  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.
