Geographical effects on epidemic spreading in scale-free networks

Many real networks are embedded in a metric space: the interactions among individuals depend on their spatial distances and usually take place among their nearest neighbors. In this paper, we introduce a modified susceptible-infected- susceptible (SIS) model to study geographical effects on the spread of diseases by assuming that the probability of a. healthy individual infected by an infectious one is inversely proportional to the Euclidean distance between them. It is found that geography plays a more important role than hubs in disease spreading: the more geographically constrained the network is, the more highly the epidemic prevails. © World Scientific Publishing Company.