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Hi Jessica,

I looked at this a few months ago. I went through the moderna deaths, for which the lot coding is pretty simple, and manually corrected most of the data entry errors. I ended up dropping a few that were garbage but it was probably not more than 10 lot codes that didn't resolve.

Here is the CSV file if you are curious:

https://files.catbox.moe/kah452.csv

cols are

year of manufacture,

month of manufacture (based on parsing the lot code),

lot within month,

original lot code,

vaers death count (as of sometime over the summer i think)

I then plotted the distribution of counts for each lot, sorted by ascending count.

heres' the plot:

https://files.catbox.moe/8okcyc.png

Now, if these were random occurrences, this distribution should be a normal distribution. But, it appears to be exponential (see the second figure which plots the log of the count).

This tells me something is wrong here.

It could be bias in the reporting. Maybe small towns with lower counts report with higher frequency than large cities with too many to report. There could be other bias against reporting that is not uniform. Perhaps as time went on fewer reports were made because it was more "normalized" or there were increased efforts to stifle it. Or it could be backlog of reporting.

If you plot the data as a function of manufacture date, there are fewer death reports for newer product.

This could be because there are more "fallow" vials out there for newer product.

It could also be problems in manufacturing or distribution / cold chain.

It could be that a manufacturing problem did exist and got worked out after the first people started dying - the plot ordered by manufacture date shows an improvement very shortly after the rollout started. That is, maybe they saw an issue and fixed something but almost half the product had already gone out the door (that is, the drop is correlated to manufacture date, NOT injection date)

From my view, THERE IS A PROBLEM.. what it is exactly is less clear, but so far there a lot of possible answers and none are particularly good.

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Expiration dates could be tied to supply chain. So if “Substrate X” gets sent out to 5 vendors and, due to different rates of meeting quality benchmarks, they finish cooking their lots at different dates, the expiration date is still pinned to X.

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Jan 13, 2022·edited Jan 13, 2022Liked by Jessica Rose

Great and highly diligent work as always. Numbers as text, zeroes as the letter "O" , missing values, leading spaces etc. are all problems we have seen with other data sets. These take dozens of hours to clean up as Jessica has tried to do. The quasi-vax manufacturers do seem to have some major problems with stability, which could account for why some lots may be associated with slightly FEWER number of AEs etc., but in the absence of the full details of batch sizes, distribution etc., we cannot fully know ths. The important message from Jessica is a caution that one should not jump to conclusions that 5% of the lots cause 100% of the problems or that the data prove the existence of some dastardly plan. Stick to the science as Jessica is doing. Thank you again Jessica.

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Thank you! This makes more sense than anything else I have read on the subject so far. I have no way to contact you privately, and I am not looking for anything in return, but I am a semi-retired database developer experienced in cleaning government health data in order to work with it relationally (ETL processes). Contact me if there is any way I can help.

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Jan 13, 2022·edited Jan 14, 2022Liked by Jessica Rose

I work in medical devices so am somewhat familiar with lot dating -- at least in devices. You could have lots manufactured on different dates that are queued for sterilization at the same time. In devices, you could have a DOM (date of mfr) -- sometimes that is used to calculate expiration date (based on logic Expiry = DOM plus Shelf life days). Some times you can have an expiry that is based on Date of sterilization - so the label may list DOM, but the system the label pulls from calculates expiry as Expiration Date = Date of Sterilization + Shelf life in days. In med devices, they don't degrade so we use sterility as the driver of when they expire (for example large pieces of capital equipment that aren't sterilized like lasers don't have expiration date). But with a drug, I would think sterility is not the key concern and you should have chemical/material degradation concerns that drive true product expiration - meaning your Expiration should consistently by DOM + shelf life (based on validation that would have been included in submission)

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Jan 13, 2022Liked by Jessica Rose

This is very interesting and intriguing post. Are you working with Dr Mike Yeadon? Great questions and wondering if you have considered the way these lots are stored and administered once they’ve been delivered. And are these vaccines being administered exactly the same per injection (I.e. CVS delivers precisely the same amount in an arm as Rite Aid? Doubtful). Anyway, look forward to your follow up

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Good grief! Ballzzz! What am I doing here sleepless at 0300 hrs Alaska time reading, that is attempting to comprehend the magnitude of the crime of the century (so far) ? I think because I'm hooked as the extent of this tragic farce becomes factually clearer due to your amazing skill set. You are the protagonist in a crime novel in the truest sense of the genre and these updates are right next to vicariously being there on the scene, even before the police tape closes off the area. Jayzus H! I might be reclining next to my snoring cat but I'm on the edge of my seat nonetheless!

My guess as to Rochie's response to your Top Ten?

"Um, this may sound, I mean, car crashes and...(breaking down now).... well... (long pause)

My dog ate the requested data. No! Really!" That is the extent of her credibility. Methinks she fidgets too much. Always.

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Very comprehensive; thanks for your work on this!

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Jan 13, 2022Liked by Jessica Rose

Jessica just checking if you have the Moderna lot file expiry.json, from their expiration lookup tool?

https://lnl-dl.s3.amazonaws.com/Moderna/expiry.json

In looking at their deaths by lot number, I see the deaths tied more to an early time frame. Maybe when the most at-risk were getting their second dose.

The other crazy thing with VAERS though, if you follow it day to day, is the data doesn't come in FIFO. Why, for instance does new data contain events from across the entire time range? How are they processing the events and what is the back log?

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Jan 13, 2022·edited Jan 14, 2022Liked by Jessica Rose

Also -- based on Code of Federal regulations, product traceability has to be absolutely guaranteed. from a med device standpoint, we can trace all mfg data for a product given a lot/part number combination. That should be maintained and quickly determinable (in the event of a recall) and is a requirement of devices and I assume drugs. Required traceability is not just mfg location, it's also specific equipment, specific employees that assembled it, inspections, testing results, etc. Basically the entire lot history record has to be available in the event of audit or need to analyze product quality signals to support a recall.

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I’d also be curious how the lots were distributed. For example, a lot may have 6000 vials, but did all 6000 vials get sent to the same site? Did only 4000 vials go out and 2000 remain in the warehouse?

I’ve also seen a theory that the “later” dated lots’ adverse events may suffer from data suppression. The CDC caught on after a few months and started suppressing the AE reports.

Thanks for all you do!

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Given how awful the entry process is reported to be by multiple docs, and the general state of gov info systems, I can't say I'm surprised. GIGO.

As others have noted, without data as to quantities and distribution of lots it could be anything or nothing. I'm reluctant to give it much weight.

Frankly, to my mind it's plenty clear these treatments are a seriously bad idea as designed. I don't see much need to crawl down this particular rabbit hole.

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I wonder which VAX LOT Fauci got.

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Dr. Jane Ruby says that hackers got more information regarding batches. She said that the batches appeared to be coordinated and she believes that they represent testing usually done on animals before human trials. That is, one set of batches were for what she called “Lethal dose testing,” where they kill %50 of animals to find an upper limit and then work their way down to human testing, and the next set represented randomized trials with placebos. She also said that Red states in the US were given more toxic batches than Blue states. Here is the link. The batches are discussed starting at about 40:25 through 50:1. https://rumble.com/vsubzc-blood-deceit-and-exposure.html

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Great work, even though it complicates the picture relative to the Daily Expose article series on the subject. I think perhaps the best thing to do is analyze a significant number of the dud lot #s to see if in fact they are just saline. The work of Drs Young, Madej, Botha, La Quinta Columna et al would then have some background context.

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can you share your code? looks like ggplot in R? maybe myself and others can chip in on a repo.

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