Displaying results 9 - 16 of 16
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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 -
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 -
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 -
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 -
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 -
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 -
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