Official data suggests certain batches of Covid-19 Vaccine were intentionally poisoned as just 5% of batches are responsible for 100% of Vaccine Deaths [Denninger stat analysis]

What originally got my attention was the tinfoil hat crowd screaming about lots being intentionally distributed to certain people to kill them — in other words certain Covid-19 vaccine lots were for all intents and purposes poisoned. That was wildly unlikely so I set out to disprove it and apply some broom handles to the tinfoil hatters heads. What I found, however, was both interesting and deeply disturbing.

Lots are quite large, especially when you’re dealing with 200 million people and 400 million doses. Assuming the lots are not preferentially assigned to certain cohorts (e.g. one goes to all nursing homes, etc) adverse reactions should thus be normally distributed between lots; if they’re not one of these things is almost-certainly true:

Now let’s talk about VAERS. You can grab the public data from it, but VAERS intentionally makes it difficult to discern differences in lot outcomes. Why? Because they separate out the specifics of the vax (the manufacturer, lot number, etc.) into a different file. This means that simply loading it into Excel does you no good and attempting to correlate and match the two tables in Excel itself is problematic due to the extreme size of the files — in fact, it blew Excel up here when I tried to do it. But that’s an external data-export problem; internally, within HHS, it is certainly not hard for them to run correlations.

Indeed the entire point of VAERS is to find said correlations before people get screwed in size and stop it from happening.

Let’s step back a bit in history. VAERS came into being because back in the 1970s the producers of the DTP shot had a quality control problem. Some lots had way too much active ingredient in them and others had nearly none. This caused a crap ton of bad reactions by kids who got the jabs and parents sued. Liability insurance threatened to become unobtanium (gee, you figure, after you screw a bunch of kids who had to take mandatory shots?) and thus the manufacturers pulled the DTP jab and threatened to pull all vaccines from the market.

Congress responded to this threat of intentional panic sown by the pharmaceutical industry by giving the vaccine firms immunity and setting up a tax and arbitration system, basically, to pay families if they got screwed by vaccines. Rather than force the guilty parties to eat the injuries and deaths they caused Congress instead exempted the manufacturers from the consequences of their own negligence and socialized the losses with a small tax on each shot.

Part of this was VAERS. We know VAERS understates adverse events because it while it is allegedly “mandatory” it is subject to clinical judgment and there is a wild bias against believing that these jabs, or any jab for that matter, has bad side effects. In addition there is neither a civil or criminal penalty of any kind for failure to report. We now know some people who have had bad side effects from the Covid-19 jabs have shown up on social media after going to the doctor and then tried to find their own record, which is quite easy to do if you know the lot number from your card, what happened and the date the event happened — their doctor never filed it. This does not really surprise me since filing those reports takes quite a bit of time and the doctor isn’t paid for it by the government or anyone else, so even without bias there will be those who simply won’t do the work unless there are severe penalties for not doing so. There are in fact no penalties whatsoever. The under-reporting does not have a reliable boundary on it, but estimates are that only somewhere between 3% and 10% of actual adverse events get into the database. That’s right — at best the adverse event rate is ten times that of what you find in VAERS.

But now it gets interesting because VAERS exports, it appears, were also set up, whether deliberately or by coincidink, to make it hard for ordinary people to find a future correlation between injury or death and vaccine lot number.

NOTE THAT THIS EXACT CIRCUMSTANCE — THAT MANUFACTURERS HAD QUALITY CONTROL PROBLEMS ORIGINALLY — IS WHY VAERS EXISTS. YOU WOULD THINK THAT IF CONGRESS WAS ACTUALLY INTERESTED IN SOLVING THE PROBLEM THIS WOULD BE THE EASIEST SORT OF THING TO MONITOR AND WOULD BE REGULARLY REPORTED. YOU’D ALSO THINK THERE WERE STRONG CIVIL AND EVEN CRIMINAL PENALTIES FOR NOT REPORTING ADVERSE EVENTS.

You’d be wrong; the data is across two tables and uncorrelated as VAERS releases it and there is no quick-and-easy reporting on their site that groups events on a comparative basis by lot number. While it is possible to do this sort of analysis from their web page it’s not easy.

