{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T09:44:26Z","timestamp":1768988666275,"version":"3.49.0"},"reference-count":16,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2005,6,21]],"date-time":"2005-06-21T00:00:00Z","timestamp":1119312000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"},{"start":{"date-parts":[[2005,6,21]],"date-time":"2005-06-21T00:00:00Z","timestamp":1119312000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/2.0\/"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                        <jats:title>Background<\/jats:title>\n                        <jats:p>Public health departments in the United States are beginning to gain timely access to health data, often as soon as one day after a visit to a health care facility. Consequently, new approaches to outbreak surveillance are being developed. When cases cluster geographically, an analysis of their spatial distribution can facilitate outbreak detection. Our method focuses on detecting perturbations in the distribution of pair-wise distances among all patients in a geographical region. Barring outbreaks, this distribution can be quite stable over time. We sought to exemplify the method by measuring its cluster detection performance, and to determine factors affecting sensitivity to spatial clustering among patients presenting to hospital emergency departments with respiratory syndromes.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Methods<\/jats:title>\n                        <jats:p>The approach was to (1) define a baseline spatial distribution of home addresses for a population of patients visiting an emergency department with respiratory syndromes using historical data; (2) develop a controlled feature set simulation by inserting simulated outbreak data with varied parameters into authentic background noise, thereby creating semisynthetic data; (3) compare the observed with the expected spatial distribution; (4) establish the relative value of different alarm strategies so as to maximize sensitivity for the detection of clustering; and (5) measure factors which have an impact on sensitivity.<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Results<\/jats:title>\n                        <jats:p>Overall sensitivity to detect spatial clustering was 62%. This contrasts with an overall alarm rate of less than 5% for the same number of extra visits when the extra visits were not characterized by geographic clustering. Clusters that produced the least number of alarms were those that were small in size (10 extra visits in a week, where visits per week ranged from 120 to 472), diffusely distributed over an area with a 3 km radius, and located close to the hospital (5 km) in a region most densely populated with patients to this hospital. Near perfect alarm rates were found for clusters that varied on the opposite extremes of these parameters (40 extra visits, within a 250 meter radius, 50 km from the hospital).<\/jats:p>\n                     <\/jats:sec><jats:sec>\n                        <jats:title>Conclusion<\/jats:title>\n                        <jats:p>Measuring perturbations in the interpoint distance distribution is a sensitive method for detecting spatial clustering. When cases are clustered geographically, there is clearly power to detect clustering when the spatial distribution is represented by the M statistic, even when clusters are small in size. By varying independent parameters of simulated outbreaks, we have demonstrated empirically the limits of detection of different types of outbreaks.<\/jats:p>\n                     <\/jats:sec>","DOI":"10.1186\/1472-6947-5-19","type":"journal-article","created":{"date-parts":[[2005,6,21]],"date-time":"2005-06-21T18:13:54Z","timestamp":1119377634000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Real time spatial cluster detection using interpoint distances among precise patient locations"],"prefix":"10.1186","volume":"5","author":[{"given":"Karen L","family":"Olson","sequence":"first","affiliation":[]},{"given":"Marco","family":"Bonetti","sequence":"additional","affiliation":[]},{"given":"Marcello","family":"Pagano","sequence":"additional","affiliation":[]},{"given":"Kenneth D","family":"Mandl","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2005,6,21]]},"reference":[{"issue":"2","key":"70_CR1","doi-asserted-by":"publisher","first-page":"141","DOI":"10.1197\/jamia.M1356","volume":"11","author":"KD Mandl","year":"2004","unstructured":"Mandl KD, Overhage JM, Wagner MM, Lober WB, Sebastiani P, Mostashari F, Pavlin JA, Gesteland PH, Treadwell T, Koski E: Implementing syndromic surveillance: a practical guide informed by the early experience. Journal of the American Medical Informatics Association. 2004, 11 (2): 141-150. 10.1197\/jamia.M1356.","journal-title":"Journal of the American Medical Informatics Association"},{"issue":"1","key":"70_CR2","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1186\/1472-6947-5-4","volume":"5","author":"JC Brillman","year":"2005","unstructured":"Brillman JC, Burr T, Forslund D, Joyce E, Picard R, Umland E: Modeling emergency department visit patterns for infectious disease complaints: results and application to disease surveillance. BMC Med Inform Decis Mak. 2005, 5 (1): 4-10.1186\/1472-6947-5-4.","journal-title":"BMC Med Inform Decis Mak"},{"issue":"5","key":"70_CR3","doi-asserted-by":"publisher","first-page":"858","DOI":"10.3201\/eid1005.030646","volume":"10","author":"R Heffernan","year":"2004","unstructured":"Heffernan R, Mostashari F, Das D, Karpati A, Kulldorff M, Weiss D: Syndromic surveillance in public health practice, New York City. Emerg Infect Dis. 2004, 10 (5): 858-864.","journal-title":"Emerg Infect Dis"},{"key":"70_CR4","unstructured":"Update 15 \u2013 Situation in Hong Kong, activities of WHO team in China. http:\/\/www.who.int\/csr\/sarsarchive\/2003_03_31\/en\/"},{"key":"70_CR5","volume-title":"Monitoring the health of populations: Statistical principles and methods for public health surveillance","author":"R Brookmeyer","year":"2004","unstructured":"Brookmeyer R, Stroup DF: Monitoring the health of populations: Statistical principles and methods for public health surveillance. 