Displaying results 25 - 30 of 30
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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 -
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 -
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 -
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 -
StarScan: A Novel Scan Statistic for Irregularly-Shaped Spatial Clusters
Content Type: Abstract
Kulldorff’s spatial scan statistic1 detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over circular spatial regions. The fast localized subset scan2 enables scalable detection of proximity-constrained subsets… read more -
Identifying Emerging Novel Outbreaks In Textual Emergency Department Data
Content Type: Abstract
Typical approaches to monitoring ED data classify cases into pre-defined syndromes and then monitor syndrome counts for anomalies. However, syndromes cannot be created to identify every possible cluster of cases of relevance to public health. To… read more