(Further, and this also intentionally frustrates analysis, VAERS keeps no record nor reports on the number of shots administered per lot, making norming to some stable denominator literally impossible. If you think that’s an accident I have a bridge for sale. It’s a very nice bridge.)

But, grasshopper, I have Postgres. Indeed if you’re reading this article it is because I both have it and know how to program against it; this blog is, in fact, stored in Postgres.

Postgres, like all databases, is very good at taking something that can be foreign-key related and correlating it. In fact that’s one of a database’s prime strengths. Isn’t SQL, which I assume VAERS uses as well, wonderful?

Having loaded the base table and manufacturer tables related by the VAERS-ID I ran this query:

karl=> select vax_lot(vaers_vax), count(vax_lot(vaers_vax)) from vaers, vaers_vax where vaers_id(vaers) = vaers_id(vaers_vax) and died=’Y’ and vax_type=’COVID19′ and vax_manu(vaers_vax)=’MODERNA’ group by vax_lot(vaers_vax) order by count(vax_lot(vaers_vax)) desc;

Select the lot, and count the instances of that lot, from the VAERS data where the report ID is in the table of persons who had a bad reaction, said bad reaction was that they died, where the vaccine is a Covid-19 vaccine and where the manufacturer is MODERNA. Order the results by the count of the deaths per lot in descending order.

vax_lot | count
-----------------+-------
039K20A | 87
013L20A | 66
012L20A | 64
010M20A | 62
037K20A | 49
029L20A | 48
012M20A | 46
024M20A | 44
027L20A | 44
015M20A | 43
025L20A | 42
026A21A | 41
013M20A | 41
016M20A | 41
022M20A | 41
030L20A | 40
026L20A | 39
007M20A | 39
013A21A | 36
011A21A | 36
031M20A | 35
032L20A | 35
010A21A | 33
011J20A | 33
030A21A | 33
028L20A | 32
011L20A | 32
004M20A | 32
025J20-2A | 31 << — What’s this? (see below)
041L20A | 31
011M20A | 31
031L20A | 30
032H20A | 29
030M20A | 28
042L20A | 27
Unknown | 27
006M20A | 27
012A21A | 25
002A21A | 25
043L20A | 24
032M20A | 24
023M20A | 23
040A21A | 23
027A21A | 23
017B21A | 22
036A21A | 20
unknown | 19
020B21A | 19
047A21A | 19
006B21A | 18
044A21A | 17
038K20A | 17
048A21A | 15
003A21A | 15
014M20A | 15
031A21A | 15
031B21A | 15
021B21A | 15
025A21A | 14
007B21A | 14
003B21A | 14
001A21A | 13
038A21A | 13
025B21A | 13
001B21A | 12
046A21A | 12
027B21A | 11
045A21A | 11
038B21A | 11
025J20A | 11
002C21A | 11
016B21A | 11
036B21A | 11
039B21A | 10
002B21A | 10
018B21A | 10
019B21A | 10
008B21A | 10
029K20A | 10
029A21A | 10
028A21A | 9
047B21A | 9
001C21A | 9
044B21A | 8
045B21A | 8
009C21A | 8
048B21A | 8
026B21A | 8
UNKNOWN | 7
039A21A | 7
040B21A | 7
046B21A | 7
032B21A | 7
038C21A | 6
030m20a | 6
027C21A | 6
008C21A | 6
006C21A | 6
004C21A | 6
047C21A | 6
007C21A | 5
025C21A | 5
042B21A | 5
043B21A | 5
025J202A | 5 << — Same as the above one?
052E21A | 5
003C21A | 5
030B21A | 5
030a21a | 5
016C21A | 5
017C21A | 5
N/A | 5
NO LOT # AVAILA | 5
037A21B | 5
037B21A | 5
024m20a | 4
031l20a | 4
003b21a | 4
026a21a | 4
041B21A | 4
005C21A | 4
033C21A | 4
035C21A | 4
021C21A | 4
040a21a | 4
041C21A | 4
006D21A | 4
022C21A | 4
037k20a | 4
048C21A | 4
03M20A | 3
008B212A | 3
039k20a | 3
024C21A | 3
016m20a | 3
038k20a | 3
025b21a | 3
033B21A | 3
026C21A | 3
Moderna | 3
033c21a | 3
014C21A | 3
…..

