Displaying results 9 - 16 of 30
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An Empirical Comparison of Spatial Scan Statistics for Outbreak Detection
Content Type: Abstract
We present a systematic empirical comparison of five recently proposed expectation-based scan statistics, in order to determine which methods are most successful for which spatial disease surveillance tasks. -
Fast and Flexible Outbreak Detection by Linear-Time Subset Scanning
Content Type: Abstract
The spatial scan statistic [1] detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over a large set of spatial regions. Typical spatial scan approaches either constrain the search regions to… read more -
Monitoring Pharmacy Retail Data for Anomalous Space-Time Clusters
Content Type: Abstract
Bio-surveillance systems monitor multiple data streams (over-the-counter (OTC) sales, Emergency Department visits, etc.) to detect both natural disease outbreaks (e.g. influenza) and bio-terrorist attacks (e.g. anthrax re-lease). Many detection… read more -
Incorporating Learning into Disease Surveillance Systems
Content Type: Abstract
Current state-of-the-art outbreak detection methods [1-3] combine spatial, temporal, and other covariate information from multiple data streams to detect emerging clusters of disease. However, these approaches use fixed methods and models for… read more -
Learning Outbreak Regions for Bayesian Spatial Biosurveillance
Content Type: Abstract
This work incorporates model learning into a Bayesian framework for outbreak detection. Our method learns the spatial characteristics of each outbreak type from a small number of labeled training examples, assuming a generative outbreak model with… read more -
Tracking Dynamic Water-borne Outbreaks with Temporal Consistency Constraints
Content Type: Abstract
Space-time scan statistics are often used to identify emerging spatial clusters of disease cases [1,2]. They operate by maximizing a score function (likelihood ratio statistic) over multiple spatio-temporal regions. The temporal component is… read more -
Fast Multidimensional Subset Scan for Outbreak Detection and Characterization
Content Type: Abstract
The multivariate linear-time subset scan (MLTSS) extends previous spatial and subset scanning methods to achieve timely and accurate event detection in massive multivariate datasets, efficiently optimizing a likelihood ratio statistic over… read more -
An Empirical Comparison of Spatial Scan Statistics for Outbreak Detection
Content Type: Abstract
Expectation-based scan statistics extend the traditional spatial scan statistic approach by using historical data to infer the expected counts for each spatial location, then detecting regions with higher than expected counts. Here we consider five… read more