Managing Trajectories and Interactions during a Pandemic: A Trajectory Similarity-based Approach (Demo Paper)

Abstract

COVID-19 has brought about substantial social, economic and health related burdens, motivating different control measures from policy makers worldwide. Contact tracing plays a pivotal role in the COVID-19 era. However, contact tracing is by nature entirely retrospective: it can only identify contacts of known or suspected cases. Our proposed system is prospective, aiming to 'create' networks that will ultimately make contact tracing and pandemic management easier. As contact tracing seeks to reconstruct the underlying interaction network, we can improve the process by reducing the complexity of contact network structure; we introduce a method for reducing contact network complexity through strategic scheduling. The method functions through pairwise comparison of individual trajectories in a coordinate space of activities, locations, and time intervals. We demonstrate the method through a simulated scenario where individuals (students) register for activities using a mobile application in a campus. The application then applies our algorithm to provide individuals with schedules that reduce the complexity of the overall network, without compromising individual privacy.