Social Distancing 2.0 with Privacy-Preserving Contact Tracing to Avoid a Second Wave of COVID-19
This addresses the urgent public health challenge of controlling COVID-19 spread while protecting user privacy, though it is incremental as it builds on existing contact tracing concepts.
The paper tackles the problem of preventing a second wave of COVID-19 after reopening the economy by assessing Social Distancing 2.0, which combines proximity alerts and privacy-preserving contact tracing. Modeling results show that a 50% adoption rate reduces the basic reproductive number R0 to less than 1, effectively preventing resurgence.
How to avoid a second wave of COVID-19 after reopening the economy is a pressing question. The extremely high basic reproductive number $R_0$ (5.7 to 6.4, shown in new studies) of SARS-CoV-2 further complicates the challenge. Here we assess effects of Social distancing 2.0, i.e. proximity alert (to maintain inter-personal distance) plus privacy-preserving contact tracing. To solve the dual task, we developed an open source mobile app. The app uses a Bluetooth-based, decentralized contact tracing platform over which the anonymous user ID cannot be linked by the government or a third party. Modelling results show that a 50\% adoption rate of Social distancing 2.0, with privacy-preserving contact tracing, would suffice to decrease the $R_0$ to less than 1 and prevent the resurgence of COVID-19 epidemic.