Calculating a bound for NUMDAYS: Summary and Results Part 4/4

I have also created an interactive dataset you can use to verify my analysis

So I combined the ideas from the previous 3 articles

Estimating the bounds for NUMDAYS in VAERS Part 1/4

Estimating the MAX_ONSET_DATE Part 2/4

Estimating the earliest vaccination date Part 3/4

And I wrote a Python script which will use the Jan 2022 dataset and add the following fields to it:

MIN_VAX_DATE - a prediction/calculation of the earliest vaccination date

MAX_ONSET_DATE - a prediction/calculation of the latest onset date

NUMDAYS_BOUND - this is just the difference between the MAX_ONSET_DATE and MIN_VAX_DATE.

Unlike the NUMDAYS field, the dataset will always have a NUMDAYS_BOUND field.

Once I completed the calculations, I uploaded the CSV file to Zoho Analytics.

Then I constructed a Query Table based on the table data for Jan, Feb and Mar 2022 which summarizes the information for reports where the NUMDAYS is empty and NUMDAYS_BOUND is less than 30

You can interact with the results of this Query Table here

Notice that this alone adds nearly 3000 rows into our analysis.

Between them, Jan, Feb and March 2022 have nearly 100,000 reports. So this only represents about 3% of the reports.

However, all of these are

a) within the 30 day range usually considered for the vaccine

b) and you will not even be using these reports in your analysis if you relied solely on the NUMDAYS field (which would be empty for all these IDs)

You can also click through into the URL field and see the actual snapshot of the report for yourself (and verify that the NUMDAYS_BOUND field is calculated correctly).

Summary

This concludes my series on calculating a bound for the NUMDAYS field in VAERS reports.

As you can see, using the bound has added thousands of reports for VAERS analysis even with the restriction of 30 days from vaccination date to symptom onset date.