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The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance.
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.
Afiliação
  • Margevicius KJ; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Generous N; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Abeyta E; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Althouse B; Santa Fe Institute, Santa Fe, New Mexico, United States of America.
  • Burkom H; Johns Hopkins University-Applied Physics Laboratory, Laurel, Maryland, United States of America.
  • Castro L; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Daughton A; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Del Valle SY; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Fairchild G; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Hyman JM; Department of Mathematics, Tulane University, New Orleans, Louisiana, United States of America.
  • Kiang R; National Aeronautics and Space Administration, Greenbelt, Maryland, United States of America.
  • Morse AP; Department of Geography and Planning, School of Environmental Sciences, University of Liverpool, Liverpool, United Kingdom.
  • Pancerella CM; NIHR Health Protection Research Unit in Emerging and Zoonotic Infections, Liverpool, United Kingdom.
  • Pullum L; Distributed Systems Research, Sandia National Laboratories, Livermore, California, United States of America.
  • Ramanathan A; Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.
  • Schlegelmilch J; Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America.
  • Scott A; National Center for Disaster Preparedness, The Earth Institute-Columbia University, New York, New York, United States of America.
  • Taylor-McCabe KJ; USDA APHIS Veterinary Services, Science, Technology, and Analysis Services, Fort Collins, Colorado, United States of America.
  • Vespignani A; Analytics, Intelligence and Technology Division, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America.
  • Deshpande A; Laboratory for the Modeling of Biological and Socio-Technical Systems, Northeastern University, Boston, Massachusetts, United States of America.
PLoS One ; 11(1): e0146600, 2016.
Article em En | MEDLINE | ID: mdl-26820405
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. 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.
Assuntos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / Monitoramento Epidemiológico Tipo de estudo: Risk_factors_studies / Screening_studies Limite: Animals / Humans Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Doenças Transmissíveis / Monitoramento Epidemiológico Tipo de estudo: Risk_factors_studies / Screening_studies Limite: Animals / Humans Idioma: En Revista: PLoS One Assunto da revista: CIENCIA / MEDICINA Ano de publicação: 2016 Tipo de documento: Article País de afiliação: Estados Unidos