Automated real time constant-specificity surveillance for disease outbreaks
<p>Abstract</p> <p>Background</p> <p>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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2007-06-01
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| Series: | BMC Medical Informatics and Decision Making |
| Online Access: | http://www.biomedcentral.com/1472-6947/7/15 |
| _version_ | 1811259850488807424 |
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| author | Brownstein John S Wieland Shannon C Berger Bonnie Mandl Kenneth D |
| author_facet | Brownstein John S Wieland Shannon C Berger Bonnie Mandl Kenneth D |
| author_sort | Brownstein John S |
| collection | DOAJ |
| description | <p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 (<it>p </it>< 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.</p> <p>Conclusion</p> <p>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.</p> |
| first_indexed | 2024-04-12T18:37:52Z |
| format | Article |
| id | doaj.art-6822076cfad743499a430abf431ba7f6 |
| institution | Directory Open Access Journal |
| issn | 1472-6947 |
| language | English |
| last_indexed | 2024-04-12T18:37:52Z |
| publishDate | 2007-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Informatics and Decision Making |
| spelling | doaj.art-6822076cfad743499a430abf431ba7f62022-12-22T03:20:52ZengBMCBMC Medical Informatics and Decision Making1472-69472007-06-01711510.1186/1472-6947-7-15Automated real time constant-specificity surveillance for disease outbreaksBrownstein John SWieland Shannon CBerger BonnieMandl Kenneth D<p>Abstract</p> <p>Background</p> <p>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.</p> <p>Results</p> <p>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 (<it>p </it>< 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.</p> <p>Conclusion</p> <p>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.</p>http://www.biomedcentral.com/1472-6947/7/15 |
| spellingShingle | Brownstein John S Wieland Shannon C Berger Bonnie Mandl Kenneth D Automated real time constant-specificity surveillance for disease outbreaks BMC Medical Informatics and Decision Making |
| title | Automated real time constant-specificity surveillance for disease outbreaks |
| title_full | Automated real time constant-specificity surveillance for disease outbreaks |
| title_fullStr | Automated real time constant-specificity surveillance for disease outbreaks |
| title_full_unstemmed | Automated real time constant-specificity surveillance for disease outbreaks |
| title_short | Automated real time constant-specificity surveillance for disease outbreaks |
| title_sort | automated real time constant specificity surveillance for disease outbreaks |
| url | http://www.biomedcentral.com/1472-6947/7/15 |
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