In infectious disease epidemiology, one is strongly interested in predicting the evolution of a newly emerging pathogen or in monitoring the effects of targeted or universal intervention programs on infectious disease spread in a human population. For many of these research questions such as at the initial phase of a pandemic, 'chance' ("stochasticity") and heterogeneity in risks are key determinants on whether or not the infection will spread or mitigation strategies would be effective and cost-effective. Therefore, stochastic individual-based infectious disease models provide a valuable alternative to the hitherto widely applied deterministic compartmental models. Because of the computational complexity associated with the use of individual-based models in large populations, efficient programming techniques need to be developed and implemented to allow uncertainty analysis and meaningful calibration procedures. The central research questions of this project are fourfold: (1) Which is the most computationally efficient way to simulate an emerging infectious disease epidemic by means of a stochastic individual-based model? (2) Which is the most efficient way to conduct uncertainty analysis and calibration procedures in a stochastic individual-based model applied to pandemic influenza? (3) Which are the main factors that would influence the spread of pandemic influenza in Flanders? (4) Given key characteristics of pandemic influenza (scenarios defined in relation to the basic reproduction number, serial interval and morbidity and mortality in various groups of the population), which prevention and control measures are most effective and most cost-effective to mitigate their spread in Flanders? Initially, the model will be applied to Flanders but generic efficient programming is crucial to enable efficient application to other regions in the world and other emerging pathogens.