  The most frequent infectious diseases in humans - and those with the highest
potential for rapid pandemic spread - are usually transmitted via droplets
during close proximity interactions (CPIs). Despite the importance of this
transmission route, very little is known about the dynamic patterns of CPIs.
Using wireless sensor network technology, we obtained high-resolution data of
CPIs during a typical day at an American high school, permitting the
reconstruction of the social network relevant for infectious disease
transmission. At a 94% coverage, we collected 762,868 CPIs at a maximal
distance of 3 meters among 788 individuals. The data revealed a high density
network with typical small world properties and a relatively homogenous
distribution of both interaction time and interaction partners among subjects.
Computer simulations of the spread of an influenza-like disease on the weighted
contact graph are in good agreement with absentee data during the most recent
influenza season. Analysis of targeted immunization strategies suggested that
contact network data are required to design strategies that are significantly
more effective than random immunization. Immunization strategies based on
contact network data were most effective at high vaccination coverage.
