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In collaboration with the i2b2 TransMART Foundation and with OHDSI.

PURPOSE

This is a consortium set up to support international collaboration on COVID-19 research while respecting patient privacy.

MOTIVATION

In the case of a pandemic due to a new pathogen, it is crucial for healthcare facilities to be able to share information about the efficacy of various treatments and relate them to the clinical and genetic profile of the patients. Isolated and small-scale datasets in different silos limit the ability to do in-depth meaningful analysis. There is a need to find similar patients and build better and more accurate personalized or subpopulation models that can help better understand risk factors and more effectively predict the response and progression of the disease.

The conventional solution is to either share aggregated data or to share “de-identified” data. However, these techniques have well-known limitations and can severely restrict the accuracy, and therefore the utility, of the obtained insights. The heterogeneity of national data protection regulations is an additional obstacle.

The SCOR consortium response to this concern consists in making use of well-known, mathematically proven cryptographic techniques that are embodied in an open-source software package called MedCo, made available to all consortium partners. MedCo will be used to address specific medical questions, defined within the consortium.

SCOR will prove that it is possible and faster to advance in COVID-19 research (and similar epidemics and pandemics) by obtaining world-scale meaningful insights without compromising the patients’ privacy.

SCOR HIGHLIGHTS

This is what makes SCOR stand out among other approaches to COVID-19 research:

DISTRIBUTED DATA EXPLORATION AND ANALYSIS

Access-controlled exploration and analysis across internationally-distributed clinical sites: enable accurate complex analyses as if all the data were centralized, without the need of moving the data or relying on meta-analyses.

Local data control AND STREAMLINED IRB APPROVAl

Each clinical site has full control over its data, which can be stored locally or at an external storage provider. No local clear-text data (not even local aggregates) ever leaves the chosen storage. The achieved protection guarantees facilitate ethics approval.

Trust Decentralization

There is no need of any central authority as, thanks to multiparty homomorphic encryption, trust is distributed across all clinical sites and data is encrypted end-to-end. No clear-text data is leaked even if a subset of sites gets compromised.

TASK FORCES

The consortium is structured in the following task forces:

Infrastructure and Deployment

This task force is in charge of provisioning the machines and the network resources needed at each site to interconnect all the nodes and enable the execution of the encrypted computations across the consortium network world-wide.

Security and Privacy

This task force adapts and customizes the privacy-conscious mechanisms (based on multi party homomorphic encryption and additional privacy enhancing technologies) that enable the execution of the research protocols on end-to-end protected data, in such a way that individual data never leaves the premises or the control of the data owner.

Research Protocols

This task force defines the research protocols on COVID-19 data, to identify useful patterns and interdependencies, and to model the virus progression, the development of immune responses, and the effectiveness of several treatments depending on the patient features.

Governance and Ethics

This task force deals with the governance of the consortium, addresses the data ownership and management policies, and defines the ethics framework for the international sharing and collaboration on COVID-19 patient data.

Interoperability & Data Formats

This task force is in charge of defining the common data formats for coding the information from COVID-19 patients, so that they can be interoperable across the whole network. For this purpose, it relies in global standards such as OMOP and the minimum datasets defined by the World Health Organization (WHO).

PARTNERS

Swiss Federal Institute of Technology Lausanne

Lausanne, Switzerland

Jean-Pierre Hubaux
Juan Troncoso-Pastoriza

The University of Texas

Health Science Center Houston

Houston, TX, USA

Assaf Gottlieb
Arif Harmanci
Xiaoqian Jiang
Miran Kim
Yejin Kim

 Bar Ilan University

Ramat Gan, Israel

Milana Frenkel-Morgenstern

Lausanne University Hospital (CHUV)

Lausanne, Switzerland

Jean Louis Raisaro
Jacques Fellay
Nicolas Rosat

Gachon University

Gyeonggi-do, South Korea

Jaehun Jung

Harvard Medical School

Boston, MA, USA

Jeffrey Klann

Geneva University Hospitals

Geneva, Switzerland

Angèle Gayet-Ageron

ICS Maugeri

Pavia, Italy

Riccardo Bellazzi

Houston Methodist Hospital

Houston, TX, USA

Stephen Wong

IRCCS Policlinico Ca’ Granda Ospedale Maggiore di Milano

Milano, Italy

Luigia Scudeller

 National Human Genome Research Institute

Bethesda, ML, USA

Heidi Sofia

University of Pavia

Pavia, Italy

Riccardo Bellazzi

Fondazione IRCCS Policlinico

San Matteo, Pavia, Italy

Catherine Klersy

Scripps Research Institute

La Jolla, CA, USA

Ali Torkamani

University of Washington

Seattle, WA, USA

Trevor Cohen
Adam Wilcox

Vanderbilt University

Nashville, TN, USA

Brad Malin

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