AI for global disease surveillance

Research output: Contribution to journalArticle

11 Scopus citations

Abstract

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 languageEnglish (US)
Pages (from-to)66-69
Number of pages4
JournalIEEE Intelligent Systems
Volume24
Issue number6
StatePublished - Nov 1 2009

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ASJC Scopus subject areas

  • Computer Networks and Communications
  • Artificial Intelligence

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