Dr. Eleazar Eskin, Professor and Chair of Computational Medicine, has helped develop a public anonymous symptom screening survey that can be used to help protect employees of an institution while tracking changes in symptoms over time, making a map of exposures in an area, and tracking and measuring the effect of public health policies based on current guidelines. The project is already being used as part of the STOPCOVID-19 TOGETHER survey and being piloted at UCLA.
Since not all individuals entering a workplace or campus are employees, there is difficulty in collecting symptom information on these individuals to take the appropriate steps to prevent disease spread and reduce infection. The survey system proposed by Dr. Eskin allows participants to take an anonymous symptom survey which, upon completion, are issued a unique completion code specific to an hour and date (rather than individual). Individuals can then share their code with an institution, giving only information of completion while not providing any personal information. Each completion code can be verified by the employer or institution. The cryptographic algorithm makes it impossible to guess a valid code. When displayed on a cell phone, the code changes color over time to indicate time elapsed since taking the survey.
Link to Project Website: https://stopcovid19together.org/
Link to Faculty Website: http://web.cs.ucla.edu/~eeskin/
Link to Relevant Publications:
1) https://ucla.technologypublisher.com/techcase/20-0384