2025
2.
Hurtado, Sofia; Marculescu, Radu
Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data Workshop
Advances in Social Networks Analysis and Mining, Springer, 2025, ISBN: 978-3-031-85386-9, (Article presented at the REINFORCE workshop in the ASONAM conference in September 2024, and finally published in June 2025).
Abstract | Links | BibTeX | Tags: Disease Mitigation, Graph Neural Network, Human Mobility Data
@workshop{Hurtado2024,
title = {Graph Learning for Bidirectional Disease Contact Tracing on Real Human Mobility Data},
author = {Sofia Hurtado and Radu Marculescu },
editor = {I-Hsien Ting and Reda Alhajj and Panagiotis Karampelas and Min-Yuh Day},
url = {https://link.springer.com/book/9783031785535},
doi = {10.1007/978-3-031-85386-9_15},
isbn = {978-3-031-85386-9},
year = {2025},
date = {2025-06-11},
urldate = {2025-06-11},
booktitle = {Advances in Social Networks Analysis and Mining},
issue = {ASONAM},
publisher = {Springer},
abstract = {For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on potential exposures, a fully automated system is needed to track the infectious transmission routes. To this end, our research leverages large-scale contact networks from real human mobility data to identify the path of transmission. More precisely, we introduce a new Infectious Path Centrality network metric that informs a graph learning edge classifier to identify important trans- mission events, achieving an F1-score of 94%. Additionally, we explore bidirectional contact tracing, which quarantines individuals both retroactively and proactively, and compare its effectiveness against traditional forward tracing, which only isolates individuals after testing positive. Our results indicate that when only 30% of symptomatic individuals are tested, bidirectional tracing can reduce infectious effective reproduction rate by 71%, thus significantly controlling the outbreak.},
howpublished = {Social Networks Analysis and Mining},
note = {Article presented at the REINFORCE workshop in the ASONAM conference in September 2024, and finally published in June 2025},
keywords = {Disease Mitigation, Graph Neural Network, Human Mobility Data},
pubstate = {published},
tppubtype = {workshop}
}
For rapidly spreading diseases where many cases show no symptoms, swift and effective contact tracing is essential. While exposure notification applications provide alerts on potential exposures, a fully automated system is needed to track the infectious transmission routes. To this end, our research leverages large-scale contact networks from real human mobility data to identify the path of transmission. More precisely, we introduce a new Infectious Path Centrality network metric that informs a graph learning edge classifier to identify important trans- mission events, achieving an F1-score of 94%. Additionally, we explore bidirectional contact tracing, which quarantines individuals both retroactively and proactively, and compare its effectiveness against traditional forward tracing, which only isolates individuals after testing positive. Our results indicate that when only 30% of symptomatic individuals are tested, bidirectional tracing can reduce infectious effective reproduction rate by 71%, thus significantly controlling 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.
