Generating synthetic syndromic-surveillance data for evaluating visual-analytics techniques

Ross Maciejewski; Ryan Hafen; Stephen Rudolph; George Tebbetts; William S. Cleveland; Shaun J. Grannis; David S. Ebert

doi:10.1109/MCG.2009.43

Generating synthetic syndromic-surveillance data for evaluating visual-analytics techniques

Ross Maciejewski, Ryan Hafen, Stephen Rudolph, George Tebbetts, William S. Cleveland, Shaun J. Grannis, David S. Ebert

Research output: Contribution to journal › Article › peer-review

17 Scopus citations

Abstract

This system generates synthetic syndromic-surveillance data for evaluating visualization and visual-analytics techniques. Modeling data from emergency room departments, the system generates two years of patient data, into which system users can inject spatiotemporal disease outbreak signals. The result is a data set with known seasonal trends and irregular outbreak patterns.

Original language	English (US)
Pages (from-to)	18-28
Number of pages	11
Journal	IEEE Computer Graphics and Applications
Volume	29
Issue number	3
DOIs	https://doi.org/10.1109/MCG.2009.43
State	Published - 2009
Externally published	Yes

Keywords

Computer graphics
Syndromic surveillance
Synthetic data
Visual analytics

ASJC Scopus subject areas

Software
Computer Graphics and Computer-Aided Design

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10.1109/MCG.2009.43

Cite this

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title = "Generating synthetic syndromic-surveillance data for evaluating visual-analytics techniques",

abstract = "This system generates synthetic syndromic-surveillance data for evaluating visualization and visual-analytics techniques. Modeling data from emergency room departments, the system generates two years of patient data, into which system users can inject spatiotemporal disease outbreak signals. The result is a data set with known seasonal trends and irregular outbreak patterns.",

keywords = "Computer graphics, Syndromic surveillance, Synthetic data, Visual analytics",

author = "Ross Maciejewski and Ryan Hafen and Stephen Rudolph and George Tebbetts and Cleveland, {William S.} and Grannis, {Shaun J.} and Ebert, {David S.}",

note = "Funding Information: We thank the Indiana State Department of Health for providing the data. The US Department of Homeland Security Regional Visualization and Analytics Center (RVAC) Center of Excellence and the US National Science Foundation (grants 0081581, 0121288, and 0328984) funded this research.",

year = "2009",

doi = "10.1109/MCG.2009.43",

language = "English (US)",

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AU - Hafen, Ryan

AU - Rudolph, Stephen

AU - Tebbetts, George

AU - Cleveland, William S.

AU - Grannis, Shaun J.

AU - Ebert, David S.

N1 - Funding Information: We thank the Indiana State Department of Health for providing the data. The US Department of Homeland Security Regional Visualization and Analytics Center (RVAC) Center of Excellence and the US National Science Foundation (grants 0081581, 0121288, and 0328984) funded this research.

PY - 2009

Y1 - 2009

N2 - This system generates synthetic syndromic-surveillance data for evaluating visualization and visual-analytics techniques. Modeling data from emergency room departments, the system generates two years of patient data, into which system users can inject spatiotemporal disease outbreak signals. The result is a data set with known seasonal trends and irregular outbreak patterns.

AB - This system generates synthetic syndromic-surveillance data for evaluating visualization and visual-analytics techniques. Modeling data from emergency room departments, the system generates two years of patient data, into which system users can inject spatiotemporal disease outbreak signals. The result is a data set with known seasonal trends and irregular outbreak patterns.

KW - Computer graphics

KW - Syndromic surveillance

KW - Synthetic data

KW - Visual analytics

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Generating synthetic syndromic-surveillance data for evaluating visual-analytics techniques

Abstract

Keywords

ASJC Scopus subject areas

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