Displaying results 1 - 8 of 30
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Fast Graph Structure Learning from Unlabeled Data for Outbreak Detection
Content Type: Abstract
Disease surveillance data often has an underlying network structure (e.g. for outbreaks which spread by person-to-person contact). If the underlying graph structure is known, detection methods such as GraphScan (1) can be used to identify an… 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 -
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 -
Support Vector Subset Scan for Spatial Outbreak Detection
Content Type: Abstract
Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan… 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 -
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 -
Non-Parametric Scan Statistics for Disease Outbreak Detection on Twitter
Content Type: Abstract
Disease outbreak detection based on traditional surveillance datasets, such as disease cases reported from hospitals, is potentially limited in that the collection of clinic information is costly and time consuming. However, social media provides… read more