Contextualizing Data Streams for Infectious Disease Surveillance

Authors

  • Kirsten Taylor-McCabe Los Alamos National Laboratory, Los Alamos, NM, United States
  • Lauren Castro Los Alamos National Laboratory, Los Alamos, NM, United States
  • Nicholas Generous Los Alamos National Laboratory, Los Alamos, NM, United States
  • Kristen Margevicius Los Alamos National Laboratory, Los Alamos, NM, United States
  • Mac Brown 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.v6i1.5064

Abstract

To aid in developing a global biosurveillance program, it is critical to develop a framework to capture and understand the myriad of data streams and evaluate them in context of surveillance goals.  Toward this goal, Los Alamos National Laboratory has developed a new method of evaluating the effectiveness of data stream types through the use of a novel concept called the surveillance window, a technique that integrates operational systems analysis, surveillance system analysis and epidemiological analysis. This study provides a simple, yet elegant methodology for which to ground truth known and emerging data streams for utility in integrated biosurveillance efforts.

Author Biography

Kirsten Taylor-McCabe, Los Alamos National Laboratory, Los Alamos, NM, United States

Kirsten J Taylor-McCabe, Ph.D., is the Pathogen Science Team Leader in the Biosecurity and Public Health Group in Bioscience Division at Los Alamos National Laboratory.  Kirsten received a B.A in Biochemistry and Molecular, Cellular & Developmental Biology from the University of Colorado at Boulder. She holds a Masters in Biochemistry from Kent State University and Ph.D in Molecular Biochemistry from Loyola University Stritch School of Medicine.

Downloads

Published

2014-03-09

How to Cite

Taylor-McCabe, K., Castro, L., Generous, N., Margevicius, K., Brown, M., & Deshpande, A. (2014). Contextualizing Data Streams for Infectious Disease Surveillance. Online Journal of Public Health Informatics, 6(1). https://doi.org/10.5210/ojphi.v6i1.5064

Issue

Section

Oral Presentations