Do you have a solution that can maximize symptom data to enable earlier detection & improved situational awareness using the Delphi Group at Carnegie Mellon University (CMU) and the Joint Program on Survey Methodology at the University of Maryland (UMD) symptom datasets?
Sponsored by Facebook Data for Good, hosted by the Duke-Margolis Center for Health Policy, and in partnership with the Delphi Group at Carnegie Mellon University, the Joint Program on Survey Methodology at the University of Maryland (UMD), and Resolve 2 Save Lives (an initiative of Vital Strategies), the COVID-19 Symptom Data Challenge asks participants to develop a novel analytic approach that uses the CMU/UMD COVID-19 symptom survey data to enable earlier detection and improved situational awareness of the outbreak by public health authorities and the general public.
The Challenge will include prizes for semi-finalists/finalists, including $50k for the 1st place winner, $25k for the runner up, and $5k for semi-finalists. In addition to the cash prize, the winner will be featured on the Facebook Data for Good page and invited to a discussion forum with potential partners including representatives from health departments and academic institutions. All results from the challenge are expected to be placed in the public domain.
Phase I submissions are due by September 29th, 2020 11:59 PM ET