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Displaying results 1 - 8 of 9
  • A Temporal Method for Outbreak Detection Using a Bayesian Network

    Content Type: Abstract

    Non-temporal Bayesian network outbreak detection methods only look at data from the most recent day. For example, PANDA-CDCA (PC) only looks at data from the last 24 hours to determine how likely an outbreak is occurring. PC… read more
    … data from the most recent day. For example, PANDA-CDCA (PC) only looks at data from the last 24 hours to determine how likely an outbreak is occurring. PC is a Bayesian network disease outbreak detection system … al. obtained such results when evaluating the ability of PC to detect a laboratory validated outbreak of influenza. …
  • Modeling Clinician Detection Time of a Disease Outbreak Due to Inhalational Anthrax

    Content Type: Abstract

    We developed a probabilistic model of how clinicians are expected to detect a disease outbreak due to an outdoor release of anthrax spores, when the clinicians only have access to traditional clinical information (e.g., no computer-based alerts). We… read more
    … how clinicians are expected to detect a disease outbreak due to an outdoor release of anthrax spores, when the … Modeling_Clinician_Detection_Time_Of_A_Disease_Outbreak_Due_To_Inhalational_Anthrax.pdf 2 views Submitted by elamb … Modeling Clinician Detection Time of a Disease Outbreak Due to Inhalational Anthrax Christina Adamou, Gregory F. …
  • A Multivariate Bayesian Scan Statistic

    Content Type: Abstract

    This paper develops a new method for multivariate spatial cluster detection, the ìmultivariate Bayesian scan statisticî (MBSS). MBSS combines information from multiple data streams in a Bayesian framework, enabling faster and more accurate… read more
    … a wa- ter-borne illness can be predicted based on water dis- tribution information. To compute the data likeli- hood … will allow us to discriminate between clusters that are due to outbreaks and those due to other irrelevant causes. … Communications in Sta- tistics: Theory and Methods, 1997, 26(6): 1481-1496. [3] Neill DB, Detection of spatial and …
  • 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.
    … computable as a function of the aggregate count (i.e. number of disease cases) and aggregate baseline (i.e. … points are mapped to a uni- form grid, and searching over all rectangular regions on the grid) for the Bayesian … Communications in Sta- tistics: Theory and Methods, 1997, 26(6): 1481-1496. [2] Neill DB, Moore AW, Rapid detection of …
  • An Outbreak Detection Algorithm that Efficiently Performs Complete Bayesian Model Averaging Over All Possible Spatial Distributions of Disease

    Content Type: Abstract

    Many disease-outbreak detection algorithms, such as control chart methods, use frequentist statistical techniques. We describe a Bayesian algorithm that uses data D consisting of current day counts of some event (e.g., emergency department (ED)… read more
    … performs complete Bayesian Model Averaging (BMA) over all possible spatial distributions of disease, yet runs in … fficiently_Performs_Complete_Bayesian_Model_Averaging_Over_All_Possible_Spatial_Distributions_Of_Disease.pdf 5 views … Performs Complete Bayesian Model Averaging Over All Possible Spatial Distributions of Disease Yanna Shen1,2, …
  • A Generalization of the AMOC Curve

    Content Type: Abstract

    The Activity Monitoring Operating Characteristic (AMOC) curve is a useful and popular method for assessing the performance of algorithms that detect outbreaks of disease [1]. As it is typically applied in biosurveillance, the AMOC curve plots the… read more
    … type of outbreak of some disease D (e.g., an outbreak due to outdoor, airborne release of anthrax spores). The … provide a promising new approach for analyzing outbreak de- tection algorithms. REFERENCES [1] Fawcett T, Provost F. … Proceed- ings of International Conference on Knowledge Dis- covery and Data Mining (1999) 53-62. [2] Cooper GF, …
  • A Bayesian Algorithm for Detecting CDC Category A Outbreak Diseases from Emergency Department Chief Complaints

    Content Type: Abstract

    This paper describes a Bayesian algorithm for diagnosing the CDC Category A diseases, namely, anthrax, smallpox, tularemia, botulism and hemorrhagic fever, using emergency department chief complaints. The algorithm was evaluated on real data and on… read more
    … Bayesian algo- rithms, has been reported to diagnose all of the CDC Category A diseases [2]. This paper describes … for the detection of anthrax in a simu- lated outbreak due to a windborne-release of anthrax spores. The false … In future work, we plan to evaluate its ability to de- tect the other CDC Category A diseases. We also plan …
  • Chief Complaint Preprocessing Evaluated on Statistical and Non-Statistical Classifiers

    Content Type: Abstract

    To determine whether preprocessing chief complaints before automatically classifying them into syndromic categories improves classification performance.
    … (85%) in preprocessing chief complaints. However, CCP did not exhibit an overall improvement in clas- sification …