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Title: The Biosurveillance Analytics Resource Directory (BARD): Facilitating the use of epidemiological models for infectious disease surveillance

Journal Article · · PLoS ONE
 [1];  [1];  [1];  [2];  [3];  [1];  [1];  [1];  [1];  [4];  [5];  [6];  [7];  [8];  [8];  [9];  [10];  [1];  [11];  [1] more »;  [12] « less
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
  2. Santa Fe Inst. (SFI), Santa Fe, NM (United States)
  3. Johns Hopkins Univ., Laurel, MD (United States)
  4. Tulane Univ., New Orleans, LA (United States)
  5. National Aeronautics and Space Administration, Greenbelt, MD (United States)
  6. Univ. of Liverpool, Liverpool (United Kingdom); NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool (United Kingdom)
  7. Sandia National Lab. (SNL-CA), Livermore, CA (United States)
  8. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  9. Columbia Univ., New York, NY (United States)
  10. USDA APHIS Veterinary Services, Science, Technology, and Analysis Services, Fort Collins, CO (United States)
  11. Northeastern Univ., Boston, MA (United States)
  12. Univ. of Erlangen-Nuremberg (Germany)
+ Show Author Affiliations

Epidemiological modeling for infectious disease is important for disease management and its routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. A key need is a universal framework to facilitate model description and understanding of its features. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. The framework was developed through a consensus among a panel of subject matter experts. In this paper, we describe the framework, its application to model characterization, and the development of the Biosurveillance Analytics Resource Directory (BARD; http://brd.bsvgateway.org/brd/), to facilitate the rapid selection of operational models for specific infectious/communicable diseases. In conclusion, we offer this framework and associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States); Sandia National Lab. (SNL-CA), Livermore, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396; AC04-94AL85000; AC05-00OR22725
OSTI ID:
1246952
Alternate ID(s):
OSTI ID: 1266815; OSTI ID: 1267065
Report Number(s):
LA-UR-14-28011
Journal Information:
PLoS ONE, Vol. 11, Issue 1; ISSN 1932-6203
Publisher:
Public Library of ScienceCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 4 works
Citation information provided by
Web of Science

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Cited By (3)

Analytics for Investigation of Disease Outbreaks: Web-Based Analytics Facilitating Situational Awareness in Unfolding Disease Outbreaks
  • Velappan, Nileena; Daughton, Ashlynn Rae; Fairchild, Geoffrey
  • JMIR Public Health and Surveillance, Vol. 5, Issue 1 https://doi.org/10.2196/12032
journal January 2019
An approach to and web-based tool for infectious disease outbreak intervention analysis journal April 2017
An extensible framework and database of infectious disease for biosurveillance journal August 2017

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