Automated real time constant-specificity surveillance for disease outbreaks.

Automated real time constant-specificity surveillance for disease outbreaks. Research Abstract Details 

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Automated real time constant-specificity surveillance for disease outbreaks. Abstract Text:

Shannon C Wieland,John S Brownstein,Bonnie Berger,Kenneth D Mandl,

BACKGROUND: For real time surveillance, detection of abnormal disease patterns is based on a difference between patterns observed, and those predicted by models of historical data. The usefulness of outbreak detection strategies depends on their specificity; the false alarm rate affects the interpretation of alarms. RESULTS: We evaluate the specificity of five traditional models: autoregressive, Serfling, trimmed seasonal, wavelet-based, and generalized linear. We apply each to 12 years of emergency department visits for respiratory infection syndromes at a pediatric hospital, finding that the specificity of the five models was almost always a non-constant function of the day of the week, month, and year of the study (p < 0.05). We develop an outbreak detection method, called the expectation-variance model, based on generalized additive modeling to achieve a constant specificity by accounting for not only the expected number of visits, but also the variance of the number of visits. The expectation-variance model achieves constant specificity on all three time scales, as well as earlier detection and improved sensitivity compared to traditional methods in most circumstances. CONCLUSION: Modeling the variance of visit patterns enables real-time detection with known, constant specificity at all times. With constant specificity, public health practitioners can better interpret the alarms and better evaluate the cost-effectiveness of surveillance systems.

Automated real time constant-specificity surveillance for disease outbreaks. Publishing Authors By Initials

SC Wieland,JS Brownstein,B Berger,KD Mandl,

For similar natural sciences: time research abstracts see: natural sciences: time research

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Automated real time constant-specificity surveillance for disease outbreaks. Journal Published:

PUBLICATION TYPE: Research Support, N.I.H., Extr

Journal: BMC medical informatics and decision making

VOLUME: 7

Page Numbers: 15

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ISSN: 1472-6947

DAY: 13

MONTH: 06

YEAR: 2007

Automated real time constant-specificity surveillance for disease outbreaks. Information

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LANGUAGE: eng

NlmUniqueID: 101088682

Automated real time constant-specificity surveillance for disease outbreaks. Keywords Mesh Terms:

KEYWORDS: Time

MESH TERMS: utilization

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Grant and Affiliation Information for Automated real time constant-specificity surveillance for disease outbreaks.

AFFILIATION: Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA. shann@mit.edu <shann@mit.edu>

Country: England

AGENCY: United States NLM

GRANT: R21LM009263-01

ACRONYM: LM

MEDLINETA: BMC Med Inform Decis Mak

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