Peter Stone's Selected Publications

Classified by TopicClassified by Publication TypeSorted by DateSorted by First Author Last NameClassified by Funding Source


Multiagent Epidemiologic Inference through Realtime Contact Tracing

Guni Sharon, James Ault, Peter Stone, Varun Kompella, and Roberto Capobianco. Multiagent Epidemiologic Inference through Realtime Contact Tracing. In Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2021), May 2021.

Download

[PDF]1014.3kB  

Abstract

This paper addresses an epidemiologic inference problem where, given realtime observation of test results, presence of symptoms, and physical contacts, the most likely infected individuals need to be inferred. The inference problem is modeled as a hidden Markov model where infection probabilities are updated at every time step and evolve between time steps. We suggest a unique inference approach that avoids storing the given observations explicitly. Theoretical justification for the proposed model is provided under specific simplifying assumptions. To complement these theoretical results, a comprehensive experimental study is performed using a custom-built agent-based simulator that models inter-agent contacts. The reported results show the effectiveness of the proposed inference model when considering more realistic scenarios -- where the simplifying assumptions do not hold. When pairing the proposed inference model with a simple testing and quarantine policy, promising trends are obtained where the epidemic progression is significantly slowed down while quarantining a bounded number of individuals.

BibTeX Entry

@inproceedings{AAMAS21_COVID,
 title={Multiagent Epidemiologic Inference through Realtime Contact Tracing},
 author={Guni Sharon and James Ault and Peter Stone and Varun Kompella and Roberto Capobianco},
 booktitle={Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2021)},
 location = {London, UK},
 month = {May},
 year={2021},
 organization={International Foundation for Autonomous Agents and Multiagent Systems},
 abstract = {
     This paper addresses an epidemiologic inference problem where,
     given realtime observation of test results, presence of symptoms,
     and physical contacts, the most likely infected individuals need
     to be inferred. The inference problem is modeled as a hidden
     Markov model where infection probabilities are updated at every
     time step and evolve between time steps.  We suggest a unique
     inference approach that avoids storing the given observations
     explicitly. Theoretical justification for the proposed model is
     provided under specific simplifying assumptions. To complement
     these theoretical results, a comprehensive experimental study is
     performed using a custom-built agent-based simulator that models
     inter-agent contacts. The reported results show the effectiveness
     of the proposed inference model when considering more realistic
     scenarios -- where the simplifying assumptions do not hold. When
     pairing the proposed inference model with a simple testing and
     quarantine policy, promising trends are obtained where the
     epidemic progression is significantly slowed down while
     quarantining a bounded number of individuals.},
}

Generated by bib2html.pl (written by Patrick Riley ) on Wed Jul 21, 2021 08:39:16