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Emerging diseases, novel strains of reemerging diseases, and bioterrorism threats necessitate the development of computational models that can supply health care providers with tools to facilitate analysis and simulation of the progression of infectious diseases in a population. Most computational models assume homogeneous mixing within populations. However, a more realistic approach to the simulation of infectious disease outbreaks includes the stratification of populations in which the interactions between individuals are affinity-based. To examine the effects of heterogeneous populations on the outbreak dynamics, we developed a hybrid model that includes clustered individuals which represent differentiated populations. This facilitates the study of the effects of distinct behavioral properties on the dynamics of an infectious disease epidemic. Our results indicate that non-uniform interactions and affinity-driven behavior can drastically change the outbreak dynamics in the population.

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