Let's look into this...
Love your analysis of an analysis. A couple questions:
Do we know how many vials are in a lot number and does this vary? Could it be that the toxic VLs simply had more vials produced and deployed than the less toxic VLs?
How could a manufacturer vary the “payload” or other attributes across VLs? Doesn’t the EUA have required specs?! I.e., what product was actually authorized if the mfg is allowed to alter the authorized spec?
Do we know if the VLs deployed in the US all come from the same mfg plant? The use of letters in the nomenclature suggests to me different plants. And shouldn’t the numbering/lettering be part of the EUA spec?
Very interesting and disturbing!
I have received a couple of comments from Craig Paardekooper. Like you I had conjectured that he derived the date of the deployment of the batches from the batch ID. But we are wrong. His account of what he did is as follows:
perhaps this might explain the time sequence -
NOTE ON TIME SEQUENCE OF BATCHES
Note: The time sequence of all batches is determined by the order in which each batch first appears in VAERS. This inturn is determined by the order of the VAERS ID for the first report associated with that batch.
The VAERS ids are ALWAYS ascending, are completely numeric, and are unique. As each new report is made, the VAERS system generates the next successive numerical VAERS ID for that report.
Consequently, the resultant graph depicts the time sequence in which the batches were FIRST REPORTED for adverse reactions. Yes, there could be a delay between the deployment of a batch and the first report, HOWEVER this would be totally insignificant given the number of reports generated by batches, and the very high probability that some reports were generated within a day of taking the vaccine.”
A further comment is this.
“I have seen Jessica's post.
My time sequence for the batches is based on the sequence of VAERS Ids. As each report is added to VAERS it generates a new VAERS Id - which is a pure number, always ascending.
I count the number of adverse reactions for each batch in the order in which those batches appear in the VAERS database - in other words, the order in which a FIRST adverse report is made for each batch.
Now, if there was one report for a batch, then the whole batch must have been made prior to that time - so we have, in effect, the order in which the batches were reported, which equals the order in which the batches were deployed.
Consequently, my graph is sound.
Jessica may have assumed I was sequencing the batches based on batch lot numbers.
These explanations make perfectly good sense to me. As far as I can see they support the whole analysis given in his video, including the temporal separation of the batches of the three manufacturers.
His analysis strikes me as being so extraordinarily significant that I would like to see attempts at disconfirming it from every possible angle, to see whether it still stands.
I thought you had his email address, but if not I am happy to act as go-between between you and him. If you would like me to send you his email address just email me at the address I used to subscribe here. In any case I will be most interested in any further consideration of his analysis.
I'm rarely monosyllabic, but... "Whew!"
Jessica, there was a discussion about the idea of "highly toxic lots" in the comments section to your Nov. 2 article "A Report on Myocarditis Adverse Events....". At that time, you said that the data was too sparse to support any conclusions or claims about significant patterns. You were especially concerned that many records have missing or obviously spurious VAX_LOT fields.
I'm glad to see that you're still looking at this. The distribution of events is very different from what I would expect from randomly distributed, sparsely sampled AE's. So, I was puzzled by your earlier dismissal.
Could you clarify how your thinking has changed, about the problem of sparse data?
Is it possible that reporting bias could be an important factor? That is, Steve Kirsch mentions one particular neurological practice with 20,000 patients that reported 2,000 AE's. Other clinics might have made zero VAERS reports even with similar actual AE rates. What if clinics with high reporting propensity, were somehow associated with shots from just a few lots?
THE EXPOSE (UK) noticed that AE reports from red states were coming in at a much greater rate than blue states. The rate from Kentucky was about 20x worse than from California. They thought perhaps the red states were being targeted with bad lots. But perhaps again it's a reporting bias, as red-state doctors are much more likely to report AE's?
Have you read Andreas Oehlers writing on this?
Craig Paardekooper has posted another video today (Dec. 1, 2021) at https://www.bitchute.com/video/g62gcJrOuOYu/, titled COVID VACCINE BATCH NUMBERS CODE FOR TOXICITY. He uncovers an extraordinary pattern. The video at https://www.bitchute.com/video/6xIYPZBkydsu/, VARIATION IN TOXICITY OF COVID VACCINE BATCHES is also worth watching.
