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dc.contributor.authorCassa, Christopher Anthony
dc.contributor.authorIancu, Karin
dc.contributor.authorOlson, Karen Lea
dc.contributor.authorMandl, Kenneth David
dc.date.accessioned2011-03-08T04:27:33Z
dc.date.issued2005
dc.identifier.citationCassa, Christopher A., Karin Iancu, Karen L. Olson, and Kenneth D. Mandl. 2005. A software tool for creating simulated outbreaks to benchmark surveillance systems. BMC Medical Informatics and Decision Making 5: 22.en_US
dc.identifier.issn1472-6947en_US
dc.identifier.urihttp://nrs.harvard.edu/urn-3:HUL.InstRepos:4738539
dc.description.abstractBackground: Evaluating surveillance systems for the early detection of bioterrorism is particularly challenging when systems are designed to detect events for which there are few or no historical examples. One approach to benchmarking outbreak detection performance is to create semi-synthetic datasets containing authentic baseline patient data (noise) and injected artificial patient clusters, as signal. Methods: We describe a software tool, the AEGIS Cluster Creation Tool (AEGIS-CCT), that enables users to create simulated clusters with controlled feature sets, varying the desired cluster radius, density, distance, relative location from a reference point, and temporal epidemiological growth pattern. AEGIS-CCT does not require the use of an external geographical information system program for cluster creation. The cluster creation tool is an open source program, implemented in Java and is freely available under the Lesser GNU Public License at its Sourceforge website. Cluster data are written to files or can be appended to existing files so that the resulting file will include both existing baseline and artificially added cases. Multiple cluster file creation is an automated process in which multiple cluster files are created by varying a single parameter within a user-specified range. To evaluate the output of this software tool, sets of test clusters were created and graphically rendered. Results: Based on user-specified parameters describing the location, properties, and temporal pattern of simulated clusters, AEGIS-CCT created clusters accurately and uniformly. Conclusion: AEGIS-CCT enables the ready creation of datasets for benchmarking outbreak detection systems. It may be useful for automating the testing and validation of spatial and temporal cluster detection algorithms.en_US
dc.language.isoen_USen_US
dc.publisherBioMed Centralen_US
dc.relation.isversionofdoi:10.1186/1472-6947-5-22en_US
dc.relation.hasversionhttp://www.ncbi.nlm.nih.gov/pmc/articles/PMC1182374/pdf/en_US
dash.licenseLAA
dc.subjectAlgorithmsen_US
dc.subjectBenchmarkingen_US
dc.subjectBioterrorismen_US
dc.subjectCluster Analysisen_US
dc.subjectComputer Simulationen_US
dc.subjectDecision Support Techniquesen_US
dc.subjectDisease Outbreaksen_US
dc.subjectGeographyen_US
dc.subjectHumansen_US
dc.subjectPopulation Surveillanceen_US
dc.subjectSoftware Designen_US
dc.titleA software tool for creating simulated outbreaks to benchmark surveillance systemsen_US
dc.typeJournal Articleen_US
dc.description.versionVersion of Recorden_US
dc.relation.journalBMC Medical Informatics and Decision Makingen_US
dash.depositing.authorMandl, Kenneth David
dc.date.available2011-03-08T04:27:33Z
dash.affiliation.otherHMS^Pediatrics-Children's Hospitalen_US
dash.affiliation.otherHMS^Health Sciences and Technologyen_US
dash.affiliation.otherHMS^Pediatrics-Children's Hospitalen_US
dc.identifier.doi10.1186/1472-6947-5-22*
dash.contributor.affiliatedCassa, Christopher
dash.contributor.affiliatedOlson, Karen
dash.contributor.affiliatedMandl, Kenneth


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