Viral outbreaks spread throughout networks of people via transmission events. We aim to combine human mobility data, network science, and machine learning to inform and mitigate the disease dynamics for COVID-19. Furthermore, we aim to build an always-on social sensing system to improve a population’s resilience to a novel virus.


Selected Publications

Hurtado, Sofia; Marculescu, Radu; Drake, Justin

Quarantine in Motion: a Graph Learning Framework to Reduce Disease Transmission without Lockdown Inproceedings Forthcoming

In: Proceedings of the 2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2022), Forthcoming.


Hurtado, Sofia; Marculescu, Radu; Drake, Justin; Srinivasan, Ravi

Pruning Digital Contact Networks for Meso-scale Epidemics Surveillance Using Foursquare Data Inproceedings

In: ACM/IEEE Intl. Conf. on Advances in Social Network Analysis and Mining (ASONAM), 2021.

Links | BibTeX

Topirceanu, Alexandru; Udrescu, Mihai; Marculescu, Radu

Centralized and decentralized isolation strategies and their impact on the COVID-19 pandemic dynamics Journal Article

In: arXiv preprint arXiv:2004.04222, 2020.

Links | BibTeX