About: Enhancing Time-Series Detection Algorithms for Automated Biosurveillance

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Enhancing Time-Series Detection Algorithms for Automated Biosurveillance
Creator	Pavlin, Julie Bloom, Steven Burkom, Howard Cox, Kenneth English, Roseanne Xing, Jian Tokars, Jerome
Source	PMC
abstract	BioSense is a US national system that uses data from health information systems for automated disease surveillance. We studied 4 time-series algorithm modifications designed to improve sensitivity for detecting artificially added data. To test these modified algorithms, we used reports of daily syndrome visits from 308 Department of Defense (DoD) facilities and 340 hospital emergency departments (EDs). At a constant alert rate of 1%, sensitivity was improved for both datasets by using a minimum standard deviation (SD) of 1.0, a 14–28 day baseline duration for calculating mean and SD, and an adjustment for total clinic visits as a surrogate denominator. Stratifying baseline days into weekdays versus weekends to account for day-of-week effects increased sensitivity for the DoD data but not for the ED data. These enhanced methods may increase sensitivity without increasing the alert rate and may improve the ability to detect outbreaks by using automated surveillance system data.
has issue date	2009-04-01(xsd:dateTime)
bibo:doi	10.3201/1504.080616
bibo:pmid	19331728
has license	no-cc
sha1sum (hex)	2735c2e5b493fd4e7b7c4ec3d44a775f9399aa0d
schema:url	https://doi.org/10.3201/1504.080616
resource representing a document's title	Enhancing Time-Series Detection Algorithms for Automated Biosurveillance
has PubMed Central identifier	PMC2671446
has PubMed identifier	19331728
schema:publication	Emerg Infect Dis
resource representing a document's body	covid:2735c2e5b493fd4e7b7c4ec3d44a775f9399aa0d#body_text
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