Displaying results 9 - 16 of 30
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Detecting and Preventing Emerging Epidemics of Crime
Content Type: Abstract
We apply recently developed spatial biosurveillance techniques to the law enforcement domain, with the goal of helping local police departments to rapidly detect and respond to (or better yet, to predict and prevent) emerging spatial patterns of… 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 -
Fast subset scan for multivariate spatial biosurveillance
Content Type: Abstract
The spatial scan statistic detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over a large set of spatial regions. Several recent approaches have extended spatial scan to multiple data streams. Burkom… read more -
Generalized fast subset sums for Bayesian detection and visualization
Content Type: Abstract
The multivariate Bayesian scan statistic (MBSS) enables timely detection and characterization of emerging events by integrating multiple data streams. MBSS can model and differentiate between multiple event types: it uses Bayes’ Theorem to… 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 -
Detecting Previously Unseen Outbreaks with Novel Symptom Patterns
Content Type: Abstract
Commonly used syndromic surveillance methods based on the spatial scan statistic first classify disease cases into broad, pre-existing symptom categories ("prodromes") such as respiratory or fever, then detect spatial clusters where the recent… read more -
Scalable Detection of Irregular Disease Clusters Using Soft Compactness Constraints
Content Type: Abstract
The spatial scan statistic [1] detects significant spatial clusters of disease by maximizing a likelihood ratio statistic F(S) over a large set of spatial regions, typically constrained by shape. The fast localized scan [2] enables scalable… 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