Why David Gorski is wrong about VAERS

The Case for Vaccine Data Science

I don’t know if David Gorski is a Pharma shill.

But it is possible he is not a shill and also wrong in his analysis of VAERS.

To paraphrase business philosopher Jim Rohn:

“Don’t mistake sincerity for being right. It is possible to be sincerely wrong”

In this article, I make the case for why David Gorski is wrong about VAERS. Whether he is sincerely wrong, I have no idea.

And it is not even very hard to see how this happened.

The tech community has zero interest in doing serious analysis of vaccine injury data.

Maybe they have been dissuaded because they don’t want to be called an “anti-vaxxer”?

So there is almost no one who is applying state of the art techniques for vaccine data analysis, especially for VAERS.

What is Data Science?

The techniques we can use for the study of large¹ datasets (such as the VAERS CSV files for 2021 and 2022) have improved immensely over the last 10 years or so. This is commonly referred to as Data Science.

Once you start applying these techniques, it becomes much easier to analyze the VAERS CSV files compared to the methods used previously.

And on top of that, the field of text analytics - which allows us to convert unstructured text into structured data - has also improved immensely due to Machine Learning. As a result, it is now possible to do much better analysis of the text writeups included in VAERS reports.

Once you take into account these discontinuous jumps in technological progress, you can see that the current state of vaccine data analysis is many years behind the state-of-the-art.

Findings

So here are some of my findings based on using data science techniques to analyze VAERS reports.

There are a lot of people who use the example of the “Incredible Hulk” VAERS report as if it is representative of VAERS. It is not.

Quantifying "crazy" VAERS reports

Personally, I think people who are citing the Incredible Hulk anecdote are using it as a replacement for thinking.

But there are also much more substantial criticisms of using VAERS to monitor for vaccine safety, and you will find most of them are written by Dr David Gorski.

This is a collection of articles where I have offered rebuttals to David Gorski.

I don’t have any training in medicine or biology, but I do have some experience with Data Science and I can use my skills to verify the VAERS claims made by David Gorski and others who write on his website.

My main takeaway from writing this series of articles is that people who are doing VAERS analysis should be using more data science principles, and there definitely has to be a lot more work done on text analysis of VAERS writeups.

Part 1

Should you use Excel or R for VAERS analysis?

Part 2

Is litigation a source of over-reporting for COVID19 vaccines?

Part 3

Is the CDC lying about deleted VAERS reports?

Part 4

Did v-safe reduce the under-reporting rate for severe adverse events for COVID19 vaccines?

Part 5

How v-safe INCREASED the under-reporting rate for COVID19 vaccines

Part 6

CDC's v-safe text mining is laughable

Part 7

Should we accept the "background death rate" explanation at face value?

Part 8

Should "anti-vaxxers" cite VSD, PRISM or CISA instead of VAERS?

Part 9

If VAERS is merely "hypothesis-generating", why do we even need IRB protocols?

Part 10

Quantifying the effect of VAERS delayed reports

1. I can imagine some programmers chuckling when I say 1M records is “large”. But it is large if the main tool in your toolbelt is Excel. And most VAERS data analysis you see today is still mainly based on Excel, which only reiterates my main point.