Highly infectious diseases such as SARS (Severe Acute Respiratory Syndrome), Avian Influenza (Bird Flu), Small Pox, and currently Swine Flu, to name but a few, pose a significant threat to the global population. Detection and prevention of infectious diseases is notoriously complex and problematic due to the ever increasing number of international travellers. In addition, the risk of being infected with an infectious disease in densely populated urban areas tends to be much higher compared to rural areas. When an outbreak occurs, the detection of source of infection (or index case), clusters of cases and transmission routes in a rapid manner is crucial in preventing the infectious disease from further spreading. Contact tracing has proven to be helpful for these detections. Traditionally, contact tracing is a field work of the medical personnel with little assistance of IT (Information Technology), if any. During the worldwide outbreak of SARS in 2003, HCIS (Health Care Information Systems) were built to facilitate contact tracing. However, contact tracing, and thus the detection process, is not a fully automatic process in these systems. In this paper, with SARS as a case study, we realize detection as an automatic process by applying algorithms and data mining techniques in the patients' activities and social interaction together with characteristics of the infectious disease.