The FloodLAMP Archive

FloodLAMP Biotechnologies was a small public-benefit company that developed and deployed decentralized molecular COVID-19 testing during the pandemic.

This page is a guide to the public archive: what it contains, how it is organized, and how to use it effectively.

This archive of approximately 200 primary files has been prepared in "AI-ready" form. The intention is for the user to download all or portions of the archive and then utilize the zip and combined markdown files with their AI tool of choice.

Use the full archive system prompt Download the full archive zip (1.1 GB) Clone the archive GitHub Go to the archive Google Drive folder

The FloodLAMP Manuscript

"Operational outcomes from 11 decentralized RT-LAMP COVID-19 surveillance programs in 6 U.S. states, 2020–2023"

The manuscript synthesizes the full body of work into a single peer-reviewed paper (submitted), including regulatory reform proposals found only in the manuscript.

Open markdown on GitHub Open PDF on GitHub Download Word document from GitHub

What FloodLAMP Is

FloodLAMP was a pandemic-era effort to build low-cost, decentralized molecular COVID-19 testing based on RT-LAMP and related workflows.

The work spanned assay development, validation documents, pilot deployments, software, operations, regulatory submissions, and broader thinking about open-access diagnostics.

The archive preserves both the technical materials and the surrounding context: what was built, how it was used, what worked in practice, and what broader lessons emerged from the effort.

What This Archive Is

This archive is a curated public release of documents from FloodLAMP's operating period and its closeout work.

It includes original working files, converted markdown versions, combined commentary files, pilot data materials, regulatory documents, whitepapers, presentations, and related reference material.

The archive is organized into four top-level categories: Guides, Pilots, Regulatory, and Various. Those categories are the main navigation structure for this site.

How To Use This Archive With AI

Most of the archive has been prepared in markdown specifically so it can be searched, summarized, compared, and synthesized with AI tools.

A good workflow is to start with one category or subcategory, describe your background and what you want to learn, and then give an AI system the most relevant files or combined markdown bundles.

Use the category pages on this site to identify the most relevant subcategories.
Use the linked context/commentary files when you want a curated orientation before reading raw documents.
Use combined markdown files or zip bundles when you want a larger corpus for AI-assisted synthesis.
Use the GitHub repository when you want direct access to the underlying archive structure and files.