The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance
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{"title"=>"The biosurveillance analytics resource directory (BARD): Facilitating the use of epidemiological models for infectious disease surveillance", "type"=>"journal", "authors"=>[{"first_name"=>"Kristen J.", "last_name"=>"Margevicius", "scopus_author_id"=>"6507557434"}, {"first_name"=>"Nicholas", "last_name"=>"Generous", "scopus_author_id"=>"56058499600"}, {"first_name"=>"Esteban", "last_name"=>"Abeyta", "scopus_author_id"=>"57189872860"}, {"first_name"=>"Ben", "last_name"=>"Althouse", "scopus_author_id"=>"54900566600"}, {"first_name"=>"Howard", "last_name"=>"Burkom", "scopus_author_id"=>"6602405992"}, {"first_name"=>"Lauren", "last_name"=>"Castro", "scopus_author_id"=>"7202228750"}, {"first_name"=>"Ashlynn", "last_name"=>"Daughton", "scopus_author_id"=>"55675490900"}, {"first_name"=>"Sara Y.", "last_name"=>"Del Valle", "scopus_author_id"=>"22333350900"}, {"first_name"=>"Geoffrey", "last_name"=>"Fairchild", "scopus_author_id"=>"55949067700"}, {"first_name"=>"James M.", "last_name"=>"Hyman", "scopus_author_id"=>"7005695085"}, {"first_name"=>"Richard", "last_name"=>"Kiang", "scopus_author_id"=>"57132733100"}, {"first_name"=>"Andrew P.", "last_name"=>"Morse", "scopus_author_id"=>"57193065464"}, {"first_name"=>"Carmen M.", "last_name"=>"Pancerella", "scopus_author_id"=>"6602511815"}, {"first_name"=>"Laura", "last_name"=>"Pullum", "scopus_author_id"=>"7004505147"}, {"first_name"=>"Arvind", "last_name"=>"Ramanathan", "scopus_author_id"=>"56644268800"}, {"first_name"=>"Jeffrey", "last_name"=>"Schlegelmilch", "scopus_author_id"=>"55332673900"}, {"first_name"=>"Aaron", "last_name"=>"Scott", "scopus_author_id"=>"14012898700"}, {"first_name"=>"Kirsten J.", "last_name"=>"Taylor-McCabe", "scopus_author_id"=>"6506092525"}, {"first_name"=>"Alessandro", "last_name"=>"Vespignani", "scopus_author_id"=>"7004546582"}, {"first_name"=>"Alina", "last_name"=>"Deshpande", "scopus_author_id"=>"7202167657"}], "year"=>2016, "source"=>"PLoS ONE", "identifiers"=>{"scopus"=>"2-s2.0-84959017932", "sgr"=>"84959017932", "issn"=>"19326203", "doi"=>"10.1371/journal.pone.0146600", "pmid"=>"26820405", "pui"=>"608273530"}, "id"=>"bda90181-4cd7-3869-9362-13542a883eff", "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.", "link"=>"http://www.mendeley.com/research/biosurveillance-analytics-resource-directory-bard-facilitating-epidemiological-models-infectious-dis", "reader_count"=>16, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Researcher"=>7, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Researcher"=>7, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Bachelor"=>1, "Lecturer"=>1, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Biochemistry, Genetics and Molecular Biology"=>1, "Medicine and Dentistry"=>1, "Agricultural and Biological Sciences"=>4, "Veterinary Science and Veterinary Medicine"=>2, "Psychology"=>1, "Social Sciences"=>1, "Computer Science"=>3, "Immunology and Microbiology"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Social Sciences"=>{"Social Sciences"=>1}, "Psychology"=>{"Psychology"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>4}, "Computer Science"=>{"Computer Science"=>3}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>1}, "Unspecified"=>{"Unspecified"=>2}, "Veterinary Science and Veterinary Medicine"=>{"Veterinary Science and Veterinary Medicine"=>2}}, "reader_count_by_country"=>{"United States"=>1, "Switzerland"=>1}, "group_count"=>1}

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/4279862"], "description"=>"<p>Framework Use Cases.</p>", "links"=>[], "tags"=>["BARD", "Biosurveillance Analytics Resource Directory", "LANL", "model characterization process", "Los Alamos National Laboratory", "subject matter experts", "disease modeling field", "framework", "model description", "Infectious Disease Surveillance"], "article_id"=>2628948, "categories"=>["Medicine", "Molecular Biology", "Ecology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Cancer", "Science Policy"], "users"=>["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"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0146600.t003", "stats"=>{"downloads"=>0, "page_views"=>2, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/Framework_Use_Cases_/2628948", "title"=>"Framework Use Cases.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2016-01-28 03:54:12"}
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  • {"files"=>["https://ndownloader.figshare.com/files/4279836", "https://ndownloader.figshare.com/files/4279838"], "description"=>"<div><p>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; <a href=\"http://brd.bsvgateway.org/brd/\" target=\"_blank\">http://brd.bsvgateway.org/brd/</a>), 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.</p></div>", "links"=>[], "tags"=>["BARD", "Biosurveillance Analytics Resource Directory", "LANL", "model characterization process", "Los Alamos National Laboratory", "subject matter experts", "disease modeling field", "framework", "model description", "Infectious Disease Surveillance"], "article_id"=>2628924, "categories"=>["Medicine", "Molecular Biology", "Ecology", "Biological Sciences not elsewhere classified", "Information Systems not elsewhere classified", "Cancer", "Science Policy"], "users"=>["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"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0146600.s001", "https://dx.doi.org/10.1371/journal.pone.0146600.s002"], "stats"=>{"downloads"=>0, "page_views"=>0, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/The_Biosurveillance_Analytics_Resource_Directory_BARD_Facilitating_the_Use_of_Epidemiological_Models_for_Infectious_Disease_Surveillance/2628924", "title"=>"The Biosurveillance Analytics Resource Directory (BARD): Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2016-01-28 03:54:12"}

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Relative Metric

{"start_date"=>"2016-01-01T00:00:00Z", "end_date"=>"2016-12-31T00:00:00Z", "subject_areas"=>[]}
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