@conference{Hurtado2023Q,

title = {Quarantine in Motion: A Graph Learning and Multi-Agent Reinforcement Learning Framework to Reduce Disease Transmission Without Lockdown},

author = {Sofia Hurtado and Radu Marculescu},

url = {https://ieeexplore.ieee.org/document/10068686},

year  = {2023},

date = {2023-11-06},

urldate = {2023-11-06},

booktitle = {Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2023)},

abstract = {Exposure notification applications are designed to help trace disease spreading by alerting exposed individuals to get tested. However, false alarms can cause users to become hesitant to respond, making the applications ineffective. To address the shortcomings of slow manual contact tracing, costly lockdowns, and unreliable exposure notification applications, better disease mitigation strategies are needed. In this study, we propose a new disease mitigation paradigm where people can reduce infection spreading while maintaining some mobility (i.e., Quarantine in Motion). Our approach utilizes Graph Neural Networks (GNNs) to predict disease hotspots such as restaurants, shops and parks, and Multi-Agent Reinforcement Learning (MARL) to collaboratively manage human mobility to reduce disease transmission. As proof of concept, we simulate an infection using real-world mobility data from New York City (over 200,000 devices) and Austin (over 36,000 devices) and train 10,000 agents from each city to manage disease dynamics. Through simulation, we show that a trained population suppresses their reproduction rate below 1, thereby mitigating the outbreak.},

keywords = {Epidemics, GPS, Graph Neural Network, Multi Agent Reinforcement Learning},

pubstate = {published},

tppubtype = {conference}

}