There is incomplete data at this time on the nature of various contacts and specific relationships among individuals that result in CA-MRSA colonization or infection. So the CA-MRSA ABM uses a hy- brid approach to model contact likelihood, based partly on perfect mixing and partly on implied social network relationships by activity. Disease transition at the individual level is modeled by transition prob- abilities and event scheduling. The event scheduler is a nondeterministic finite state automaton (FSA) and discrete event simulation (DES). The FSA models individual disease state transition in the absence of agent behavioral responses or policy interventions. Effectively, this is a Markov discrete-time state transi- tion model in which agents change disease states and are dynamically compartmentalized based on their current disease and activity states. The DES is used to model disease state transitions that depend on indi- vidual patient behavior and assumed policy interventions, and potentially of physician and healthcare worker behavior.