flu season. There weren't enough frail people and no flu that was going around in January and February when we would expect higher mortality. Hence it was a COVID wave that made it possible to secretly kill people with vaccines.
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?
And there is non-COVID, non-vaccine excess mortality. I don't think it makes too much sense displaying this, but you can still get an idea what happened.
https://substack.pervaers.com/excess_mortality_3/test.png
EDIT: Oh and also remember that most of these people are very frail to begin with and would have died within months. It still matters if their lives were taken by something they were told was harmless.
...and thanks for this.
I agree; it matters a lot if you weaken people by forcing bad medicine on them. In December of 2022 and January of 2023 alone, I counted four deaths among relatives and acquaintances. All old and frail, sure, but the accumulation tells me something.
The bad medicine sure did a "great job", but don't underestimate the impact of loneliness and fear on life expectancy at that age. The isolation alone can kill. It's hard for many people to acknowledge it because loneliness is generally not quantified.
If this topic is of interest to you at all look into the "widowhood effect".
I actually offered 3 retirement homes to go for walks with the elders, but none of them seemed to want my help during that time. What happened back then shattered my soul day by day because I have worked with old people. Everyone who has seen a person who doesn't handle the nearing end very well knows fear of death is fear of loneliness, so knowing these things were happening crushed me. It was all the more hurtful when people started criticizing the unvaxxed for their "lack of solidarity".
Usually, the old people one personally knows are not representative of the loneliness and misery that abound. I learned this during community service (Zivildienst). For some, the doctor and the Zivi were the only people to talk to, and the doctor never had much time for talking.
Absolutely. Zivis and physical therapists are often those who have the most time on their hands for getting to know the patients and seeing the tragedies unfold. You are also not an integral part of the medical establishment, so patients tend to open up more.
Sorry if my reply to your second question sounded a bit snappy. I will try again.
Answer to 1:
Non-vaccine excess mortality. I have all the charts prepared. It's for the next article.
This is the crime:
1. They opened schools in August or September, provoking a December wave of COVID deaths
2. This causes mortality displacement with a deficit in flu season (early 2021)
3. They timed the vaccination campaigns perfectly to fall into the time of displaced mortality
I uploaded a sample for you:
https://substack.pervaers.com/excess_mortality_3/C_0_Deutschland.png
Here you can not only see how they exploited mortality displacement to commit genocide (by UN definition), but also how the data is delayed and how the up-trend in mortality did NOT begin in 2022, but with the vaccination campaign in early 2021.
The data forgery goes much further than just the delays. They even implemented "fake christmas breaks" to make it look real.
Hopefully I can get help with FOIA requests, because fragdenstaat.de has removed the RKI from the list of intitutions I can send requests to.
Ultimately we will need a judge to order the release of data from a very powerful German GmbH.
There is a long road ahead of us to have justice served.
Answer to 2:
I haven't looked at non-COVID, non-excess deaths. I will create a chart for Germany and send you a link.
However I can not apply this to forged data and expect good results. The data is forged, beginning during the first wave of vaccinations, but much more severely during the second wave (boosters), with some variance between federal states.
We have to keep two things in mind:
1.) Many of the COVID deaths were attributed to other causes.
2.) The vaccination data is delayed and the delay increases over time.
Thank you for the additional explanations.
Know that I am not with you regarding intent. The crimes are in the uncritical application of a new treatment, the forcing of the treatment on the population, and the refusal to acknowledge these mistakes. Hanlon's razor tells me that they (whoever "they" are) are too stupid to time the vaccination campaign as you described. And why should they?
We also have to acknowledge the extent and nature of the lies we were fed by the media.
Take for example the notion of unvaccinated "breeding out new variants". This is precisely what the vaccinations did. Since every vaccination wave co-occurred with covid waves, the breakthrough infections put uniform immune pressure on the virus, so new variants gaining predominance was inevitable.
Instead of acknowledging this very simple truth that I would think every virologist understands we ended up being presented with the exact opposite of the truth.
And that is what keeps puzzling me. To tell not just a lie, but one that claims the exact opposite of what is true, some knowledge of the truth is required. So who fed these lies to the ones who fed them to us?
Oh, at some point they *will* acknowledge that the vaccines have been responsible for new variants. And then they will praise the vaccines again, because the new variants are so much milder...
Hahaha, yes. I got that a lot after writing the Omicron article.
You sure do have a point. I don't think there is a large scheme that everyone is in on either (as in a conspiracy). However I am absolutely convinced that there are many people with a much better understanding of the data than me who should have seen what was happening. Instead of blowing the whistle, we ended up with forged data. The data is forged and I will prove it.
