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COVID-19 Vaccine Harm Data Reveals Concerning Patterns

Pfizer and Moderna COVID-19 vaccines seem to have four levels of harm intensity that are based on batch number, says former pharmaceutical executive, Sasha Latypova.

“They’re dependent on the letters used in the alphanumeric numbering, which should be just random,” Latypova told The Epoch Times.

“We have variability of toxicity, but it’s also aligned in three patterns—high, medium, and low. And the fourth one, something as apparent as placebo.”

She cited two publications that point to this conclusion.

One was a research letter submitted by Danish scientists to the peer-reviewed European Journal of Clinical Investigation. Max Schmeling, lead author of the letter, used data obtained from the Danish government to map out Pfizer’s mRNA COVID-19 vaccine batches versus the adverse events they caused.

Schmeling found that the batches fell into three distinct levels of harm. There were nine “high” batches in Denmark that caused thousands of suspected adverse events per batch (blue). There were 25 “medium” batches that were less toxic (green). And there were 12 “low” batches that seemed to cause very few adverse events (yellow).

A French genomics statistician, Herve Seligmann, tried to reproduce this study using a grassroots database that matched adverse events to batch numbers. He got similar results.

The Danish scientists also noted that some batches caused adverse events that were severe, while other batches had milder adverse events. Larger vaccine batches tended to have lower rates of adverse effects than smaller batches. So when more doses were produced in a single batch, the rate of adverse effects per 1,000 doses was generally lower.

This is a highly unusual situation. According to the standards set by the U.S. Food and Drug Administration, a pharmaceutical product should not have a high variation of adverse events from batch to batch.

Both Moderna and Pfizer COVID-19 vaccines have failed to meet this safety standard, said Latypova. In addition to all this, the figure above shows three clean trendlines, with no noise in between.

“Meaning that this is not actually random, it’s by design,” said Latypova.

In other words, the differences in the rates of adverse events are not likely to be random, but rather point to underlying factors related to the vaccine batches themselves.

Latypova is an expert in drug safety and manufacturing. She has been independently monitoring COVID-19 vaccines since their rollout using publicly available data.


Interview trailer:

Watch the full interview:



Editor’s note: Current Good Manufacturing Practice (CGMP) is a set of standards and regulations enforced by the U.S. Food and Drug Administration (FDA) to ensure the quality, safety, and efficacy of pharmaceutical products.

Sasha Latypova: I looked at the data, not just the total number of adverse events or deaths, but also [at] the pattern of the data across manufacturing lots or batches. So all pharmaceutical products are manufactured in lots of batches. The lots are numbered. If you go to the pharmacy and buy Advil, you can look at the box, it has a lot number.

So those are the lot numbers that can be recorded in the [Vaccine Adverse Event Reporting System (VAERS)] reports—when people submit their VAERS reports. And they’re not always there, because people who are submitting the reports—which are either patients, their relatives, pharmacists or doctors, healthcare professionals or nurses—they don’t always have that information. So you may not remember what was the batch number you were injected with—you don’t have your card with you, or the physician doesn’t know.

But in about 50 percent of the reports, those numbers are there. And so I was able to match those lot numbers with the Centers for Disease Control (CDC) lot numbers—I have a list of them. And I was able to see that not only the adverse events were high, but the variability of them by batch was extreme. Absolutely extreme.

Jan Jekielek: Explain that to me, the variability among them.

Variability of Toxicity

Ms. Latypova: So the variability … meaning how many total reports are submitted for a particular batch number. Some batch numbers had two or three reports. And some had five or six thousand. That should never happen. If you think about it, if you’re buying Advil today, and you’re buying it a month from now, your experience a month from now shouldn’t be 1,000 percent different, because that’s really dangerous.

Even if you think Advil is a safe medication or aspirin is a safe medication, or even if you’re buying orange juice, and your experience today and a week from now is 1,000 percent different—that’s going to be very dangerous. In one of those instances it is very dangerous.

