Pandemic outbreaks are unpredictable as to their virus strain, transmissibility, and impact on our quality of life. Hence, the decision support models for mitigation of pandemic outbreaks must be user-friendly and operational, and also incorporate valid estimates of disease transmissibility and severity. This dissertation research is aimed at 1) reviewing the existing pandemic simulation models to identify their implementation gaps with regard to usability and operability, and suggesting research remedies, 2) increasing operability of simulation models by calibrating them via an epidemiological model that estimates infection probabilities using viral shedding profiles of concurrent pandemic and seasonal influenza, and 3) developing a testing strategy for the state laboratories, with their limited capacities, to improve their ability to estimate evolving transmissibility parameters. Our review of literature (Aim 1) indicates the need to continue model enhancements in critical areas including updating of epidemiological data during a pandemic, smooth handling of large demographical databases, incorporation of a broader spectrum of social-behavioral aspects, and improvement of computational efficiency and accessibility. As regards the ease of calibration (Aim 2), we demonstrate that the simulation models, when driven by the infection probabilities obtained from our epidemiological model, accurately reproduce the disease transmissibility parameters. Assuming the availability of sufficient disease reporting infrastructure and strong compliance by both infected population and healthcare providers, our testing strategy (Aim 3) adequately supports characterization of real-time epidemiological parameters. Future research on this topic will be aimed at integrating the laboratory testing strategy with our modeling and simulation approach to develop dynamic mitigation strategies for pandemic outbreaks.