A new breed of global disease surveillance systems has the potential to significantly speed up detection of disease outbreaks. They often rely on intelligent systems and databases, infectious disease informatics, and advanced analytic techniques such as time-series analysis, text mining, agent-based modeling, social-network analysis, and disease modeling, visualization, and mapping. Researchers have proposed several syndromic surveillance approaches. These systems, although sharing similar objectives, vary in system architecture, information processing and management techniques, and algorithms for anomaly detection, and they have different geographic coverage and disease focuses. Each syndromic surveillance system implements a unique set of outbreak detection algorithms. Systematic, field-based, objective comparative studies among systems are critically needed for surveillance system evaluation and comparison.
|Original language||English (US)|
|Number of pages||4|
|Journal||IEEE Intelligent Systems|
|State||Published - Nov 1 2009|
ASJC Scopus subject areas
- Computer Networks and Communications
- Artificial Intelligence