This publication describes systems and techniques for identifying epidemic candidates utilizing mobile device location information. Through the use of beacons and cryptographic data, the privacy and location of a user of the mobile device are protected. The mobile device generates a beacon that includes cryptographic data, broadcasts the beacon via wireless communication from the mobile device, monitors for beacons communicated wirelessly, receives beacons from other mobile devices, stores beacons from other mobile devices for a configurable time period, communicates with a server to receive a beacon broadcast, calculates an identification using a timestamp included in the beacon broadcast, compares the calculated identification to received identifications in the beacon broadcast to determine a match that indicates exposure, and displays an alert to a user of the mobile device. A machine-learned (ML) model, on the device or at a server, can receive data associated with an exposure and analyze it. The ML model can use algorithms to make a recommendation, for example: recommend a candidate for testing.
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N/A, "Identifying Epidemic Candidates Using Mobile Devices", Technical Disclosure Commons, (June 29, 2020)