Dynamic social networks for the Boston area (4.1 million individuals) were constructed based on data for individuals, locations and activity patterns collected from the real world. We modeled a full season of endemic influenza-like illnesses (ILI), healthcare seeking behavior and a surveillance system for outpatient visits. The resulting in silico surveillance data contain the demographics and complete history of disease progression for all individuals in the population; those who are in a specified surveillance system create a data stream of ILI visits. Outbreaks of influenza are artificially inserted into this surveillance data. Outbreak detection using space-and-time scan statistics was used to analyze the background with and without the inserted outbreaks. The performance of the algorithm was assessed under different levels of coverage and catchment distributions. One hundred unique baseline data sets were generated. Twelve artificial outbreaks were inserted in each. Six different surveillance system designs were assessed.