There are 547 unique lot entries that have one or more deaths associated with them. Some of the lot numbers are in the wrong format or missing, as you can also see. That’s not unusual and in fact implicates the ordinary failure to get things right when people fill out the input. For example “Moderna” in the above results is clearly not a lot number. I’ve made no attempt to “sanitize” the data set in this regard and, quite-clearly, neither has VAERS even months after the fact with their “alleged” follow-up on reports.

But there is a wild over-representation in deaths of just a few lots; in fact fewer than 50 lots account for all lots where more than 20 associated deaths accumulated and out of the 547 unique entries fewer than 100 account for all those with more than 10 deaths.

vax_lot | count
-----------------+-------
EN6201 | 117
EN5318 | 99
EN6200 | 97
EN6198 | 89
EL3248 | 86
EL9261 | 84
EM9810 | 82
EN6202 | 75
EL9269 | 75
EL3302 | 69
EL3249 | 67
EL8982 | 67
EN6208 | 59
EL9267 | 58
EL9264 | 57
EL0140 | 54
EN6199 | 54
EJ1686 | 51
EL9265 | 50
EL1283 | 48
ER2613 | 48
EN6204 | 47
EN6205 | 45
EK9231 | 43
EL3246 | 43
EN6207 | 41
EN6203 | 41
ER8732 | 40
EL1284 | 39
EL0142 | 38
EJ1685 | 38
ER8737 | 37
EN9581 | 36
EN6206 | 35
EP7533 | 35
EL9262 | 34
EL9266 | 33
EL3247 | 32
ER8727 | 28
EP6955 | 27
ER8730 | 26
EW0150 | 25
EK5730 | 24
EP7534 | 24
EM9809 | 22
EK4176 | 22
EH9899 | 21
EW0171 | 21
unknown | 20
ER8731 | 19
ER8735 | 18
EW0172 | 18
EL9263 | 17
EW0151 | 15
ER8733 | 15
EW0158 | 14
EW0164 | 14
EW0162 | 14
EW0169 | 14
ER8729 | 13
ER8734 | 13
Unknown | 13
EW0153 | 13
EW0167 | 12
EW0168 | 10
EW0161 | 10
EW0182 | 9
NO LOT # AVAILA | 8
EW0181 | 8
EW0186 | 8
ER8736 | 8
EW0191 | 8
FF2589 | 7
EW0173 | 6
EW0175 | 6
FA7485 | 6
EW0177 | 6
FD0809 | 6
301308A | 6
EW0170 | 6
FC3182 | 6
EW0217 | 6
EK41765 | 5
EW0196 | 5
EW0176 | 5
EW0183 | 4
EN 5318 | 4
el3249 | 4
EW0178 | 4
EW0179 | 4
EW0187 | 4
FA6780 | 4
FA7484 | 4
EN 6207 | 4

Pfizer has 395 unique lot numbers associated with at least one death and, again, there are a few that are obviously bogus. But again, normal distribution my ass; there is a wild over-representation with one lot, EN6201, being associated with 117 deaths and fewer than 20 are associated with more than 50.

For grins and giggles let’s look at the age distribution for 039K20A — the worst Moderna lot.

karl=> select avg(age_yrs) from vaers, vaers_vax where vaers_id(vaers) = vaers_id(vaers_vax) and vax_type=’COVID19′ and vax_manu(vaers_vax)=’MODERNA’ and vax_lot(vaers_vax)=’039K20A’ and age_yrs is not null;
avg
———————
51.4922202119410700
(1 row)

Ok, so the average age of people who got that shot, had a bad reaction (and had a valid age in the table) is 51.

Well there goes the argument that we jabbed all the old people in nursing homes with the really nasty outcome lot and they died but it not caused by the jab and the second lot, which had a much lower rate, all went into younger people’s arms and that’s why they didn’t die. Uh, no, actually when it comes to the age of the people who got jabbed in these two instances its the other way around; the second lot, which was less deadly, had bad reactions in older people on average yet fewer died — and significantly so too (by 10 years.)

In addition there is no solid correlation between the “bad” lots and first report of trouble. The absolute worst of Moderna had a bad report in the first days of January. But — another lot of their vaccine with only 172 reports against it (1/20th the rate of the worst for total adverse events) had its first adverse event report on January 6th.

What the actual **** is going on here? 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) and 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.

