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ForkInSocket's avatar

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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Brian Mowrey's avatar

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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