This paper presents a means of collecting and analyzing data related to personal social contact networks. A custom application is developed for smartphones that support Bluetooth connectivity, as representative of the ensemble of many consumer electronic products, to infer users’ location and proximity to one another, the duration of such proximity (‘contact’)，and GPS-based information. In many instances of testing the application in this work, this is augmented by device meta-identity. The smartphone application and data storage and retrieval are discussed in detail. Preliminary data were collected (device-device proximity, proximity duration, and location) in pilot testing on the Blackberry Storm and HTC Hero (Android) smartphones. Data are presented as distributions and visualization tools for evolving contact graphs, including Pareto distributions and power law exponents representing face-to-face contacts. Extracted parameters are useful for estimating the potential of infection spread (e.g., respiratory illness), where a key transmission vector is person-person contact. A variant of the standard SEIR individual-based model is developed, with individual contact patterns guided by contact distributions extracted from the smartphone proximity data. Finally, a detailed agent-based model (ABM) of a small community is developed and the spread of an infectious disease is simulated. The data from the ABM is then analyzed in terms of proximity distributions across various demographic profiles, illustrating the utility of the proposed data collection technologies in supporting advancing modeling and simulation efforts associated with infectious diseases.