How does social distancing aect the reach of an epidemic in social
networks? We present Monte Carlo simulation results of a Susceptible-
Infected-Removed with Social Distancing (SIRwSD) model. The key
feature of the model is that individuals are limited in the number of acquaintances that they can interact with, thereby constraining disease
transmission to an infectious subnetwork of the original social net-
work. While increased social distancing typically reduces the spread
of an infectious disease, the magnitude varies greatly depending on
the topology of the network indicating the need for policies that are
network-dependent. Our results also reveal the importance of coordinating policies at the `global' level. In particular, the public health
benets from social distancing to a group (e.g., a country) may be
completely undone if that group maintains connections with outside
groups that are not following suit.