ABSTRACT
Proximity-based contact tracing relies on mobile-device interaction to estimate the spread of disease. ShareTrace is one such approach that improves the efficacy of tracking disease spread by considering direct and indirect forms of contact. In this work, we utilize the actor model to provide an efficient and scalable formulation of ShareTrace with asynchronous, concurrent message passing on a temporal contact network. We also introduce message reachability, an extension of temporal reachability that accounts for network topology and message-passing semantics. Our evaluation on both synthetic and real-world contact networks indicates that correct parameter values optimize for algorithmic accuracy and efficiency. In addition, we demonstrate that message reachability can accurately estimate the risk a user poses to their contacts. © 2022 IEEE.
ABSTRACT
We propose a novel privacy-preserving COVID-19 risk assessment algorithm that can make a fundamental contribution to the development of the next generation resilient public health and health care systems. The proposed algorithm, ShareTrace, uses a hyperlocal interaction graph to capture direct and indirect physical interactions among users. Combining user-reported symptoms that are propagated through the hyperlocal interaction graph via a novel message passing algorithm, ShareTrace is able to pick up early warning signals based on the combination of interactions with others and symptoms. The proposed algorithm is inspired by the belief propagation algorithm and iterative decoding of low-density parity-check codes over factor graphs. Our evaluation on synthetic data shows the efficiency and efficacy of the proposed solution. © 2021 ACM.