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Displaying results 9 - 15 of 15
  • 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
    … Information: Daniel B. Neill, neill@cs.cmu.edu http://www.cs.cmu.edu/~neill Advances in Disease Surveillance 2008;5:45 mailto:neill@cs.cmu.edu http://www.cs.cmu.edu/~neill …
  • 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
    … Information: Daniel B. Neill, neill@cs.cmu.edu http://www.cs.cmu.edu/~neill Advances in Disease Surveillance 2008;5:48 mailto:neill@cs.cmu.edu http://www.cs.cmu.edu/~neill …
  • 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
    … Further Information: Daniel B. Neill, neill@cs.cmu.edu www.cs.cmu.edu/~neill Advances in Disease Surveillance 2007;4:107 mailto:neill@cs.cmu.edu http://www.cs.cmu.edu/~neill …
  • Generalized fast subset sums for Bayesian detection and visualization

    Content Type: Abstract

    The multivariate Bayesian scan statistic (MBSS) enables timely detection and characterization of emerging events by integrating multiple data streams. MBSS can model and differentiate between multiple event types: it uses Bayes’ Theorem to… read more
    … and Y Liu.; licensee Emerging Health Threats Journal. www.eht-journal.org 3939 into two streams of real-world … Health Threats Journal DB Neill and Y Liu. 2011, 4:s43 www.eht-journal.org page 2/2 4040 …
  • Fast subset scan for multivariate spatial biosurveillance

    Content Type: Abstract

    The spatial scan statistic detects significant spatial clusters of disease by maximizing a likelihood ratio statistic over a large set of spatial regions. Several recent approaches have extended spatial scan to multiple data streams. Burkom… read more
    … Neill et al.; licensee Emerging Health Threats Journal. www.eht-journal.org 3737 multiple data streams, as well as … Health Threats Journal DB Neill et al. 2011, 4:s42 www.eht-journal.org page 2/2 3838 …
  • A Nonparametric Scan Statistic for Multivariate Disease Surveillance

    Content Type: Abstract

    We present a new method for multivariate outbreak detection, the ìnonparametric scan statisticî (NPSS). NPSS enables fast and accurate detection of emerging space-time clusters using multiple disparate data streams, including nontraditional data… read more
    … Further Information: Daniel B. Neill, neill@cs.cmu.edu www.cs.cmu.edu/~neill Advances in Disease Surveillance …
  • 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
    … anomalous subsets detected with and without the graph con- straints. We consider a large set of potential graph …