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BMC Med Inform Decis Mak. 2007 Jun 13;7:15.

Automated real time constant-specificity surveillance for disease outbreaks.

Author information

1
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139-4307, USA. shann@mit.edu <shann@mit.edu>

Abstract

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.

PMID:
17567912
PMCID:
PMC1919360
DOI:
10.1186/1472-6947-7-15
[Indexed for MEDLINE]
Free PMC Article

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