epiDonate - distributed serverless data infrastructure for epidemiological studies

• Published in: AMIA Summits on Translational Science proceedings Vol. 2023; pp. 25 - 31
• Main Authors: Almeida, Jonas S, Patel, Bhaumik, Russ, Daniel E, Bhawsar, Praphulla, Maurice, Pedro F Saint, Matthews, Charles, Anand, Adit, Ferguson, Martin, Johnson, Davin, Brotzman, Michelle, Gerlanc, Nicole, Chanock, Stephen, de Gonzalez, Amy Berrington, Gaudet, Mia, Garcia-Closas, Montserrat
• Format: Journal Article
• Language: English
• Published: United States American Medical Informatics Association 2023
• ISSN: 2153-4063

Epidemiological studies face two important challenges: the need to ingest ever more complex data types, and mounting concerns about participant privacy and data governance. These two challenges are compounded by the expectation that data infrastructure will eventually need to facilitate cross-registration of participants by multiple epidemiological studies.
The portable web-service epiDonate was developed using the serverless model known as FaaS (Function-as-a-Service). The reference implementation uses nodejs. The implementation relies on a simple tokenization scheme, mediated by a public API, that a) distinguishes admin from participant roles, with b) extensible permission configuration operating a read/write structure.
The critical design feature of epiDonate is the absence of business logic on the server-side (the web service). The simplicity removes the need to customize virtual machines and enables ecosystems of multiple web Applications backed by one or more data donation deployments.
https://episphere.github.io/donate.