Models of infectious diseases have been developed since the first half of the twentieth century. Most models haven't considered the role that emotional factors of the individual may play on the population's behavioral adaptation during the spread of a pandemic disease. Considering that local interactions among individuals generate patterns that -at a large scale- govern the action of masses, we have studied the behavioral adaptation of a population induced by the spread of an infectious disease. Therefore, we have developed a rule-based model of a hypothetical zombie outbreak, written in Kappa language, and simulated using Guillespie's stochastic approach. Our study addresses the specificity and heterogeneity of the system at the individual level, a highly desirable characteristic, mostly overlooked in classic epidemic models. Together with the basic elements of a typical epidemiological model, our model includes an individual representation of the disease progression and the traveling of agents among cities being affected. It also introduces an approximation to measure the effect of panic in the population as a function of the individual situational awareness. In addition, the effect of two possible countermeasures to overcome the zombie threat is considered: the availability of medical treatment and the deployment of special armed forces. However, due to the special characteristics of this hypothetical infectious disease, even using exaggerated numbers of countermeasures, only a small percentage of the population can be saved at the end of the simulations. As expected from a rule-based model approach, the global dynamics of our model resulted primarily governed by the mechanistic description of local interactions occurring at the individual level. As a whole, people's situational awareness resulted essential to modulate the inner dynamics of the system.