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?

Here, in pictures, since some of you need to be hit upside the head with a ****ing railroad tie before you wake up:

That’s Pfizer deaths by lot, worst-to-best. Look normal to you? Remember, zero deaths in a given lot doesn’t come up since it’s not in the system.

(Yes, there are invalid lot numbers, particularly in the second graph, with lots of “1s”. The left side however is what it is.)

There’s a much-larger problem. Have a look at Moderna’s chart of the same thing. First, deaths:

Is there something inherent in the production of the “instructions”, however they’re delivered, that results in a non-deterministic outcome within a batch of jabs which was not controlled for, perhaps because it isn’t understood SINCE WE HAVE NEVER DONE THIS BEFORE IN MAN OR BEAST and if it goes wrong you’re ****ed?

This is a power-law (exponential) distribution; it is not a step-function nor normally distributed. Those don’t happen with allegedly consistent manufacturing processes and the potential confounding factor that could be an innocent explanation (all the bad ones were early and killed all the old people early who died of “something” but it wasn’t the vaccines since they all got the jab first) has been invalidated because the dates of death are in fact reasonably distributed.

Have doctors been told to stop reporting? Note that HHS can issue such an order under the PREP Act and there is no judicial review if they do that. Did they?

This demands an explanation. Three different firms all using spike proteins, two using a different technology than the third, all three causing the body to produce the spike rather than deliver it directly and all three of them have a wild skew of some lots that hose people left and right while the others, statistically, do not screw people.

This data also eliminates the hypothesis put forward that lack of aspiration technique is responsible — that is, that occasional accidental penetration of a vein results in systemic distribution. That would not be lot-specific.

Next question, which VAERS cannot answer: Is there an effectiveness difference between the lots that screw people and those that do not?

Are we done being stupid yet? Statistically all of the adverse events of any sort are in a handful of lots irrespective of the brand. The rest generate a few bad outcomes while a very, very small number of lots generate a huge percentage of the harm. And no, that’s not tied to age bracketing (therefore who got it first either); some of the worst have average age distributions that are less than lots with lower adverse event rates. It is also not tied to when used either since one of the “better” lots has a first-AE report right at the start of January — as do the “bad” lots.

The only thing all three of these vaccines have in common is that all three of them rely on the human body to produce the spike protein that is then attacked by the immune system and produces antibodies; none of them directly introduce the offending substance into the body. The mechanism of induction is different between the J&J and Pfizer/Moderna formulations but all exhibit the same problem. The differential shown in the data is wildly beyond reasonable explanation related to the cohort dosed and the reported person’s average age for the full set of events (not just deaths) does not correlate with elevated risk in a given lot either so it is clearly not related to the age of the person jabbed (e.g. “certain lots all went to nursing homes since they were first.”) While the highest AE rate lots all have early use dates so do some of the low-AE rate lots so the attempt to explain the data away as “but the highest risk got it first” fails as well.

In other words the best-fit hypothesis is that causing the body to produce part of a pathogen when that part has pathological capacity (as we know is the case for the spike) cannot be controlled adequately through commercial manufacturing process at-scale. This means that no vector-based, irrespective of how (e.g. viral vector or mRNA), not-directly-infused coronavirusjab will ever have an acceptable safety profile because some lots will be “hot” and harm crazy percentages of those they’re given to with no way to know in advance. The basic premise used here — to have the body produce the agent the immune system identifies rather than directly introduce it where you can control the quantity, is a failure.

The entire premise of calling something that does this a “vaccine” is bogus and in the context of a coronavirus this may never be able to be done safely.

Something is very wrong here folks and the people running VAERS either aren’t looking on purpose, know damn well its happening and are saying nothing about it on purpose — never mind segregating the data in such a fashion that casual perusal of their downloads won’t find it — or saw it immediately and suppressed reporting on purpose.

If these firms were not immune from civil and even criminal prosecution as a result of what Biden and Trump did the plaintiff’s bar would have been crawling up *******s months ago.

This ought to be rammed up every politician’s ass along with every single person at the CDC, NIH and FDA. They know this is going on; it took me minutes to analyze and find this.

