Quarantine in Motion: A Graph Learning and Multi-Agent Reinforcement Learning Framework to Reduce Disease Transmission Without Lockdown-Reference-Cited by-同舟云学术

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Quarantine in Motion: A Graph Learning and Multi-Agent Reinforcement Learning Framework to Reduce Disease Transmission Without Lockdown

Published:2023-11-06 Issue: Volume:1 Page:82-89
ISSN:
Container-title:Proceedings of the International Conference on Advances in Social Networks Analysis and Mining
language:
Short-container-title:

Author:

Hurtado Sofia¹^ORCID,Marculescu Radu¹^ORCID

Affiliation:

1. Chandra Department of Electrical Computer Engineering, University of Texas at Austin, Austin, Texas, United States

Funder

National Science Foundation

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3625007.3627727

Reference23 articles.

1. Why large cities are more vulnerable to the covid-19 pandemic;Lai S.;Journal of Urban Management,2022

2. Inferring the effectiveness of government interventions against covid-19;Brauner J.;Science,2020

3. Foursquare visits dataset https://foursquare.com/products/visits/.

4. D. Kluger, Y. Aizenbud, A. Jaffe, et al., "Impact of healthcare worker shift scheduling on workforce preservation during the covid-19 pandemic," Infection Control and Hospital Epidemiology, 2020.

5. S. Kaiser, A. Watson, B. Dogan, et al., "Preventing covid-19 transmission in education settings," Pediatrics, 2021.

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