An efficient and secure data science platform for COVID-19 cases and other related health data

The COVID-19 crisis has developed, and not unjustifiably, a strong data aspect, requiring to handle, store and exchange large amounts of data in an efficient and secure way, not only among hospitals and clinics, but also between government services globally. In this project, we propose the development of an extendible prototype of a big data platform for COVID-19 data. We focus on the scalability and the security of this platform, by employing novel technologies, such as NoSQL databases, MapReduce analytics and Blockchain. We will present this platform in the context of COVID-19 cases with underlying conditions (focusing on addiction and communicable diseases). CMUQL, the project partner, will benefit from the platform to perform current and future epidemiological research at scale in a secure and efficient manner.

Faculty Supervisor:

Marios Fokaefs;Madeleine Durand;Bram Adams

Student:

Partner:

CMU du Quartier Latin

Discipline:

Engineering

Sector:

Health and Related Sciences & Technology

University:

Polytechnique Montréal; Université de Montréal

Program: