Dear HHS Officials,

We are a dedicated research team based in Seattle, Washington; Wisconsin; and Chicago, IL, committed to enhancing the integrity and transparency of the Vaccine Adverse Event Reporting System (VAERS). Our mission is to address critical gaps in vaccine adverse event tracking, particularly during the COVID-19 mRNA vaccine rollout, by leveraging advanced analytical tools to uncover hidden patterns and ensure accurate public health oversight.

At the heart of our approach is a powerful, Python script we designed specifically for dissecting the "deltas"—the precise changes and discrepancies—between consecutive VAERS data drops. This script, available at https://chart.vaersdata.org, automates the comparison of snapshots from VAERS archives dating back to its inception, enabling rapid identification of additions, deletions, modifications, and anomalies that could otherwise go unnoticed. By preserving all changes in their original rows within a single, streamlined file, it empowers data scientists, regulators, and researchers to work more efficiently and reliably, fostering greater accountability in vaccine safety monitoring.

Our independent investigation into VAERS drop archives, cross-referenced against trusted collections like Dr. Steven Rubin's comprehensive medalerts.org repository (maintained by a computer scientist with over 40 years of experience), has already revealed concerning inconsistencies suggestive of potential data handling issues. For instance, follow-up reports—some confirming deaths—have been altered or entirely removed, with symptoms like "autopsy" deleted and vaccine types changed (e.g., from MNQ to MEN), effectively vanishing up to 10 death reports from official CDC tallies. These deltas highlight underreporting risks, such as uncounted follow-up cases (262 unique IDs identified via medalerts.org's tools) and discrepancies in batch numbers or severity classifications that persist across drops. Such findings underscore the urgent need for robust, verifiable delta analysis to safeguard data integrity and prevent undercounting of adverse events.

We believe adopting our Python script could transform HHS's VAERS auditing processes, providing an indispensable tool for proactive anomaly detection, streamlined compliance reviews, and evidence-based policy decisions. Early results from our delta-focused analyses, shared in 2023–2024 articles at https://deepdots.substack.com (attached as Exhibits A–F; authored by Gary Hawkins), demonstrate its value. Example exhibits are outlined below. More could be forthcoming.

So far, our work-in-progress shows clear areas that need attention:

• A. Detailed patient stories (narrative fields) were removed from reports outside the U.S., limiting full review (Exhibit A);
• B. Follow-up reports, including some that confirmed deaths, were later deleted (Exhibit B);
• C. Batch numbers (lot data) for Pfizer and Moderna are missing, blocking the ability to follow specific groups (Exhibit C);
• D. Serious blood clots in the lungs (pulmonary embolisms) appear under-reported due to weak labels (tagging) (Exhibit D);
• E. Many batch code mistakes (lot code errors) were fixed over time, pointing to early tracking problems (Exhibit E);
• F. Some severe reactions were labeled as mild, softening their real impact (Exhibit F).

We are still building this project and believe these early signals could lead to stronger VAERS practices and better public health protection. We would value your interest and input at this stage.

We invite you to:

Review the attached exhibits and let us know your thoughts; Share any insights on how VAERS data is managed; Explore ways we might work together to improve reporting.

Thank you for considering this developing work. We are ready to provide updates, raw data, or a live walk-through at your convenience over your preferred tele-communication platform.

Sincerely,

Jason Page