*Edit to say my math is probably retarded, but the point stands....
I watched a very interesting video from someone who analyzed the VAERS data. The vast majority of problems clustered in 1 in 200 vaccine batches.
It looked as though the companies (Pfizer, J&J and Moderna) were coordinating when the bad batches were released when he time plotted the data.
If your odds of getting a bad batch are 1 in 200, then for every subsequent shot/booster, your odds of exposure go up significantly.
1 in 200 first dose / 1 in 100 by second dose / 1 in 50 with booster / 1 in 25 on second booster and so on. By the 8th dose, your odds of getting a bad batch somewhere along the line is 1 in 1.5625 cases which means that 64% of the vaccine recipients would have had a bad batch along the way.
And the VAERS data likely doesn't even capture the long-term side effects such as damaging your immune system (HIV like) or causing cancer down the road. They primarily report immediate outcomes like strokes, myocarditis, bells palsy, etc.
That video was very seriously flawed. It's basic premise is impossible.
I downloaded the VAERS data and tried to recreate his results.
It's impossible that '1 in 200 batches' were a problem, because there are only (roughly) 300 real batch numbers.
The remaining 40,000+ (false) 'batches' are various typos, or entries like 'N/A', or 'Unknown', 'idk', etc. One-third of all the entries were simply blank. These false batches are identified as 'a huge number of safe batches', each with only one or two entries. But they don't actually exist. They are data errors.
If he removed (or corrected) all the typos, he would be left with (roughly) 300 batches -- but he would still have no way of knowing if those batches were of the same total size (and therefore analytically comparable). The CDC specifically states that it will not release that information to the public.
Just because this (very flawed) video supports our point of view, it doesn't mean we shouldn't check to see if it is a proper analysis. And it isn't.
7.89 billion shots apparently, that's not going to divide into 300 batches is it? That would be about 25m vials per batch, even if we were to reduce that by 10x to make it more applicable to the US only that's 2.5m vials per batch, batches are not 2.5m in number. Something has gone wrong with the numbering or was fixed, either way 300 batches isn't going to be accurate simply by handling the numbers involved roughly.
When you click the link click "I Agree" button at them bottom of the 1st page > "VAERS DATA SEARCH" button in the middle > then on the next page click any of the "SEND" buttons on the right.
and yes there are typos. Lots of them but it's obvious which ones those are.
You can check any of those lot #'s for validity at https://vaxcheck.jnj/
I'm not looking at the data wrong. There are only (about) 300 unique batch numbers, once you remove typos.(Prove me wrong.)
Due to that fact alone, you can't have an analysis that says there are 200 'safe' batches for every 'toxic' batch. (It would mean you either have one or two 'toxic' batches.)
Look at the data that comes from your search...
I'm currently looking at a region around Jun 2021, and (genuine) batch number '042A21A'. That batch number is represented dozens and dozens of ways in the database.
Each of the **'A'**s are sometimes represented as a '4' or an '8' or an 'H' (the first is a 'looks like' error, the second two are a 'sounds like' error.)
Each of the **'2'**s are sometimes changed to a 'Z'.
Sometimes people put a dash (or space or hashtag) between '042A' and '21A'. Sometimes in other places.
Sometimes some of the numbers or letters are simply left off.
Therefore, you could see "042A214", "042A -21A", "042AZ1A", "042A218", "042-A21A", "#042A21A", "042421A", "042A21H", and so on - which are all the same batch.
When you figure out how many ways this ONE batch number is represented in the database, it staggers the imagination. And each one of these 'false' batch numbers is assumed (by the video) to be a 'safe batch' because it only links to one adverse event.
The database is hopelessly 'unclean'. If you (or someone else) has a carefully cleaned version, please let me know. Without removing the 'fake' batch numbers, you cannot run an analysis.
In the search you provided, the 'largest batches' that I saw was one called 'Unknown' with 1,782 cases - and another called 'NONE' with 4,669 cases.
