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Covasim: An agent-based model of COVID-19 dynamics and interventions.
Kerr, Cliff C; Stuart, Robyn M; Mistry, Dina; Abeysuriya, Romesh G; Rosenfeld, Katherine; Hart, Gregory R; Núñez, Rafael C; Cohen, Jamie A; Selvaraj, Prashanth; Hagedorn, Brittany; George, Lauren; Jastrzebski, Michal; Izzo, Amanda S; Fowler, Greer; Palmer, Anna; Delport, Dominic; Scott, Nick; Kelly, Sherrie L; Bennette, Caroline S; Wagner, Bradley G; Chang, Stewart T; Oron, Assaf P; Wenger, Edward A; Panovska-Griffiths, Jasmina; Famulare, Michael; Klein, Daniel J.
Afiliação
  • Kerr CC; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Stuart RM; Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Mistry D; Burnet Institute, Melbourne, Victoria, Australia.
  • Abeysuriya RG; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Rosenfeld K; Burnet Institute, Melbourne, Victoria, Australia.
  • Hart GR; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Núñez RC; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Cohen JA; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Selvaraj P; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Hagedorn B; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • George L; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Jastrzebski M; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Izzo AS; GitHub, Inc., San Francisco, California, United States of America.
  • Fowler G; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Palmer A; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Delport D; Burnet Institute, Melbourne, Victoria, Australia.
  • Scott N; Burnet Institute, Melbourne, Victoria, Australia.
  • Kelly SL; Burnet Institute, Melbourne, Victoria, Australia.
  • Bennette CS; Burnet Institute, Melbourne, Victoria, Australia.
  • Wagner BG; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Chang ST; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Oron AP; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Wenger EA; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Panovska-Griffiths J; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Famulare M; Big Data Institute, University of Oxford, Oxford, United Kingdom.
  • Klein DJ; Wolfson Centre for Mathematical Biology and The Queen's College, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol ; 17(7): e1009149, 2021 07.
Article em En | MEDLINE | ID: mdl-34310589
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Análise de Sistemas / SARS-CoV-2 / COVID-19 / Modelos Biológicos Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article
Texto completo
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Análise de Sistemas / SARS-CoV-2 / COVID-19 / Modelos Biológicos Tipo de estudo: Etiology_studies / Prognostic_studies Limite: Humans Idioma: En Ano de publicação: 2021 Tipo de documento: Article