Displaying results 1 - 6 of 6
-
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. -
An Expectation-Based Scan Statistic for Detection of Space-Time Clusters
Content Type: Abstract
This paper describes a new class of space-time scan statistics designed for rapid detection of emerging disease clusters. We evaluate these methods on the task of prospective disease surveillance, and show that our methods consistently outperform… 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 -
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 -
An Empirical Comparison of Spatial Scan Statistics for Outbreak Detection
Content Type: Abstract
Expectation-based scan statistics extend the traditional spatial scan statistic approach by using historical data to infer the expected counts for each spatial location, then detecting regions with higher than expected counts. Here we consider five… read more -
An Expectation-Based Scan Statistic for Detection of Space-Time Clusters
Content Type: Abstract
The space-time scan statistic is a powerful statistical tool for prospective disease surveillance. It searches over a set of spatio-temporal regions (each representing some spatial area S for the last k days), finding the most… read more