Displaying results 1 - 7 of 7
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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 … the time series of observed counts (e.g. daily hospital visits for each zip code). Objective Our goal is to learn … -
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. … multiple data streams (OTC sales, Emergency Department visits, etc.) to detect both natural disease outbreaks (e.g. … (over-the-counter (OTC) sales, Emergency Department visits, etc.) to detect both natural disease outbreaks (e.g. … -
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… 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 … -
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… outbreaks injected into real-world Emergency Department visit data from Allegheny County, PA. In the simulations … -
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… spatial scans, with and without LTSS, on 281 days of ED visit data from 88 Allegheny County zip codes. Various scan … -
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… into five Allegheny County datasets: respiratory ED visits, three streams of OTC data (cough/cold, antifever, … -
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… may include sources such as emergency department (ED) visits, with each stream representing a different chief …