Interaction networks shaped by social processes constitute the substrate on which various phenomena of interest to human biology occur, for example, epidemics, diffusion of health information, and the exertion of social influence related to health. Understanding the structure of network formation is thus crucial to our understanding of how relational human interactions mediate key biosocial outcomes. Research in this area has been hampered by a lack of high-quality data on the formation and structure of contact networks. Using Wireless Sensor Network (WSN) technology, measured the temporal dynamics of close-proximity interaction networks during a typical school day in a high school in the San Francisco Bay Area. Participants wore small wireless sensors which send and receive radio signals to and from other sensors nearby. This technology allowed us to collect dynamic contact network data with unparalleled precision. 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