  This paper is concerned with developing mean-field game models for the
evolution of epidemics. Specifically, an agent's decision -- to be socially
active in the midst of an epidemic -- is modeled as a mean-field game with
health-related costs and activity-related rewards. By considering the fully and
partially observed versions of this problem, the role of information in guiding
an agent's rational decision is highlighted. The main contributions of the
paper are to derive the equations for the mean-field game in both fully and
partially observed settings of the problem, to present a complete analysis of
the fully observed case, and to present some analytical results for the
partially observed case.

Optimization and Control;Computer Science and Game Theory