Enhancing Infectious Disease Outbreak Surveillance via Bidirectional Contact Tracing

Abstract

Contact tracing (CT) remains essential in mitigating the spread of pandemics (including COVID-19). Specifically, backward CT helps find superspreaders and hidden chains of transmission from asymptomatically infected users. However, most literature proposing CT frameworks and apps deployed by various countries do not attempt backward CT. In this work, we present a novel approach for bidirectional CT. The proposed approach works using Bluetooth low-energy sensors that detect the presence of users in a vicinity and inform a central BS of user presence. By fixing Bluetooth low-energy sensor (BLE-S) in buildings, the proposed framework can trace the contacts resulting from contamination of a location (indirect contacts). We present two algorithms using which the proposed framework can trace forward and backward contacts. Using a simulation, we also track the spread of infection among different “generations” of the infected and the impact of backward tracing on preventing the spread across generations. We observe the effect of critical epidemiological parameters, such as the reproduction number (R) and the overdispersion parameter (k), specifically on backward CT efficiency.