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dc.contributor.authorHagen, KSen_US
dc.contributor.authorFricker, RDen_US
dc.contributor.authorHanni, KDen_US
dc.contributor.authorBarnes, Sen_US
dc.contributor.authorMichie, Ken_US
dc.date.accessioned2016-12-28T01:08:08Z
dc.date.available2016-12-28T01:08:08Z
dc.date.issued2011-05en_US
dc.identifier.urihttp://hdl.handle.net/10919/73845
dc.description.abstractThe Early Aberration Reporting System (EARS) is used by some local health departments (LHDs) to monitor emergency room and clinic data for disease outbreaks. Using actual chief complaint data from local public health clinics, we evaluate how EARS—both the baseline system distributed by the CDC and two variants implemented by one LHD—perform at locally detecting the 2009 influenza A H1N1 pandemic. We also compare the EARS methods to a CUSUM-based method. We find that the baseline EARS system performed poorly in comparison to one of the LHD variants and the CUSUM-based method. These results suggest that changes in how syndromes are defined can substantially improve EARS performance. The results also show that incorporating algorithms that use more historical data will improve EARS performance for routine surveillance by local health departments.en_US
dc.format.extent30 - 30 page(s)en_US
dc.titleAssessing the Early Aberration Reporting System's Ability to Locally Detect the 2009 Influenza Pandemicen_US
dc.typeArticle - Refereed
dc.description.notesBecause the primary authors were US Government employees at the time of publication, the publisher does not hold the copyright.en_US
dc.title.serialStatistics, Politics and Policyen_US
dc.identifier.volume2en_US
dc.identifier.issue1en_US
pubs.organisational-group/Virginia Tech
pubs.organisational-group/Virginia Tech/All T&R Faculty
pubs.organisational-group/Virginia Tech/Science
pubs.organisational-group/Virginia Tech/Science/COS T&R Faculty
pubs.organisational-group/Virginia Tech/Science/Statistics


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