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The Pandemic Response Commons
Matthew Trunnell; Casey Frankenberger; Bala Hota; Troy Hughes; Plamen Martinov; Urmila Ravichandran; Nirav S Shah; Robert L Grossman.
Affiliation
  • Matthew Trunnell; Open Commons Consortium
  • Casey Frankenberger; Rush University Medical Center
  • Bala Hota; Rush University Medical Center
  • Troy Hughes; University of Chicago
  • Plamen Martinov; Open Commons Consortium
  • Urmila Ravichandran; NorthShore University Health System
  • Nirav S Shah; NorthShore University Health System
  • Robert L Grossman; University of Chicago
Preprint in English | medRxiv | ID: ppmedrxiv-22276542
ABSTRACT
ObjectiveA data commons is a software platform for managing, curating, analyzing, and sharing data with a community. The Pandemic Response Commons is a data commons designed to provide a data platform for researchers studying an epidemic or pandemic. MethodsThe pandemic response commons was developed using the open source Gen3 data platform and is based upon consortium, data, and platform agreements developed by the not-for-profit Open Commons Consortium. A formal consortium of Chicagoland area organizations was formed to develop and operate the pandemic response commons. ResultsWe developed a general pandemic response commons and an instance of it for the Chicagoland region called the Chicagoland COVID-19 Commons. A Gen3 data platform was set up and operated with policies, procedures and controls based upon NIST SP 800-53. A consensus data model for the commons was developed, and a variety of datasets were curated, harmonized and ingested, including statistical summary data about COVID cases, patient level clinical data, and SARS-CoV-2 viral variant data. Discussion and conclusionGiven the various legal and data agreements required to operate a data commons, a pandemic response commons is designed to be in place and operating at a low level prior to the occurrence of an epidemic, with the activities increasing as required during an epidemic. A regional instance of a Pandemic Response Commons is designed to be part of a broader data ecosystem or data mesh consisting of multiple regional commons supporting pandemic response through sharing of regional data.
License
cc_by
Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study Language: English Year: 2022 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Observational study / Prognostic study Language: English Year: 2022 Document type: Preprint
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