Selecting Essential Information for Biosurveillance - A Multi-Criteria Decision Analysis

Authors

  • Nicholas Generous Los Alamos National Laboratory, Los Alamos, NM, United States
  • Kristen Margevicius Los Alamos National Laboratory, Los Alamos, NM, United States
  • Kirsten Taylor-McCabe Los Alamos National Laboratory, Los Alamos, NM, United States
  • Mac Brown Los Alamos National Laboratory, Los Alamos, NM, United States
  • W. Brent Daniel Los Alamos National Laboratory, Los Alamos, NM, United States
  • Lauren Castro Los Alamos National Laboratory, Los Alamos, NM, United States
  • Andrea Hengartner Los Alamos National Laboratory, Los Alamos, NM, United States
  • Alina Deshpande Los Alamos National Laboratory, Los Alamos, NM, United States

DOI:

https://doi.org/10.5210/ojphi.v6i1.5165

Abstract

This paper proposes the use of Multi-Attribute Utility Theory to address the issue of identifying and selecting essential information for inclusion into a biosurveillance system or process. We developed a decision support framework that can facilitate identifying data streams for use in biosurveillance systems or processes and demonstrated utility by applying the framework to the problem of evaluating data streams for use in an global infectious disease surveillance system.

Author Biography

Nicholas Generous, Los Alamos National Laboratory, Los Alamos, NM, United States

Nicholas Generous is a graduate research assistant at Los Alamos National Laboratories where he works on several biosurveillance related projects. He graduated from the Johns Hopkins University with both his bachelor's and master's in biology.

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Published

2014-03-09

How to Cite

Generous, N., Margevicius, K., Taylor-McCabe, K., Brown, M., Daniel, W. B., Castro, L., … Deshpande, A. (2014). Selecting Essential Information for Biosurveillance - A Multi-Criteria Decision Analysis. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5165

Issue

Section

Oral Presentations