The truth is that (if you look at only the real batches), most have a serious ('toxic') number of links - AND you are still missing all the links that are 'lost' because they are 'spread out' among all the typos. And then, you still can't compare batch to batch unless you know that the batches are all the same size - and the CDC specifically refuses to release that data to the public.
Why are you making your argument about typos when I already said there are obvious typos? Straw-man much?
I specifically trimmed a search to make all of that obvious AND provided a way to verify WITH the manufacturer the validity of the batch # WHICH WAS TO DEAL WITH THE ARGUMENT YOU JUST MADE AGAIN IN DETAIL.
Focusing / highlighting only UNKNOW batches is just intellectually dishonest as that's not what I'm pointing to.
I spend 4 hours a day, 5 days a week in VAERS data. I know it's full of typos and I know how to look at the data and generate very tailored and specific reports.
What we can clearly see though, despite that, is REAL batches with X numbers of issues and REAL batches with XXX of issues. for J&J alone. THAT is what I was pointing to.
We can also put missing series numbers into the J&J page and see they are valid batches and hypothesize those may be saline or something neutral.
Probability math is a little more complicated. If you have a 1% chance of winning and you will have 100 attempts. Your odds of winning calculated before you begin are something around 67%.
There's likely something destructive in most of them. That doctor in Canada who did a ddimer test on his patients who got the jab found that 60% of them tested positive for blood clots.
*Edit to say my math is probably retarded, but the point stands....
I watched a very interesting video from someone who analyzed the VAERS data. The vast majority of problems clustered in 1 in 200 vaccine batches.
It looked as though the companies (Pfizer, J&J and Moderna) were coordinating when the bad batches were released when he time plotted the data.
If your odds of getting a bad batch are 1 in 200, then for every subsequent shot/booster, your odds of exposure go up significantly.
1 in 200 first dose / 1 in 100 by second dose / 1 in 50 with booster / 1 in 25 on second booster and so on. By the 8th dose, your odds of getting a bad batch somewhere along the line is 1 in 1.5625 cases which means that 64% of the vaccine recipients would have had a bad batch along the way.
And the VAERS data likely doesn't even capture the long-term side effects such as damaging your immune system (HIV like) or causing cancer down the road. They primarily report immediate outcomes like strokes, myocarditis, bells palsy, etc.
That video was very seriously flawed. It's basic premise is impossible.
I downloaded the VAERS data and tried to recreate his results.
It's impossible that '1 in 200 batches' were a problem, because there are only (roughly) 300 real batch numbers.
The remaining 40,000+ (false) 'batches' are various typos, or entries like 'N/A', or 'Unknown', 'idk', etc. One-third of all the entries were simply blank. These false batches are identified as 'a huge number of safe batches', each with only one or two entries. But they don't actually exist. They are data errors.
If he removed (or corrected) all the typos, he would be left with (roughly) 300 batches -- but he would still have no way of knowing if those batches were of the same total size (and therefore analytically comparable). The CDC specifically states that it will not release that information to the public.
Just because this (very flawed) video supports our point of view, it doesn't mean we shouldn't check to see if it is a proper analysis. And it isn't.
The 600 trillion gorillion vials were contained in 300 batches? Really?
https://www.bloomberg.com/graphics/covid-vaccine-tracker-global-distribution/
7.89 billion shots apparently, that's not going to divide into 300 batches is it? That would be about 25m vials per batch, even if we were to reduce that by 10x to make it more applicable to the US only that's 2.5m vials per batch, batches are not 2.5m in number. Something has gone wrong with the numbering or was fixed, either way 300 batches isn't going to be accurate simply by handling the numbers involved roughly.
The 7.89 billion shots are worldwide, whereas the US is only 454 million (according to the article you referenced).
VAERS only covers the US (and only 1% to 10% of cases, by most estimates).