Ultimately, the motives of each actor have to be looked at individually. Some may have had a better understanding than others what they were helping to accomplish.
I am extremely skeptical that anyone timed the rollouts -- the vaccines were available when the vaccines were available, the boosters became de riguer when the vaccines waned, etc. @fabian, what you are seeing is uncritical thinking.
Just out of curiosity: How do you explain that every vaccine campaign coincides with a COVID wave?
I have yet to see this issue tackled anywhere. There could be a direct causal relationship, but I doubt that.
Oh and I think I'm only guilty of uncritical thinking when I sell it as a fact. I am not trying to do that. It warrants further investigation is all I'm saying. What lead to the decision to open schools BEFORE the vaccines were available? Wasn't the whole idea of the lockdowns at that point to bridge the gap until the vaccines became available?
There was considerable pressure on politicians to re-open schools. Another full year of home-schooling would have done incredible damage (I know; my wife is a teacher, and we have three kids at school).
That said, secondary schools around here (Hessen) only re-opened in 2021 (in our example: February for 5th grade, May for 8th grade). The December wave can not be blamed on that. My current opinion: it would have been better to completely re-open the schools, and everything else, in the summer of 2020. There is no way of stopping a respiratoy virus, and every measure tried (lockdowns, masks, vaccines) will only make it worse. Treat and care for sick people as we always did, and do not change the protocols during bad times.
I am not saying I think that lockdowns were a good idea here. However I am convinced they did affect how the epidemic progressed.
We did temporarily stop the respiratory virus in summer 2020. There were 0 deaths on some days I think. Cases were nearing zero anyway.
I wasn't aware that Hesse didn't open schools in 2020. However virus particles don't care what state they are in. I will look at case data for Hesse and compare it to other federal states. If the wave occurred with delay, I would still blame the school openings in the other states for the surge in cases.
But I do acknowledge that you could be right here. Maybe there is no apparent reason for the surge. Maybe it just happened.
Next article will probably tackle the issue of data forgery. I have crystal clear evidence.
I know, I have a daughter of 13.
Pressure from the people is not an explanation though, since there was pressure the whole time to do lots of things that were not done, but instead lead to the clubbing down of protestors. So how is it that the school openings were considered a good idea a few months before vaccines were deployed? It makes no sense whatsoever to me, not from a public health perspective.
Maybe, yes. Someone else remarked that. Btw I didn't mean the rollout was timed, but the regulations were relaxed to provoke the winter wave to occur before the rollout.
Without the December wave we would have seen the vaccines are killing people left and right. It may have been a coincidence, but no knowledge was ever generated from blaming things on incompetence and/or coincidence.
So I will still look into this, because I know for a fact the RKI is misrepresenting the data. This is a fact to me and I will be able to at least prove that. The data is forged, even if that may not be the correct legal term. It is being delayed intentionally.
The RKI is also deleting all bivalent booster doses that were administered before the approval date, so BQ.1 pops up before the bivalent boosters were administered. It says so on their Github. It's forgery and it occludes the truth. The truth being that BQ.1 is the variant adapted to Pfizer's BA4/5 imprinting.
Btw since you seem to be familiar with mortality data: I downloaded some data from the DWD (Deutscher Wetterdienst), but just from one location (Aaachen if I remember correctly). It turns out week 51/2022 saw a weekly average of daily average temperature below zero, while the last week of 2022 did not. How likely do you think it is that the excess deaths that occurred in the same week can be blamed on temperature?
Unlikely. Heat waves during summer are having a certain impact on deaths (mainly due to dehydration or heart failure, I guess), but as long as we are allowed to turn on the heating, we will be fine.
https://cm27874.substack.com/p/deaths-in-summer
Yeah I know of summer deaths. The RKI said there were 4500. It looks to me as if there were more during the hottest weeks in July, closer to 10k. It's not entirely impossible these spike protein injuries are causing issues in these conditions. People also tend to be more active in summer time which should increase the risk for cardiovascular incidents.
Heating is an issue in Germany right now due to the high prices and possibly even the slightly delusional conviction that we have to "save on energy to save the planet". Just yesterday a friend told me she is getting sick "but it's [her] own fault because [she] tried to save on the gas bill".
Oh yes, the energy propaganda is another successful attempt at driving us crazy. However, I think that if people were actually freezing to death, mainstream media could not resist reporting. Effects of constant freezing on an immune system are harder to assess.
Yeah, it seems keeping us afraid is absolutely essential. Sanity and empathy seem to disintegrate when people are afraid.
Have you ever heard of Kohlenklau? It's Déjà vu all over again. Foreign minister Bärbock said we were at war with Russia today.
But she is always wearing such nice dresses!
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