title: Syndromic surveillance for influenzalike illness in ambulatory care network.
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.
authors.affiliation: University of Minnesota Department of Health, Minneapolis, Minnesota 55414, USA. Benjamin.miller@health.state.mn.us
authors.forename: Benjamin
authors.initials: B
authors.lastname: Miller
authors.forename: Heidi
authors.initials: H
authors.lastname: Kassenborg
authors.forename: William
authors.initials: W
authors.lastname: Dunsmuir
authors.forename: Jayne
authors.initials: J
authors.lastname: Griffith
authors.forename: Mansour
authors.initials: M
authors.lastname: Hadidi
authors.forename: James D
authors.initials: JD
authors.lastname: Nordin
authors.forename: Richard
authors.initials: R
authors.lastname: Danila
mesh_terms: D000553:Ambulatory Care; D023482:Bioterrorism; D004196:Disease Outbreaks; D006279:Health Maintenance Organizations; D006801:Humans; D007251:Influenza, Human / Q000453:epidemiology*; D007256:Information Systems; D008910:Minnesota; D008962:Models, Theoretical; D011159:Population Surveillance / Q000379:methods*; D012621:Seasons