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Edwards Wendy

Description

Real-world public health data often provide numerous challenges. There may be a limited amount of background data, data dropouts, noise, and human error. The data from an emergency department (ED) in Urbana, IL includes a diagnosis field with multiple terms and notes separated by semicolons. There are over 7000 distinct terms, excluding the notes. Because it begins in April 2009, there is not yet adequate background data to use some of the regressionbased alerting algorithms. Values for some days are missing, so we also needed an algorithm that would tolerate data dropouts. 

INDICATOR is a workflow-based biosurveillance system developed at the National Center for Supercomputing Applications. One of the fundamental concepts of INDICATOR is that the burden of cleaning and processing incoming data should be on the software, rather than on the health care providers.

 

Objective

This paper compares different approaches with classification and anomaly detection of data from an ED.

Submitted by hparton on
Description

INDICATOR is a multi-stream open source platform for biosurveillance and outbreak detection, currently focused on Champaign County in Illinois. It has been in production since 2008 and is currently receiving data from emergency department, patient advisory nurse, outpatient convenient care clinic, school absenteeism, animal control, and weather sources. Historical data from some of these sources goes back to 2006.

 

Objective

To examine the correlation between different types of surveillance signals and climate information obtained from a well-defined geographic area.

Submitted by elamb on
Description

INDICATOR provides an open source platform for biosurveillance and outbreak detection. Data sources currently include emergency department, patient advisory nurse, outpatient clinic, and school absence activity We are currently working with the University of Illinois College of Veterinary Medicine and will include veterinary data so that animal and human health data can be analyzed together.

Objective

INDICATOR, an existing biosurveillance system, required an updated user interface to support more data sources and more robust reporting and data visualization.

Submitted by elamb on
Description

Infectious disease outbreaks require rapid access to information to support a coordinated response from healthcare providers and public health officials. They need to know the size, spread, and location of the outbreak, and they also need access to models that will help them to determine the best strategy to contain the outbreak. 

There are numerous software tools for outbreak detection, and there are also surveillance systems that depend on communication between health care professionals. Most of those systems use a single type of surveillance data (e.g., syndromic, mandatory reporting, or laboratory) and focus on human surveillance.

However, there are fewer options for planning responses to outbreaks. Modeling and simulation are complex and resource-intensive. For example, EpiSims and EpiCast, developed by the National Institute of Health Models of Infectious Disease Agent Study involve large, diverse datasets and require access to high-performance computing.

Cyberenvironments are an integrated set of tools and services tailored to a specific discipline that allows the community to leverage the national cyberinfrastructure in their research and teaching. They provide data stores, computational capabilities, analysis and visualization services, and interfaces to shared instruments and sensor networks.

The National Center for Supercomputing Applications is applying the concept of cyberenvironments to infectious disease surveillance to produce INDICATOR.

 

Objective

This paper describes INDICATOR, a biosurveillance cyberenvironment used to analyze hospital data and generate alerts for unusual values.

Submitted by elamb on