Displaying results 9 - 16 of 30
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A Robust Expectation-Based Spatial Scan Statistic
Content Type: Abstract
This paper describes a new expectation-based scan statistic that is robust to outliers (individual anomalies at the store level that are not indicative of outbreaks). We apply this method to prospective monitoring of over-the-counter (OTC) drug… read more -
Virtual Speed Networking with the Analytic Solutions Committee (ASC)
Content Type: Webinar
Presented January 11, 2018. The purpose of the event was to stimulate and facilitate constructive communication and collaboration among analytic method developers and practitioners charged with routine public health surveillance, ranging from… read more -
A Bayesian Scan Statistic for Spatial Cluster Detection
Content Type: Abstract
This paper develops a new Bayesian method for cluster detection, the ìBayesian spatial scan statistic,î and compares this method to the standard (frequen-tist) scan statistic approach on the task of prospective disease surveillance. -
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 -
Anomaly Pattern Detection for Biosurveillance
Content Type: Abstract
We propose a new method for detecting patterns of disease cases that correspond to emerging outbreaks. Our Anomaly Pattern Detector (APD) first uses a "local anomaly detector" to identify individually anomalous records and then searches over subsets… read more -
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. -
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 -
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