Introduction The CDC defines syndromic surveillance as a form of public health watching that employs automated data acquisition and statistical alerts to monitor disease indicators in real-time or near real-time. Its main goal is to detect beforehand large-scale infections of high sensitivity and specificity by identifying probable clusters of disease onset from prediagnostic data and comparing it with a threshold generated from a set of normal symptomatic cases (Patrick 2007). Besides, it provides information on the size and tempo of outbreaks trends and reassures the public. It uses continuous and routine data collection including patient counts at emergency departments, absentee counts, sales at medication counters, and laboratory test results to obtain information on the outbreak. The system operates in a sequence of stages by gathering details of symptoms and categorizing cases into syndromes before analyzing data to estimate probable outbreaks. Subsequently, it sends signals to public health departments to initiate responses but without triggering false alarms. Additionally, the system collects surrogate data such as work or school absenteeism apart from domestic and avian illness. It also uses alternative data sources and where the system is optimal with relatively high confidence, it can absorb integrated data from multiple sources. It employs automated electronic reporting to transfer data to a central database and utilizes statistical algorithms supported by complex information technology infrastructures to analyze data in generating a detectable daily count and nonspecific disease indicators (5) (May et al. 2009). Though the information it facilitates is not definitive, it produces a close estimation by use of probability of outbreak (Fricker 2006). Timeliness is a key attribute of the system. (Heffernan et al. 2004). Comparison with Traditional Surveillance System Syndromic surveillance is more complex as it uses informatics, statistics, and advanced epidemiological methods (Buehler 2004). On the other hand, traditional surveillance involves manual data collection through compulsory reporting of specific, diagnosed diseases to authorities (Green and Kaufman 2002). While the latter focuses on the initial stages of severe illness, the former tries to estimate and predict outbreaks a few days earlier, which facilitates time for disease management and thereby minimizes mortality (Figure 1, p. 8: What is Syndromic Surveillance?). Another major difference is that traditional surveillance systems use epidemiological methods by relying on retrospective studies to address specific risks, syndromic surveillance systems do not focus on specific risk events. As a result, the two systems differ in data aggregation, detection methods, and models used for detection (Fricker 2006). Further, traditional surveillance accounts for more accuracy because it depends on diagnostic data, whereas syndromic surveillance uses prediagnostic data. The general data source for the former is voluntary reports, while the latter employs data from protocols and automatic routines (Mandl et al. 2004). Since there is a need to balance different approaches and functions of the systems, it is better to use syndromic surveillance to enhance traditiona