Displaying results 17 - 24 of 30
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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 -
Identifying High-Risk Areas for Dengue Infection Using Mobility Patterns on Twitter
Content Type: Abstract
Traditionally, surveillance systems for dengue and other infectious diseases locate each individual case by home address, aggregate these locations to small areas, and monitor the number of cases in each area over time. However, human mobility plays… read more -
Multidimensional Semantic Scan for Pre-Syndromic Disease Surveillance
Content Type: Abstract
An interdisciplinary team convened by ISDS to translate public health use-case needs into well-defined technical problems recently identified the need for new pre-syndromic surveillance methods that do not rely on existing syndromes or pre-defined… 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 -
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 -
Support Vector Subset Scan for Spatial Outbreak Detection
Content Type: Abstract
Neill’s fast subset scan2 detects significant spatial patterns of disease by efficiently maximizing a log-likelihood ratio statistic over subsets of locations, but may result in patterns that are not spatially compact. The penalized fast subset scan… read more