Name
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. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1186/1472-6947-7-15 | BMC Med. Inf. & Decision Making |
Keywords | Field | DocType |
disease outbreak,seasonality,algorithms,public health,cost effectiveness,health informatics,false alarm rate,real time,generalized additive model | Data mining,Disease,Outbreak,Constant false alarm rate,Health informatics,Medicine,West Nile virus | Journal |
Volume | Issue | ISSN |
7 | 1 | 1472-6947 |
Citations | PageRank | References |
4 | 1.05 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shannon C. Wieland | 1 | 4 | 1.05 |
| John S Brownstein | 2 | 191 | 21.62 |
| Bonnie Berger | 3 | 1643 | 165.84 |
| Kenneth D. Mandl | 4 | 275 | 67.17 |