Peer-to-Peer Contact Tracing: A Privacy-Preserving Smartphone Application
Yasaka, Tyler M.
Lehrich, Brandon M.
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[Abstract]. Background: The novel coronavirus disease 2019 (COVID-19) pandemic is an urgent public health crisis, with epidemiologic models predicting severe consequences, including high death rates, if the virus is permitted to run its course without any intervention or response. Contact tracing using smartphone technology is a powerful tool which may be employed to limit disease transmission during an epidemic or pandemic, yet contact tracing applications present with significant privacy concerns regarding the collection of personal data such as location. Objective: To provide an effective contact tracing smartphone application that respects user privacy by not collecting location information or other personal data. Methods: We propose the use of an anonymized graph of interpersonal interactions to conduct a novel form of contact tracing, and develop a proof-of-concept smartphone application which implements this approach. Additionally, we develop a computer simulation model which demonstrates the impact of our proposal on epidemic or pandemic outbreak trajectories across multiple rates of adoption. Results: Our proof-of-concept smartphone application allows users to create “checkpoints” for contact tracing, check their risk level based on their past interactions, and anonymously self-report a positive status to their peer network. Our simulation results suggest that higher adoption rates of such an application may result in a better controlled epidemic or pandemic outbreak. Conclusions: Our proposed smartphone-based contact tracing method presents a novel solution which preserves privacy while demonstrating the potential to suppress an epidemic or pandemic outbreak. This application could potentially be applied to the current COVID-19 pandemic as well as other epidemics or pandemics in the future, in order to achieve a middle ground between drastic isolation measures and unmitigated disease spread.