However, social network structures are clearly not always well mixed, and the complexities of host interactions may have profound implications for the interpretation of epidemiological models and clinical data. To overcome the simplifying assumption of classical transmission models, a new type of model is gaining recognition. Agent-based models (ABMs) represent an important new approach for describing interacting heterogeneous agents. The heterogeneous aspect of agents enables more sophisticated and complex environments to be described by ABMs approaches. Also, it is typical to introduce a geospatial dimension into the framework so that both time and geographical patterns are represented. Thus, every ABM run identifies infected persons where they live and work in general, and their movements within groups (referred to as social networks) that influence disease spread. ABMs have been used to describe phenomena from social systems to immune systems, both of which are distributed collections of interacting entities (agents) that function without a leader. Simple agents interact locally according to simple rules of behavior, responding in appropriate ways to environmental cues and not necessarily striving to achieve an overall goal. An ABM consists of a set of agents that encapsulate the behaviors of the individuals that make up the system, and model execution consists of emulating these behaviors [2] .