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

Efficient local behavioral change strategies to reduce the spread of epidemics in networks