Displaying results 9 - 16 of 16
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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 -
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 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 -
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 -
Spatial Scanning Tips and Tricks for Practical Outbreak Detection
Content Type: Webinar
For its January 2011 Literature Review, the ISDS Research Committee invited Daniel B. Neill, PhD, Assistant Professor of Information Systems at Carnegie Mellon University, to present his paper, "An Empirical Comparison of Spatial Scan Statistics for… read more