THESE SHOTS MUST BE WITHDRAWN NOW until what has happened is fully explained and, if applicable, accountability is obtained for those injured or killed as a result. If embargoing of reports is proved, and its entirely possible that is the case, everyone involved must go to prison now and the entire program must be permanently scrapped.

THERE IS NO REASONABLE EXPLANATION FOR THIS DATA THAT REDUCES TO RANDOM CHANCE.

That's a great idea, I'll see what I can pull together.

Here is what I am planning. I welcome suggestions:

Fundamentally I want to look into the distribution of "adverse outcomes" (death or hospitalization) based on manufacturer and lot.

Is one vaccine better or worse than the others? For this I'll graph total deaths, broken out as Moderna, Pfizer, or Jansen. That in itself doesn't tell us too much. We really need the death rates. For example, as of 28 Oct in the US there have been 245,237,611 Pfizer doses given, 156,602,727 Moderna, and 15,521,154 Jansen. Since the mRNA vaccines are two dose, I'll have to cut those numbers in half. Yes, that makes an assumption that most people who got at least one of the mRNA doses finished, and that there was negligible crossover. I'm also discounting booster doses.

Is there more or less variability in vaccines? For this I'll look at total bad outcomes by lot. Then I'll get the standard deviation of the numbers. If the bad outcomes are relatively random, then the lots should be relatively even in bad outcomes and the standard deviation across lots should be relatively small. Granted, I don't know if all lots are the same size. I don't know if some are still in use and thus data is incomplete. We also don't know the distribution - did some got to relatively high risk areas (eg. older population, less healthy population, etc.) So this may not tell us much for any one vaccine (Moderna/Pfizer/Jansen) but it will tell us if one stands out from the others good or bad.

I'd like to know the relative rates of bad outcomes compared to other vaccines. For that I'll total all the covid vaccines data, get a rate based on total vaccinated people. Then I'll pull the reports for the other vaccines and see if I can find or estimate total vaccines of those types given. Looking to see if the covid vaccines are better, worse, or about the same for rates of bad outcomes. I suspect they are worse - merely because they are new, rushed into use, and not refined or well understood. They are being recommended to everyone without regard (or knowledge, yet) of who should avoid them.

Then I want to look at the time delta between vaccination and onset of the bad outcome. This is available as days post vaccine. I'll pull out min, max, mean, and standard deviation. Mostly I want to see what percentage of bad outcomes occur within the 14 day window where the patient is considered "not fully vaccinated." Because you know those patients were admitted as un-vaxxed, and almost certainly counted as covid patients since they'd have antibodies. I just want to confirm that the 14 day window is a gift to the pro-vax types encompassing virtually all bad outcomes, accounting for them as un-vaxxed.

Per the suggestion on inferring lot size I'll see if there is data available. I think it would also be interesting to get the date range of bad outcomes associated with each lot. That might also suggest how big the lot is/was - bigger lots would be in use longer while smaller ones would be exhausted quicker. Of course that assumes roughly equal rates of delivery. Maybe useful, maybe not. But when you're just starting out analyzing a pile of data, you poke around and look at anything interesting to see if it is useful.

Finally, I'll see if I can do these for both deaths (ultimate bad outcome) and "mere" ER visits or hospitalizations.

Fair amount of grovelling through data, but this is what I do. Heck, the base data file is only about 500 MB and a little over 600K entries. In my "day job" I routinely work with double-digit GB data sets with millions of records. Of course there I have a 64 core machine with 256 GB of memory and a RAID disk subsystem. I'm going to try this on my Raspberry Pi just for kicks... ;-)

When I looked a few months ago, I think two vaccines had a similar rate of AE, and the other was different. The difference was big enough to look significant.

If there is a cumulative effect to the vaccines, the boosters might have higher AE rates. The boosters may not have been around long enough to have enough data.

Somebody ought to have a list of lot sizes. And knowledge of the manufacturing process. Whether they’ll talk is another story.

I did a rough and conservative estimate of the AE rate for these vaccines compared to other vaccines. If I remember correctly, I had 4 billion doses of the other vaccines. The Covid vaccines had AEs 6 times as often, which makes them less safe than the others.

I found clumping of AEs close to vaccination date. I focused on the first week. I would expect reporting to diminish over time, but the clumping was pronounced.

My Windows 7 machine was laboring with the Excel file I had.

I’d be delighted to hear what you come up with.

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