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

Guni Sharon Postdoctoral Fellow gunisharon [at] gmail com
Peter Stone Faculty pstone [at] cs utexas edu