CATS: Crowd-based Alert and Tracing Services for building a Safe Community Cluster against COVID-19
Khalid Jaber Almalki, Sejun Song, Muhammad Mohzary, Baek‐Young Choi · Integrated Network Management · 2021
COVID-19 has been causing several pandemic waves worldwide due to its long incubation period and hostile asymptomatic transmission. Society should continue to practice social distancing and masking in public despite aggressive vaccinations until achieving population immunity. However, the existing technology solutions, such as contact tracing apps and social-distancing devices, have been faced with suspicion due to privacy and accuracy concerns and have not been widely adopted. This paper proposes a novel infection management system named Crowd-based Alert and Tracing Services (CATS) to build a safe community cluster. CATS applies social distancing and masking principles to small, focused communities to provide higher privacy protection, efficient penetration of technology, and greater accuracy. We have designed a smart tag for managing social distancing. We also implemented a Machine Learning (ML)-based face mask tracking system to build non-binary Safety Impact Values (SIV).