OBJECTIVES: To date, efforts to model Clostridium difficile infection (CDI) have been limited. Most models do not address the contribution of asymptomatic carriers as sources of new infections and are restricted to hospital acquired CDI. We aim to develop a simulation model to systematically examine the dynamic relationship between three major subpopulations of CDI transmission: hospitals, communities, and long-term facilities, to evaluate treatment effectiveness and costs. METHODS: We conducted a systematic investigation to determine the key epidemiological factors influencing CDI transmission according to the three major subpopulations: hospitals, communities, and long-term care facilities. We have developed a stochastic agent-tracking meta-population network model of CDI transmission, and identified parameters that would capture transmission from symptomatic and asymptomatic carriers to uninfected individuals among the subpopulations. RESULTS: We identified eight infection states: susceptible, gastrointestinal exposure, colonized, diseased, deceased, clinically resolved colonized, relapse of CDI, and cleared. Key parameters include; health outcomes of target populations, time horizon, diagnostic characteristics, treatment effectiveness, transmission rates, susceptibility rates, recurrence rates, and costs.  Initial treatments of CDI do not induce a lasting response in 15%-25% of patients. The estimated effectiveness of antibiotic therapy for a first recurrence is 60% and declining with multiple recurrences. Major predictors of recurrences were advanced age and duration of initial hospitalizations. Recurrences of CDI were associated with major increases in hospital LOS and costs. CONCLUSIONS: Our dynamic network model of CDI transmission will improve the capacity to project and to quantify the impact of a CDI outbreak in terms of clinical burden and costs.