2025
3.
Hurtado, Sofia; Marculescu, Radu
Health Status Discovery for Online Bidirectional Contact Tracing and Disease Aware Navigation Conference
2025 IEEE Conference on Artificial Intelligence (CAI), 2025.
Links | BibTeX | Tags: Disease Mitigation, Multi Agent Reinforcement Learning, Networks
@conference{nokey,
title = {Health Status Discovery for Online Bidirectional Contact Tracing and Disease Aware Navigation},
author = {Sofia Hurtado and Radu Marculescu},
doi = {10.1109/CAI64502.2025.00084},
year = {2025},
date = {2025-05-06},
booktitle = {2025 IEEE Conference on Artificial Intelligence (CAI)},
pages = {457-462},
keywords = {Disease Mitigation, Multi Agent Reinforcement Learning, Networks},
pubstate = {published},
tppubtype = {conference}
}
2023
2.
Hurtado, Sofia; Marculescu, Radu
Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2023), 2023.
Abstract | Links | BibTeX | Tags: Epidemics, GPS, Graph Neural Network, Multi Agent Reinforcement Learning
@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}
}
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.
2022
1.
Hurtado, Sofia; Marculescu, Radu
On- and Offline Multi-agent Reinforcement Learning for Disease Mitigation using Human Mobility Data Workshop
3rd Offline RL Workshop: Offline RL as a ''Launchpad'', 2022, (Workshop is a part of NEURIPS 2022).
Abstract | Links | BibTeX | Tags: Disease Mitigation, Human Mobility Data, Multi Agent Reinforcement Learning
@workshop{Hurtado2022,
title = {On- and Offline Multi-agent Reinforcement Learning for Disease Mitigation using Human Mobility Data },
author = {Sofia Hurtado and Radu Marculescu},
url = {https://openreview.net/forum?id=Abuzft2FfH},
year = {2022},
date = {2022-11-11},
urldate = {2022-11-11},
booktitle = {3rd Offline RL Workshop: Offline RL as a ''Launchpad''},
abstract = {The COVID-19 pandemic generates new real-world data-driven problems such as predicting case surges, managing resource depletion, or modeling geo-spatial infection spreading. Though reinforcement learning (RL) has been previously proposed to optimize regional lock-downs, the availability of mobility tracking data with offline RL allows us to push decision making from the top-down perspective (i.e., driven by governments) to the bottom up perspective (i.e., driven by individuals). Rather than predicting the outcome of the outbreak, we utilize offline RL as a tool, along with epidemic modeling, to empower collaborative decision-making at the individual level. In our investigations, we ask whether we can train the population of a city to become more resilient against infectious diseases? To investigate, we deploy a 'city' of 10,000 agents loaded with real visits at Points of Interest (POIs) (e.g., restaurants, gyms, parks) throughout a target metropolitan area during the COVID-19 pandemic (July 2020). Using a standard disease compartmental model, we find that the city of trained agents can reduce disease transmissions by 60%. This opens a new direction in using offline RL as a springboard to further the research at the intersection of artificial intelligence and disease mitigation.},
note = {Workshop is a part of NEURIPS 2022},
keywords = {Disease Mitigation, Human Mobility Data, Multi Agent Reinforcement Learning},
pubstate = {published},
tppubtype = {workshop}
}
The COVID-19 pandemic generates new real-world data-driven problems such as predicting case surges, managing resource depletion, or modeling geo-spatial infection spreading. Though reinforcement learning (RL) has been previously proposed to optimize regional lock-downs, the availability of mobility tracking data with offline RL allows us to push decision making from the top-down perspective (i.e., driven by governments) to the bottom up perspective (i.e., driven by individuals). Rather than predicting the outcome of the outbreak, we utilize offline RL as a tool, along with epidemic modeling, to empower collaborative decision-making at the individual level. In our investigations, we ask whether we can train the population of a city to become more resilient against infectious diseases? To investigate, we deploy a 'city' of 10,000 agents loaded with real visits at Points of Interest (POIs) (e.g., restaurants, gyms, parks) throughout a target metropolitan area during the COVID-19 pandemic (July 2020). Using a standard disease compartmental model, we find that the city of trained agents can reduce disease transmissions by 60%. This opens a new direction in using offline RL as a springboard to further the research at the intersection of artificial intelligence and disease mitigation.
