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

Abstract

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.

Authors:
 [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)
Publication Date:
Research Org.:
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 Org.:
USDOE
OSTI Identifier:
1246952
Alternate Identifier(s):
OSTI ID: 1266815; OSTI ID: 1267065
Report Number(s):
LA-UR-14-28011
Journal ID: ISSN 1932-6203
Grant/Contract Number:  
AC52-06NA25396; AC04-94AL85000; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
PLoS ONE
Additional Journal Information:
Journal Volume: 11; Journal Issue: 1; Journal ID: ISSN 1932-6203
Publisher:
Public Library of Science
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; disease surveillance; epidemiological models; infectious disease modeling; infectious disease epidemiology; infectious disease control; infectious disease surveillance; spatial epidemiology; public and occupational health; decision making; malaria; 59 BASIC BIOLOGICAL SCIENCES

Citation Formats

Margevicius, Kristen J., Generous, Nicholas, Abeyta, Esteban, Althouse, Ben, Burkom, Howard, Castro, Lauren, Daughton, Ashlynn, Del Valle, Sara Y., Fairchild, Geoffrey, Hyman, James M., Kiang, Richard, Morse, Andrew P., Pancerella, Carmen M., Pullum, Laura, Ramanathan, Arvind, Schlegelmilch, Jeffrey, Scott, Aaron, Taylor-McCabe, Kirsten J., Vespignani, Alessandro, Deshpande, Alina, and Vera, Julio. The Biosurveillance Analytics Resource Directory (BARD): Facilitating the use of epidemiological models for infectious disease surveillance. United States: N. p., 2016. Web. doi:10.1371/journal.pone.0146600.
Margevicius, Kristen J., Generous, Nicholas, Abeyta, Esteban, Althouse, Ben, Burkom, Howard, Castro, Lauren, Daughton, Ashlynn, Del Valle, Sara Y., Fairchild, Geoffrey, Hyman, James M., Kiang, Richard, Morse, Andrew P., Pancerella, Carmen M., Pullum, Laura, Ramanathan, Arvind, Schlegelmilch, Jeffrey, Scott, Aaron, Taylor-McCabe, Kirsten J., Vespignani, Alessandro, Deshpande, Alina, & Vera, Julio. The Biosurveillance Analytics Resource Directory (BARD): Facilitating the use of epidemiological models for infectious disease surveillance. United States. https://doi.org/10.1371/journal.pone.0146600
Margevicius, Kristen J., Generous, Nicholas, Abeyta, Esteban, Althouse, Ben, Burkom, Howard, Castro, Lauren, Daughton, Ashlynn, Del Valle, Sara Y., Fairchild, Geoffrey, Hyman, James M., Kiang, Richard, Morse, Andrew P., Pancerella, Carmen M., Pullum, Laura, Ramanathan, Arvind, Schlegelmilch, Jeffrey, Scott, Aaron, Taylor-McCabe, Kirsten J., Vespignani, Alessandro, Deshpande, Alina, and Vera, Julio. Thu . "The Biosurveillance Analytics Resource Directory (BARD): Facilitating the use of epidemiological models for infectious disease surveillance". United States. https://doi.org/10.1371/journal.pone.0146600. https://www.osti.gov/servlets/purl/1246952.
@article{osti_1246952,
title = {The Biosurveillance Analytics Resource Directory (BARD): Facilitating the use of epidemiological models for infectious disease surveillance},
author = {Margevicius, Kristen J. and Generous, Nicholas and Abeyta, Esteban and Althouse, Ben and Burkom, Howard and Castro, Lauren and Daughton, Ashlynn and Del Valle, Sara Y. and Fairchild, Geoffrey and Hyman, James M. and Kiang, Richard and Morse, Andrew P. and Pancerella, Carmen M. and Pullum, Laura and Ramanathan, Arvind and Schlegelmilch, Jeffrey and Scott, Aaron and Taylor-McCabe, Kirsten J. and Vespignani, Alessandro and Deshpande, Alina and Vera, Julio},
abstractNote = {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.},
doi = {10.1371/journal.pone.0146600},
journal = {PLoS ONE},
number = 1,
volume = 11,
place = {United States},
year = {Thu Jan 28 00:00:00 EST 2016},
month = {Thu Jan 28 00:00:00 EST 2016}
}

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