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Models of infectious diseases have been developed since the first half of thetwentieth century. Most models haven't considered the role that emotionalfactors of the individual may play on the population's behavioral adaptationduring the spread of a pandemic disease. Considering that local interactionsamong individuals generate patterns that -at a large scale- govern the actionof masses, we have studied the behavioral adaptation of a population induced bythe spread of an infectious disease. Therefore, we have developed a rule-basedmodel of a hypothetical zombie outbreak, written in Kappa language, andsimulated using Guillespie's stochastic approach. Our study addresses thespecificity and heterogeneity of the system at the individual level, a highlydesirable characteristic, mostly overlooked in classic epidemic models.Together with the basic elements of a typical epidemiological model, our modelincludes an individual representation of the disease progression and thetraveling of agents among cities being affected. It also introduces anapproximation to measure the effect of panic in the population as a function ofthe individual situational awareness. In addition, the effect of two possiblecountermeasures to overcome the zombie threat is considered: the availabilityof medical treatment and the deployment of special armed forces. However, dueto the special characteristics of this hypothetical infectious disease, evenusing exaggerated numbers of countermeasures, only a small percentage of thepopulation can be saved at the end of the simulations. As expected from arule-based model approach, the global dynamics of our model resulted primarilygoverned by the mechanistic description of local interactions occurring at theindividual level. As a whole, people's situational awareness resulted essentialto modulate the inner dynamics of the system.

A Rule-based Model of a Hypothetical Zombie Outbreak: Insights on the  role of emotional factors during behavioral adaptation of an artificial  population