Automated real time constant-specificity surveillance for disease outbreaks
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Automated real time constant-specificity surveillance for disease outbreaks
| Title: | Automated real time constant-specificity surveillance for disease outbreaks |
| Author: |
Wieland, Shannon C; Berger, Bonnie; Brownstein, John Samuel; Mandl, Kenneth David
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Wieland, Shannon C, John S Brownstein, Bonnie Berger, and Kenneth D Mandl. 2007. Automated real time constant-specificity surveillance for disease outbreaks. BMC Medical Informatics and Decision Making 7: 15. |
| Full Text & Related Files: |
1919360.pdf (386.2Kb; PDF) 
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| 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. |
| Published Version: | doi://10.1186/1472-6947-7-15 |
| Other Sources: | http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1919360/pdf/ |
| Terms of Use: | This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA |
| Citable link to this page: | http://nrs.harvard.edu/urn-3:HUL.InstRepos:4908014 |
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