Author: Ho, Yu-Chen; Chen, Yi-Hsuan; Hung, Shen-Hua; Huang, Chien-Hao; Po, Poga; Chan, Chung-Hsi; Yang, Di-Kai; Tu, Yi-Chin; Liu, Tyng-Luh; Fang, Chi-Tai
Title: Social Distancing 2.0 with Privacy-Preserving Contact Tracing to Avoid a Second Wave of COVID-19 Cord-id: 7dj7dnjk Document date: 2020_6_30
ID: 7dj7dnjk
Snippet: 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 develop an open source mobile app. The app uses a Bluetooth-based, decentralized contact tracing p
Document: 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 develop 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. Modeling 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.
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