So when you see a variability like this, between batches of what is supposed to be a consistently produced, Good Manufacturing Practice-compliant product—it means the product is not CGMP compliant.

And as a comparator—because I didn’t have a baseline for this, I didn’t have information, for example, of the batch sizes at the time. I do now, and the story doesn’t change—but at the time I didn’t, so I thought I should compare it to something known. That something known was the flu vaccines.

So I extracted all the data for flu vaccines from VAERS, and I looked at how flu vaccine data looks from batch to batch over a long period of time. And by the way, there’s more variety of producers making different sizes of batches. There are dozens of manufacturers of flu vaccines historically, and different sizes of batches.

And for flu vaccines, it looks as expected for a CGMP-compliant product, which is a straight line, very close to zero—all the batches line up and there’s very little variability, just a tiny bit.

And then when you compare them to these COVID shots—you can’t even put them on the same graph! Flu vaccine data disappears, because the COVID shots variability is huge. Absolutely huge, enormous.

Four Levels of Harm

Mr. Jekielek: You also have talked about how there’s variation between the batches. In some interpretations it might almost look deliberate. I found that to be very strange.

Ms. Latypova: In addition to seeing, first of all, huge adverse events and death rates overall, we saw huge variability batch to batch. But that variability was also not random. Meaning, it’s not that they don’t control their process and they’re producing random outputs. They’re also producing something not random.

There’s some sort of a design going on as far as what’s in those vials and different batches, because we saw clusterings, for example, by alphanumeric codes—both in Pfizer and Moderna. Depending on the letters used in the alphanumeric numbering—which should be just random, it should be just some sort of a manufacturing scheme to keep track of things—but depending on the letters, we knew that this set of letters would produce higher toxicity, and this set of letters would produce lower toxicity. That should never happen.

We also had clusterings by dates of manufacture. [That] also should not happen—you should not have a difference between the product on the first of the month or on the thirtieth of the month.

A colleague of mine from Denmark—this was recently published in a peer-reviewed journal, the European Journal of Clinical Investigation. He obtained data from the Danish government—Denmark was Pfizer-exclusive. They got all the batch numbers for Pfizer, and the adverse event data. And he was able to find three different patterns of toxicity. And they’re cleanly separated from each other. Again, we have the variability of toxicity, but it also aligns in three patterns. And there’s nothing in between, meaning that this is not actually random, it’s by design. By design they have three different levels—high, medium, and low. And the fourth one—something as apparent as placebo, because those people who were injected and don’t submit any reports, they don’t have any adverse events.

So we have at least four different levels of toxicity detected in the data from adverse events. And again, this should not happen—this should be explained, there should be an investigation.

No Consumer Protection Protocols Were Triggered

And by the way, everything I’m seeing—once you see some variability of data like this, this huge variability of the clusterings of data by various parameters—there are numerous different safety protocols that should be triggered at the manufacturer [level]. The manufacturers themselves should detect it, the manufacturers should recall these products, they should stop the production lines and start investigations. That’s what they do normally.

If the manufacturer is not doing anything about it, then the FDA has all sorts of enforcement powers. They can also force a recall, force investigations—we have all kinds of levels of protections, consumer protections, that should be triggered with this picture, long before this extent of injury has occurred. Yet none of them were triggered, none of them worked. So all of them fell apart simultaneously. And that kind of kills me. I feel like I’ve been screaming for the last two years at the empty space and nobody’s paying attention to this.

Sasha Latypova will return in Part 2 of this interview.

This transcript has been edited for brevity and clarity by Anna Varavva.


Medical Disclaimer:

Frontline Health (the “Show”), its guests, and contributors provide the latest news on health and medical discoveries, and it is meant for informational purposes only. The Show does not provide medical advice, diagnosis, treatment, cures, mitigation, or prevention for any type of disease or medical condition. Similarly, it is not intended for self-diagnosis or self-treatment of any health-related condition.

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