  It has recently become established that the spread of infectious diseases
between humans is affected not only by the pathogen itself but also by changes
in behavior as the population becomes aware of the epidemic; for example,
social distancing. It is also well known that community structure (the
existence of relatively densely connected groups of vertices) in contact
networks influences the spread of disease. We propose a set of local strategies
for social distancing, based on community structure, that can be employed in
the event of an epidemic to reduce the epidemic size. Unlike most social
distancing methods, ours do not require individuals to know the disease state
(infected or susceptible, etc.) of others, and we do not make the unrealistic
assumption that the structure of the entire contact network is known. Instead,
the recommended behavior change is based only on an individual's local view of
the network. Each individual avoids contact with a fraction of his/her
contacts, using knowledge of his/her local network to decide which contacts
should be avoided. If the behavior change occurs only when an individual
becomes ill or aware of the disease, these strategies can substantially reduce
epidemic size with a relatively small cost, measured by the number of contacts
avoided.
