Link to home

Cannot retrieve the URL specified in the Content Link property. For more assistance, contact your site administrator.

Emergence of unified concepts of disease in textual surveillance data.
C. S. THOMAS (1), N. P. Nelson (2). (1) University of California-Davis, Department of Plant Pathology, Davis, CA, U.S.A.; (2) Georgetown University Medical Center, Washington, DC, U.S.A.

Technology has increased the amount of text based information available for use in biosurviellance. Large text-based repositories are evolving to include relational databases of test results, citizen observations, social networks, expert opinion and news media. Additionally, database interconnectivity is increasing rapidly. Human health surveillance has pioneered syndromic analysis of diagnostic test results, pharmaceutical purchase, absenteeism in schools and the workplace, and other case count-based analysis to inform situational awareness. With the development of the National Plant Diagnostic Network Repository, national and regional syndromic clinic analysis is now possible. Initially, local repositories consisted of open form text that was unstandardized. Analysis showed certain words are used more frequently than others. As technology and data streams improve, and media becomes near-real time, it is possible to recognize indicators and warning of potential or emerging outbreaks earlier. Additionally, public media analysis indicated the progress of an epidemic can be discerned based on what terms the communicators choose. Such an understanding of a communicator’s choice of terms can be exploited to identify epidemic stages and severity. Biosurveillance programs have developed “taxonomies” of word choices to identify escalation of an event and tipping points as very early indicators and warning of an outbreak. Syndromic and media case examples are presented.<p><p>Keywords:

View Presentation