{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T01:43:18Z","timestamp":1777513398189,"version":"3.51.4"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2007,6,13]],"date-time":"2007-06-13T00:00:00Z","timestamp":1181692800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2007,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats: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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats: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 (<jats:italic>p<\/jats:italic>&lt; 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats: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.<\/jats:p><\/jats:sec>","DOI":"10.1186\/1472-6947-7-15","type":"journal-article","created":{"date-parts":[[2007,7,13]],"date-time":"2007-07-13T18:13:46Z","timestamp":1184350426000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Automated real time constant-specificity surveillance for disease outbreaks"],"prefix":"10.1186","volume":"7","author":[{"given":"Shannon C","family":"Wieland","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John S","family":"Brownstein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bonnie","family":"Berger","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenneth D","family":"Mandl","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2007,6,13]]},"reference":[{"issue":"Suppl","key":"146_CR1","first-page":"56","volume":"53","author":"CM Yuan","year":"2004","unstructured":"Yuan CM, Love S, Wilson M: Syndromic surveillance at hospital emergency departments \u2013 Southeastern Virginia. 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