Quarantine in Motion: A Graph Learning Framework to Reduce Disease Transmission Without Lockdown

Abstract

Exposure notification applications are developed to increase the scale and speed of disease contact tracing. Indeed, by taking advantage of Bluetooth technology, they track the infected population's mobility and then inform close contacts to get tested. In this paper, we ask whether these applications can extend from reactive to preemptive risk management tools? To this end, we propose a new framework that utilizes graph neural networks (GNN) and real-world Foursquare mobility data to predict high risk locations on an hourly basis. As a proof of concept, we then simulate a risk-informed Foursquare population of over 36,000 people in Austin TX after the peak of an outbreak. We find that even after 50% of the population has been infected with COVID-19, they can still maintain their mobility, while reducing the new infections by 13%. Consequently, these results are a first step towards achieving what we call Quarantine in Motion.