2004, Oxford University Press"},{"issue":"1","key":"70_CR6","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1111\/1467-985X.00256","volume":"166","author":"C Sonesson","year":"2003","unstructured":"Sonesson C, Bock D: A review and discussion of prospective statistical surveillance in public health. J Royal Statistical Soc A. 2003, 166 (1): 5-21. 10.1111\/1467-985X.00256.","journal-title":"J Royal Statistical Soc A"},{"key":"70_CR7","volume-title":"Bioterrorism: Mathematical modeling applications in homeland security","author":"M Bonetti","year":"2004","unstructured":"Bonetti M, Forsberg L, Ozonoff A, Pagano M: The distribution of interpoint distances. Bioterrorism: Mathematical modeling applications in homeland security. Edited by: Banks HT, Castillo-Chaves C. 2004, Philadelphia: SIAM"},{"issue":"5","key":"70_CR8","doi-asserted-by":"publisher","first-page":"753","DOI":"10.1002\/sim.1947","volume":"24","author":"M Bonetti","year":"2005","unstructured":"Bonetti M, Pagano M: The interpoint distance distribution as a descriptor of point patterns, with an application to spatial disease clustering. Stat Med. 2005, 24 (5): 753-773. 10.1002\/sim.1947.","journal-title":"Stat Med"},{"issue":"Suppl","key":"70_CR9","first-page":"256","volume":"53","author":"KL Olson","year":"2004","unstructured":"Olson KL, Bonetti M, Pagano M, Mandl KD: A population-adjusted stable geospatial baseline for outbreak detection in syndromic surveillance. Morbidity & Mortality Weekly Report, Syndromic Surveillance Reports. 2004, 53 (Suppl): 256-","journal-title":"Morbidity & Mortality Weekly Report, Syndromic Surveillance Reports"},{"key":"70_CR10","volume-title":"Proceedings of the American Statistical Association, Biometrics Section [CDROM]","author":"M Bonetti","year":"2003","unstructured":"Bonetti M, Olson KL, Mandl KD, Pagano M: Parametric models for interpoint distances and their use in biosurveillance. Proceedings of the American Statistical Association, Biometrics Section [CDROM]. 2003"},{"issue":"Suppl","key":"70_CR11","first-page":"130","volume":"53","author":"KD Mandl","year":"2004","unstructured":"Mandl KD, Reis BY, Cassa C: Measuring outbreak-detection performance by using controlled feature set simulations. Morbidity & Mortality Weekly Report, Syndromic Surveillance Reports. 2004, 53 (Suppl): 130-136.","journal-title":"Morbidity & Mortality Weekly Report, Syndromic Surveillance Reports"},{"issue":"6","key":"70_CR12","doi-asserted-by":"publisher","first-page":"355","DOI":"10.1097\/01.pec.0000133608.96957.b9","volume":"20","author":"AJ Beitel","year":"2004","unstructured":"Beitel AJ, Olson KL, Reis BY, Mandl KD: Use of emergency department chief complaint and diagnostic codes for identifying respiratory illness in a pediatric population. Pediatric Emergency Care. 2004, 20 (6): 355-360. 10.1097\/01.pec.0000133608.96957.b9.","journal-title":"Pediatric Emergency Care"},{"issue":"Suppl","key":"70_CR13","first-page":"231","volume":"53","author":"C Cassa","year":"2004","unstructured":"Cassa C, Olson KL, Mandl KD: A system to generate outbreak clusters for semisynthetic datasets to evaluate outbreak detection performance. Morbidity & Mortality Weekly Report, Syndromic Surveillance Reports. 2004, 53 (Suppl): 231-","journal-title":"Morbidity & Mortality Weekly Report, Syndromic Surveillance Reports"},{"issue":"1","key":"70_CR14","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/1472-6947-3-2","volume":"3","author":"BY Reis","year":"2003","unstructured":"Reis BY, Mandl KD: Time series modeling for syndromic surveillance. BMC Med Inform Decis Mak. 2003, 3 (1): 2-10.1186\/1472-6947-3-2. [http:\/\/www.biomedcentral.com\/1472-6947\/3\/2]","journal-title":"BMC Med Inform Decis Mak"},{"key":"70_CR15","first-page":"71","volume-title":"Monitoring the health of populations","author":"O Devine","year":"2004","unstructured":"Devine O: Exploring temporal and spatial patterns in public health surveillance data. Monitoring the health of populations. Edited by: Brookmeyer R, Stroup DF. 2004, Oxford: Oxford University Press, 71-98."},{"key":"70_CR16","volume-title":"Proceedings of the American Statistical Association, Biometrics Section [CDROM]","author":"A Ozonoff","year":"2003","unstructured":"Ozonoff A, Bonetti M, Forsberg L, Pagano M: The use of multiple addresses to enhance cluster detection. Proceedings of the American Statistical Association, Biometrics Section [CDROM]. 2003"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1472-6947-5-19.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/1472-6947-5-19\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/1472-6947-5-19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/1472-6947-5-19.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T12:36:03Z","timestamp":1728304563000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/1472-6947-5-19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2005,6,21]]},"references-count":16,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2005,12]]}},"alternative-id":["70"],"URL":"https:\/\/doi.org\/10.1186\/1472-6947-5-19","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2005,6,21]]},"assertion":[{"value":"8 December 2004","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2005","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 June 2005","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}],"article-number":"19"}}