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We describe an innovative simulation framework that combines daily commuting network data with a commonly used population-based transmission model to assess the impacts of various interventions on epidemic dynamics in Taiwan. Called the Multilayer Epidemic Dynamics Simulator (MEDSim), our proposed framework has four contact structures: within age group, between age groups, daily commute, and nationwide interaction. To test model flexibility and generalizability, we simulated outbreak locations and intervention scenarios for the 2009 swine-origin influenza A (H1N1) epidemic. Our results indicate that lower transmission rates and earlier intervention activation times did not reduce total numbers of infected cases, but did delay peak times. When the transmission rate was decreased by a minimum of 70%, significant epidemic peak delays were observed when interventions were activated before new case number 50; no significant effects were noted when the transmission rate was decreased by less than 30%. Observed peaks occurred more quickly when initial outbreaks took place in urban rather than rural areas. According to our results, the MEDSim provides insights that reflect the dynamic processes of epidemics under different intervention scenarios, thus clarifying the effects of complex contact structures on disease transmission dynamics.