  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.