Therefore, the (roughly) 300 unique batch numbers that show up in the VAERS database covers between 4.5 million to 45 million shots.
That works out to a batch size of between 15,000 and 150,000 doses.
At the (false) number of 40,000 batches that exist in VAERS, that would mean each batch is between 112 and 1125 doses.
Does that sound reasonable?
You are looking at the data wrong.
While it's not 10% (currently but they can change that) there are obviously deadly batch's and it's easy to see.
I've made a sample saved search of the VAERS to show just JnJ by lot # and month. You can modify the search nut start it exactly as I have it
https://wonder.cdc.gov/controller/saved/D8/D247F737
When you click the link click "I Agree" button at them bottom of the 1st page > "VAERS DATA SEARCH" button in the middle > then on the next page click any of the "SEND" buttons on the right.
and yes there are typos. Lots of them but it's obvious which ones those are. You can check any of those lot #'s for validity at https://vaxcheck.jnj/
I'm not looking at the data wrong. There are only (about) 300 unique batch numbers, once you remove typos. (Prove me wrong.)
Due to that fact alone, you can't have an analysis that says there are 200 'safe' batches for every 'toxic' batch. (It would mean you either have one or two 'toxic' batches.)
Look at the data that comes from your search...
I'm currently looking at a region around Jun 2021, and (genuine) batch number '042A21A'. That batch number is represented dozens and dozens of ways in the database.
Each of the **'A'**s are sometimes represented as a '4' or an '8' or an 'H' (the first is a 'looks like' error, the second two are a 'sounds like' error.)
Each of the **'2'**s are sometimes changed to a 'Z'.
Sometimes people put a dash (or space or hashtag) between '042A' and '21A'. Sometimes in other places.
Sometimes some of the numbers or letters are simply left off.
Therefore, you could see "042A214", "042A -21A", "042AZ1A", "042A218", "042-A21A", "#042A21A", "042421A", "042A21H", and so on - which are all the same batch.
When you figure out how many ways this ONE batch number is represented in the database, it staggers the imagination. And each one of these 'false' batch numbers is assumed (by the video) to be a 'safe batch' because it only links to one adverse event.
The database is hopelessly 'unclean'. If you (or someone else) has a carefully cleaned version, please let me know. Without removing the 'fake' batch numbers, you cannot run an analysis.
In the search you provided, the 'largest batches' that I saw was one called 'Unknown' with 1,782 cases - and another called 'NONE' with 4,669 cases.
The truth is that (if you look at only the real batches), most have a serious ('toxic') number of links - AND you are still missing all the links that are 'lost' because they are 'spread out' among all the typos. And then, you still can't compare batch to batch unless you know that the batches are all the same size - and the CDC specifically refuses to release that data to the public.
Why are you making your argument about typos when I already said there are obvious typos? Straw-man much?
I specifically trimmed a search to make all of that obvious AND provided a way to verify WITH the manufacturer the validity of the batch # WHICH WAS TO DEAL WITH THE ARGUMENT YOU JUST MADE AGAIN IN DETAIL.
Focusing / highlighting only UNKNOW batches is just intellectually dishonest as that's not what I'm pointing to.
I spend 4 hours a day, 5 days a week in VAERS data. I know it's full of typos and I know how to look at the data and generate very tailored and specific reports.
What we can clearly see though, despite that, is REAL batches with X numbers of issues and REAL batches with XXX of issues. for J&J alone. THAT is what I was pointing to.
We can also put missing series numbers into the J&J page and see they are valid batches and hypothesize those may be saline or something neutral.
1/200= .005 or 0.5% 8 jabs 1-(199/200)^8 =0.039306… or 3.9% getting bad jab.
Probability math is a little more complicated. If you have a 1% chance of winning and you will have 100 attempts. Your odds of winning calculated before you begin are something around 67%.
There's likely something destructive in most of them. That doctor in Canada who did a ddimer test on his patients who got the jab found that 60% of them tested positive for blood clots.