The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

نویسندگان

  • Kristen J Margevicius
  • Nicholas Generous
  • Esteban Abeyta
  • Ben Althouse
  • Howard Burkom
  • Lauren Castro
  • Ashlynn Daughton
  • Sara Y. Del Valle
  • Geoffrey Fairchild
  • James M. Hyman
  • Richard Kiang
  • Andrew P. Morse
  • Carmen M. Pancerella
  • Laura Pullum
  • Arvind Ramanathan
  • Jeffrey Schlegelmilch
  • Aaron Scott
  • Kirsten J Taylor-McCabe
  • Alessandro Vespignani
  • Alina Deshpande
  • Julio Vera
چکیده

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. 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.

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عنوان ژورنال:

دوره 11  شماره 

صفحات  -

تاریخ انتشار 2016