Most previous models for eqidemic spreading are based on an assumption that all individual characteristics in a population are identical and stochastically distributed. However, in the real world, individuals have different behavioral characteristics for resisting infections, one of which is self-protective behavior. In this study, we suggest an equation describing self-protective behavior and use the equation in an agent-based model to simulate eqidemic spreading in a population. The self-protective behavior was simply defined as the behavioral rule that when a susceptible individual meets and infective individual, the susceptible tends to in the opposite direction of the infective individual. The degree of the tendency was quantified as a vlaue of E rangign from 0.0 to 1.0, with a higher E representing a stonger thendency. The simaualtion results showd that when the recovery and infection probability are balnced to some extent, the E effect clearly appeared. The E effect led to reduction in the number of infective individuals in a stable state. In addition, the effect decreased with an increase in population size. We briefly discuss how the restults can applied in real life situations.
Most previous models for eqidemic spreading are based on an assumption that all individual characteristics in a population are identical and stochastically distributed. However, in the real world, individuals have different behavioral characteristics for resisting infections, one of which is self-protective behavior. In this study, we suggest an equation describing self-protective behavior and use the equation in an agent-based model to simulate eqidemic spreading in a population. The self-protective behavior was simply defined as the behavioral rule that when a susceptible individual meets and infective individual, the susceptible tends to in the opposite direction of the infective individual. The degree of the tendency was quantified as a vlaue of E rangign from 0.0 to 1.0, with a higher E representing a stonger thendency. The simaualtion results showd that when the recovery and infection probability are balnced to some extent, the E effect clearly appeared. The E effect led to reduction in the number of infective individuals in a stable state. In addition, the effect decreased with an increase in population size. We briefly discuss how the restults can applied in real life situations.