Automated Exposure Notification for COVID-19

Abstract

In the current COVID-19 pandemic, various Automated Exposure Notification (AEN) systems have been proposed to help quickly identify potential contacts of infected individuals. All these systems try to leverage the current understanding of the following factors: transmission risk, technology to address risk modeling, system policies and privacy considerations. While AEN holds promise for mitigating the spread of COVID-19, using short-range communication channels (Bluetooth) in smartphones to detect close individual contacts may be inaccurate for modeling and informing transmission risk. This work finds that the current close contact definitions may be inadequate to reduce viral spread using AEN technology. Consequently, relying on distance measurements from Bluetooth Low-Energy may not be optimal for determining risks of exposure and protecting privacy. This paper's literature analysis suggests that AEN may perform better by using broadly accessible technologies to sense the respiratory activity, mask status, or environment of participants. Moreover, the paper remains cognizant that smartphone sensors can leak private information and thus recommends additional objectives for maintaining user privacy without compromising utility for population health. This literature review and analysis will simultaneously interest (i) health professionals who desire a fundamental understanding of the design and utility of AEN systems and (ii) technologists interested in understanding their epidemiological basis in the light of recent research. Ultimately, the two disparate communities need to understand each other to assess the value of AEN systems in mitigating viral spread, whether for the COVID-19 pandemic or for future ones.