Russian Roulette? Pfizer Moderna Lots Confirmed Inconsistent


We reported recently about article that analyzed VAERS reports (Vaccine Adverse Events Reporting System run by CDC/FDA) based on lot numbers to explore whether adverse events and deaths were or were not consistent among the lots and which states those lots were shipped to.

The results were of course shockingly NOT consistent!

But when a story like this breaks and there is only one source for the data that's when some folks understandably get skeptical. It's healthy skepticism.

One of those individuals, lucky for us, is a tech savvy coder type who is very advanced with database queries and he wrote some code to run his own analysis on the VAERS data figuring he would debunk the story. He posts under the name Karl Denninger and maybe that's his real name but what he found was equally astonishing.

He confirmed the report from !

In his report before digging into the data he brings up important points about how we should look at this situation. There are basically 3 different scenarios which he mentions as to why there would be differentiation between the lots. Logically these would be the three:

  1. A manufacturing consistency problem rendering a misunderstood variable in the large scale production which leads to inconsistency in the lots, some being fairly benign while others have an anomaly which makes them more harmful. It could lead to too much or too little of an active ingredient which may lead to a drastic cytokine reaction or increased production of spike protein or some other problem.
  2. A significant experiment being conducted between various lots in order to test different levels of such active ingredients or run targeted nefarious weapons systems on specific groups of the population possibly in certain areas of the country or even internationally.
  3. The third potential is that indeed these anomalies are actually manufactured into the statistics because the adverse events and death had been reported more accurately previously but are now being suppressed leading to many newer lots showing much lower adverse events simply due to suppressed reporting! Of course in this case the actual adverse events and deaths would likely be consistent with the higher rates shown in the worst lots in these analyses.

So before we get into the VAERS data analysis Karl made a some good points about the history of the VAERS system being set up to identify issues with vaccines like the DTP shots in the 1970s which had similar issues with inconsistencies in the lots and kids were being harmed by specific bad lots. Of course shortly thereafter the 1986 Act gave vaccine manufacturers indemnity from liability and as Karl states rather poignantly:

Congress responded to this by giving the vaccine firms immunity and setting up a tax and arbitration system, basically, to pay families if they got screwed by the vaccines. Rather than force the guilty parties to eat the injuries and deaths they caused they instead exempted the manufacturers and socialized the losses with a small tax on each shot.

He goes on to elaborate on why the VAERS system is well known to be underreporting injuries and deaths from vaccines because it is difficult to file a report in VAERS and that there exists in the medical community a significant bias against vaccines being a cause of harm despite the mountains of evidence. Also, doctors and health providers typically are not paid for the extensive time that it takes to file a VAERS report despite the fact that VAERS reports are supposedly required by law. It's just a shame those laws are not enforced!

Moving onto the meat of the report Karl analyzed the VAERS data even deeper than it seems did and broke down all the possible explanations for the anomalous reporting among the lots.

One explanation that some are proposing is that the "bad" lots all went to elderly at retirement homes but Karl addressed that well by analyzing the average age of the reports in the worst lots and compared that to an average age in a lot with fewer deaths.

In fact what he found was that the average age was higher in the lot with fewer deaths!

He also addressed the idea that the higher death and injury lots were due to the time of distribution being during times of higher infection rates overall in the population but found that to be negated by the data as well.

He found that lots with both high and low numbers in the reporting system correlated with the same month of reporting.

In terms of numbers much of Karl's analysis of the VAERS data showed very similar results as what we've already reported from article:

You’re going to try to tell me that the CDC, NIH and FDA don’t know about this? I can suck this data into a database, run 30 seconds of queries against it and instantly identify a wildly-elevated death and hazard rate associated with certain lot numbers when the distribution of those associations should be normal, or at least something close to it, across all the lots produced and used? Then I look to try to find the obvious potential “clean” explanation (the higher death rate lot could have gone into older people) it’s simply not there when one looks at all adverse event reports. I have Moderna lots with the same average age of persons who died but ten times times the number of associated deaths.

Then I look at reported date of death and…. its reasonably close to a normal distribution. So no, it wasn’t all those old people getting killed at once in the first month. So much for that attempted explanation.

Oh if you’re interested the nastiest lot was literally everywhere in terms of states reporting adverse events against it; no, they didn’t concentrate them in one state or region either.

The outcome distribution isn’t “sort of close” when most of the lots have a single-digit number of associated deaths.

So some lots had incredibly high death and injury rates while others had very low rates of both. The bad lots also had both high death numbers as well as injuries correlated with the same lot numbers so this data does have some logical distribution. The same distribution was seen in Pfizer, Moderna and even the Janssen (J&J) products which represents a different technology!

That makes this rather difficult to explain away...

In Karl's analysis he goes on:

Isn’t it also interesting that when one removes the “dead” flag the same sort of correlation shows up? That is, there are plenty of lots with nearly nothing reported against them. For Moderna within the first page of results (~85 lots) there is more than a three times difference in total adverse events. The worst lot, 039K20A with 87 deaths, is not only worst for deaths; it also has more than 4,000 total adverse event reports against it. For context if you drill down a couple hundred entries in that report the number of total adverse events against another lot, 025C21A number 417 with five deaths.

Are you really going to try to tell me that a mass-produced and distributed jab has a roughly ten times adverse event rate between two lots and seventeen times the death rate between the same two, you can’t explain it by “older people getting one lot and not the other” and this is not a screaming indication that something that cannot be explained as random chance has occurred?

He even graphs the data for further analysis so I encourage you to take a look at Karl's post linked below if you are a visual person. I of course agree with Karl's conclusion that this entire program must be stopped and certain individuals should be on trial for crimes against humanity if it can be proven that this was intentional which is becoming more apparent as more data continues to come out.

Thanks for reading.

Please share this post far and wide and spread this information. Also share your thoughts in the comments below.



Pfizer Image courtesy of: Waleed (@65WZ)

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