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Displaying results 1 - 8 of 15
  • 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
    … (over-the-counter (OTC) sales, Emergency Department visits, etc.) to detect both natural disease outbreaks (e.g. … impressive results under simulated environments, but the com- plex behavior of real-world data and high costs asso- … bio-surveillance system. REFERENCES [1] Wagner MM, Tsui F-C, et al., A national retail data monitor for public …
  • Anomaly Pattern Detection for Biosurveillance

    Content Type: Abstract

    We propose a new method for detecting patterns of disease cases that correspond to emerging outbreaks. Our Anomaly Pattern Detector (APD) first uses a "local anomaly detector" to identify individually anomalous records and then searches over subsets… read more
    … visiting nearby hospitals with similar symp- toms), and may not be evident by examination of any single record … from Alleg- heny County, PA. Figure 1 plots the detection preci- sion, i.e. the proportion of detected anomalies that … The Bayesian aerosol release detector. Stat. Med., 2007, 26: 5225-5252. Further Information: Daniel B. Neill, …
  • A Robust Expectation-Based Spatial Scan Statistic

    Content Type: Abstract

    This paper describes a new expectation-based scan statistic that is robust to outliers (individual anomalies at the store level that are not indicative of outbreaks). We apply this method to prospective monitoring of over-the-counter (OTC) drug… read more
    … Communications in Sta- tistics: Theory and Methods, 1997, 26(6): 1481-1496. [4] Kulldorff M, Prospective time-periodic … Methods for Anomaly Detection, 2005. [6] Wagner MM, Tsui F-C, et al., A national retail data monitor for public …
  • 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
    … would not be detected by existing syndromes. Clusters may be based on symptoms, events, place names, arrival time, … The NC DPH dataset describes 198,511 de-identified ED visits over one year at 3 North Carolina hospitals. The data … The NC DPH dataset describes 198,511 de-identified ED visits over one year at 3 North Carolina hospitals. The data …
  • 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
    … the time series of observed counts (e.g. daily hospital visits for each zip code). Objective Our goal is to learn … the time series of observed counts (e.g., daily hospital visits for each zip code). Methods Our solution builds on … ehtj11115 ehtj11120 ehtj11024 ehtj11060 ehtj11110 26-50 ehtj11034 ehtj11198 ehtj11174 ehtj11048 ehtj11154 …
  • Detecting Previously Unseen Outbreaks with Novel Symptom Patterns

    Content Type: Abstract

    Commonly used syndromic surveillance methods based on the spatial scan statistic first classify disease cases into broad, pre-existing symptom categories ("prodromes") such as respiratory or fever, then detect spatial clusters where the recent… read more
    … prodrome is unexpectedly high. Novel emerging infections may have very specific and anomalous symptoms which should … M. A spatial scan statistic. Commun Stat Theor Meth. 1997;26:1481�96. 2. Blei D, Ng A, Jordan M. Latent Dirichlet … Abstracts Emerging Health Threats Journal 2011. # 2011 Y. Liu and D.B. Neill This is an Open Access article …
  • 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
    … a larger set of irregular regions, in which case they may not find the most relevant clusters. In either case, … spatial scans, with and without LTSS, on 281 days of ED visit data from 88 Allegheny County zip codes. Various scan … Communications in Statis- tics: Theory and Methods, 1997, 26(6): 1481-1496. [2] Neill DB, Detection of Spatial and …
  • 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
    … monitored data streams. However, some disease outbreaks may only affect a subpopulation of the monitored population … monitored data streams. However, some disease outbreaks may only affect a subpopulation of the monitored population …