Limited stockpiles of vaccine and antiviral drugs and other resources pose a formidable health-care delivery challenge for an impending human-to-human transmittable influenza pandemic. The existing preparedness plans by the Center for Disease Control and Health and Human Services strongly underscore the need for efficient mitigation strategies. Such a strategy entails decisions for early response, vaccination, prophylaxis, hospitalization, and quarantine enforcement. This paper presents a large scale simulation model that mimics stochastic propagation of influenza pan-demic controlled by mitigation strategies. Impact of a pandemic is assessed via measures including total numbers of infected, dead, denied hospital admission, and denied vaccine/antiviral drugs, and also through an aggregate cost measure incorporating healthcare cost and lost wages. The model considers numerous demographic and community features, daily human activities, vacci-nation, prophylaxis, hospitalization, social distancing, and hourly accounting of infection spread. The simulation model can serve as the foundation for developing dynamic mitigation strategies. The simulation model is tested on a hypothetical community with over 1.1 million people. A designed experiment is conducted to examine the statistical significance of a number of model parameters. The experimental outcomes can be used in developing guidelines for strategic use of limited resources by healthcare decision makers. Finally, a Markov decision process (MDP) model and its simulation based reinforcement learning framework for developing mitigation strategies are presented. The simulation based framework is quite comprehensive and general, and can be particularized to other types of infectious disease outbreaks.