Title | Statistical Challenges Facing Early Outbreak Detection in Biosurveillance |
Publication Type | Journal Article |
Year of Publication | 2010 |
Authors | Shmueli, G., and H. S. Burkom |
Journal | Technometrics (Special Issue on Anomaly Detection) |
Volume | 52 |
Issue | 1 |
Pages | 39-51 |
Abstract | Modern biosurveillance is the monitoring of a wide-range of pre-diagnostic and diagnostic data for the purpose of enhancing the ability of the public health infrastructure to detect, investigate, and respond to disease outbreaks. Statistical control charts have been a central tool in classic dis-ease surveillance and have also migrated into modern biosurveillance. However, the new types of data monitored, the processes underlying the time series derived from these data, and the application context all deviate from the industrial setting for which these tools were originally designed. Assumptions of normality, independence, and stationarity are typically violated in syndromic time series; target values of process parameters are time-dependent and hard to define; data labeling is ambiguous in the sense that outbreak periods are not clearly defined or known. Additional challenges arise such as multiplicity in several dimensions, performance evaluation, and practical system |
Notes | A previous version was titled "Statistical Challenges in Modern Biosurveillance". |
URL | http://pubs.amstat.org/toc/tech/52/1 |
Attachment | Size |
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Previous version ("Statistical Challenges in Modern Biosurveillance") | 381.03 KB |
Published Version | 751.05 KB |