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The recent adoption of ubiquitous computing technologies has enabled capturing large amounts of human behavioral data. The digital footprints computed from these datasets provide information for the study of social and human dynamics, including social networks and mobility patterns, key elements for the effective modeling of virus spreading. Traditional epidemiologic models do not consider individual information and hence have limited ability to capture the inherent complexity of the disease spreading process. To overcome this limitation, agent-based models have recently been proposed as an effective approach to model virus spreading. However, most agent-based approaches to date have not included real-life data to characterize the agents' behavior. In this paper we propose an agent-based system that uses social interactions and individual mobility patterns extracted from call detail records to accurately model virus spreading. The proposed approach is applied to study the 2009 H1N1 outbreak in Mexico and to evaluate the impact that government mandates had on the spreading of the virus. Our simulations indicate that the restricted mobility due the government mandates reduced by 10% the peak number of individuals infected by the virus and postponed the peak of the pandemic by two days.