Quantifying SARS-CoV-2 Infection Risk Within the Google/Apple Exposure Notification Framework to Inform Quarantine Recommendations (UA21-047)

University of Arizona researchers have developed an improvement to existing COVID-19 contact tracing technology via a multi-variable analysis of distance, volume of exposure, and likelihood of infection to give the user a personalized recommendation for self-quarantine. There are several other apps that have used the Google/Apple API and open source code to develop and improve inter-device communication applications. This invention is an improvement of both the probability of infection to the user and determining distance between Bluetooth devices. Background: Bluetooth attenuation has been validly used as a way to determine distances between devices for the past several years. However, there are variables that make the calculation difficult and inaccurate. For example, the orientation of the device relative to the other, receiver strength, emission power, etc. All of these variables must be taken into account to pinpoint the distances between individuals. This is relevant because COVID-19 contact tracing apps are generally looking for individuals who stay within 6 feet of each other for over 15 minutes. Additionally, the probability of infection from COVID-19 depends largely on the ?shedding rate? of the individual who has tested positive. Furthermore, the risk of having COVID-19 will decrease with each day that goes by without testing positive. This study personalizes the quarantine length and advises the user of their risk depending on all of the aforementioned variables. Learn more at https://bit.ly/UA21-047 Anne Spieth annes@tla.arizona.edu 520-626-1577

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