I think this might be a HOT LOT #032H20A: https://i.imgur.com/GCqqSPf.jpg
Well done data analysis. If only most commentators were as competent as you.
I have done some analysis on cleaning up Lot numbers.
I noticed for MODerna, aswell as their format 999X99X (eg. 025J20A) they also have a format 999X99-9X (eg: 025J20-2A)
I noticed a large number of events against 025J202A, but this is not a valid Lot# and should actually be 025J20-2A.
I recognize my vax lot in your work. Yikes.
It would be good to get Craig Paardekooper’s input on this, and I have been trying to find contact information for him. I haven’t been able to find an email address for him, but I have left messages in various places, such as this facebook page, https://www.facebook.com/craig.kooper.3. He has videos on bitchute: https://www.bitchute.com/Craig-Paardekooper/. I hope he will join in here.
Your hypothesis that Craig is taking the deployed date of the Vaccine Lot from its alphanumeric ID is plausible, and if this was done without taking into account the differing ID patterns used by the differing manufacturers, then this would indeed explain the apparent temporal separation of the batches by manufacturer. However, this seems to me too obvious an error for Craig to have made, though I grant he might have gotten excited about seeing a pattern which didn’t mean what he took it to mean, and so leapt too quickly to a mistaken interpretation. But we can ask him, if he will join in.
It is a reasonable conjecture that each manufacturer will be auto-generating its own Vaccine Lot ID, and that these will occur in ordinal sequence per manufacturer. It might be interesting to separate the data by manufacturer and then graph the AEs by Lot ID, and see what it shows.
I have read a story about a previous coverup in which bad batches were spread out geographically so as to conceal them. (I will look for a reference to that story, but I don’t have it right at my fingertips.) I think that Craig is suggesting that we are looking at an even more sinister scenario: namely that the data show the fingerprints of a deliberately designed medical experiment. It is important to note that if we are looking for confirmation of such a scenario then we are over-focussing on dates. All that matters is VL ID. Imagine, for example, that certain lots contain discrete amounts of graphene hydroxide, but most do not. The experimenters need not care about dates as long as they are able to calibrate the lethality by lot number. I do not myself believe that we are in such a scenario, but I do not discount the possibility, which is why I find Craig’s analysis so alarming.
I come away from Craig’s and this analysis with a very bad smell lingering … clearly something is not right. Jessica, I love your critical eye! However, as Craig’s temporal ordering was not by alphanumeric sorting but by VAERS report date (very smart!) we are left again with something like his disturbing conclusion that batches are being varied to see what AE reports result. Which leaves us with two “certainties” and more questions:
1) VAERS is not the crappy unreliable system the CDC/FDA claim it to be. It is possibly serving as the platform for a massive trial on the general public? Thus would mean the big three are consuming this data as carefully as we are. I believe
the data quality is pretty good as far as large databases go … I have read new reports are managed and examined by a third party before being released weekly for consumption. Great care goes into identifying duplicate reports.
2) the appetite of Big 3 and FDA/CDC to willfully inflict harm on the public knows no end. If data shows ongoing toxicity in new batches and the data IS being monitored and used by B3 et al, there must be a tremendous confidence in the shield provided by liability regulations. It suggests entities operating entirely outside societal structures like law and morality. This in itself is something new? I have not spent time researching previous examples of vaccine malfeasance (I am a only layperson … mom-of-five unwittingly turned covid-analyst) but from a distance they seem more “bounded” namely by strictly bounded victim pools (ie the military, impoverished children of a particular region in India etc)
As an aside, this is a paper I refer to regularly to understand the strengths and weaknesses of VAERS. It also argues an URF of 34. To dismiss VAERS as irrelevant, the position of mainstream media and officials, is absolutely incorrect.
Here is so far the complete batch nos. With their toxicity www.howbad.info
1: Could the lot variation be the product of systemic under-recording, where to muffle the signal many batches were capped? I imagine capping all batches would disillusion honest staffers who knew reports went up but didn't see it represented in the data, so perhaps a way of attenuating AE presentation systematically but still presenting an increase would be to allow true figures for only a subset of the batches?
2: It appears from some of the videos that the toxic batches were sent all over, but the benign ones weren't. Isn't that the sort of impression that might simply just be the result of all batches being sent all over, but only those with a high number of reports having reports associated with most states?
Robin Monotti and Dr Mike Yeadon mentioned your post.