  This paper introduces a microscopic approach to model epidemics, which can
explicitly consider the consequences of individual's decisions on the spread of
the disease. We first formulate a microscopic multi-agent epidemic model where
every agent can choose its activity level that affects the spread of the
disease. Then by minimizing agents' cost functions, we solve for the optimal
decisions for individual agents in the framework of game theory and multi-agent
reinforcement learning. Given the optimal decisions of all agents, we can make
predictions about the spread of the disease. We show that there are negative
externalities in the sense that infected agents do not have enough incentives
to protect others, which then necessitates external interventions to regulate
agents' behaviors. In the discussion section, future directions are pointed out
to make the model more realistic.
