Year: 2004
Type: article
Abstract: Conventional disease surveillance mechanisms that rely on passive reporting may be too slow and insensitive to rapidly detect a large-scale infectious disease outbreak; the reporting time from a patient's initial symptoms to specific disease diagnosis takes days to weeks. To meet this need, new surveillance methods are being developed. Referred to as nontraditional or syndromic surveillance, these new systems typically rely on prediagnostic data to rapidly detect infectious disease outbreaks, such as those caused by bioterrorism. Using data from a large health maintenance organization, we discuss the development, implementation, and evaluation of a time-series syndromic surveillance detection algorithm for influenzalike illness in Minnesota.
Source: Emerging infectious diseases
Authors: Benjamin Miller, Heidi Kassenborg, William Dunsmuir, Jayne Griffith, Mansour Hadidi, James D. Nordin, Richard Danila
Language: English
Subfield: Epidemiology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
Funder: University of Minnesota
OpenAlex ID: W2098488367
