of medical data to detect or anticipate disease outbreaks. According to a CDC definition, "the term 'syndromic surveillance' applies to surveillance using health-related data that precede diagnosis and signal a sufficient probability of a case or an outbreak to warrant further public health response. Though historically syndromic surveillance has been utilized to target investigation of potential cases, its utility for detecting outbreaks associated with bioterrorism is increasingly being explored by public health officials." The first indications of disease outbreak or bioterrorist attack may not be the definitive diagnosis of a physician or a lab. Using a normal influenza outbreak as an example, once the outbreak begins to affect the population, some people may call in sick for work/school, others may visit their drug store and purchase medicine over the counter, others will visit their doctor's office and other's may have symptoms severe enough that they call the emergency telephone number or go to an emergency department. Syndromic surveillance systems monitor data from school absenteeism logs, emergency call systems, hospitals' over-the-counter drug sale records, Internet searches, and other data sources to detect unusual patterns. When a spike in activity is seen in any of the monitored systems disease epidemiologists and public health professionals are alerted that there may be an issue. An early awareness and response to a bioterrorist attack could save many lives and potentially stop or slow the spread of the outbreak. The most effective syndromic surveillance systems automatically monitor these systems in real-time, do not require individuals to enter separate information (secondary data entry), include advanced analytical tools, aggregate data from multiple systems, across geo-political boundaries and include an automated alerting process. A syndromic surveillance system based on search queries was first proposed by Gunther Eysenbach, who began work on such a system in 2004. Inspired by these early, encouraging experiences, Google launched Google Flu Trends in 2008. More flu-related searches are taken to indicate higher flu activity. The results, which were published in Nature, closely matched CDC data, and led it by 1–2 weeks. However, it has been shown that the original approach behind Google Flu Trends had various modelling deficiencies leading to significant errors in its estimates. More recently, a series of more advanced linear and nonlinear approaches to influenza modeling from Google search queries have been proposed. Extending Google's work researchers from the Intelligent Systems Laboratory (University of Bristol, UK) created Flu Detector; an online