We Health Covid Watch Arizona

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We Health Covid Watch Arizona Tech ID: UA21-047 Invention: This is a mobile app available at https://www.wehealth.org/arizona. COVID-19 and the research surrounding contact tracing have so far lead to one-size-fits all solutions. For example, the standard 14 day quarantine is given to all who contact a COVID-19 positive individual if that contact was less than 6 feet for more than 15 minutes. In contrast, this improvement to existing technology is 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. Status: This is an Open Source technology. Covid Watch code is available at https://github.com/covidwatchorg. Code used to analyze the data to produce risk settings is available at https://github.com/awilson12/risk_scoring.