{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,7,25]],"date-time":"2024-07-25T02:47:42Z","timestamp":1721875662512},"reference-count":10,"publisher":"Wiley","issue":"7","license":[{"start":{"date-parts":[[2005,5,20]],"date-time":"2005-05-20T00:00:00Z","timestamp":1116547200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Environmetrics"],"published-print":{"date-parts":[[2005,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Daily disease monitoring via a public health surveillance system provides valuable information on population risks. Efficient statistical tools for early detection of rapid changes in the disease incidence are a must for modern surveillance. The need for statistical tools for early detection of outbreaks that are not based on historical information is apparent. A system is discussed for monitoring cases of infections with a view to early detection of outbreaks and to forecasting the extent of detected outbreaks. We propose a set of adaptive algorithms for early outbreak detection that does not rely on extensive historical recording. We also include knowledge of infection disease epidemiology into forecasts. To demonstrate this system we use data from the largest water\u2010borne outbreak of cryptosporidiosis, which occurred in Milwaukee in 1993. Historical data are smoothed using a loess\u2010type smoother. Upon receipt of a new datum, the smoothing is updated and estimates are made of the first two derivatives of the smooth curve, and these are used for near\u2010term forecasting. Recent data and the near\u2010term forecasts are used to compute a color\u2010coded warning index, which quantify the level of concern. The algorithms for computing the warning index have been designed to balance Type I errors (false prediction of an epidemic) and Type II errors (failure to correctly predict an epidemic). If the warning index signals a sufficiently high probability of an epidemic, then a forecast of the possible size of the outbreak is made. This longer term forecast is made by fitting a \u2018signature\u2019 curve to the available data. The effectiveness of the forecast depends upon the extent to which the signature curve captures the shape of outbreaks of the infection under consideration. Copyright \u00a9 2005 John Wiley &amp; Sons, Ltd.<\/jats:p>","DOI":"10.1002\/env.734","type":"journal-article","created":{"date-parts":[[2005,5,20]],"date-time":"2005-05-20T15:48:54Z","timestamp":1116604134000},"page":"749-766","source":"Crossref","is-referenced-by-count":11,"title":["Signature\u2010forecasting and early outbreak detection system"],"prefix":"10.1002","volume":"16","author":[{"given":"Elena N.","family":"Naumova","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ian B.","family":"MacNeill","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2005,5,20]]},"reference":[{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.2105\/AJPH.91.8.1194"},{"key":"e_1_2_1_3_1","unstructured":"HutwagnerL ThompsonW GrosecloseS WilliamsonGD.2000.An evaluation of alternative methods for detecting aberrations in public health surveillance data. 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