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Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases

In this paper we present Gryphon, a hybrid agent-based stochastic modeling and simulation platform developed for characterizing the geographic spread of infectious diseases and the effects of interventions. We study both local and non-local transmission dynamics of stochastic simulations based on th...

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Detalles Bibliográficos
Autores principales: Yu, Bin, Wang, Jijun, McGowan, Michael, Vaidyanathan, Ganesh, Younger, Kristofer
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122863/
http://dx.doi.org/10.1007/978-3-642-12079-4_26
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author Yu, Bin
Wang, Jijun
McGowan, Michael
Vaidyanathan, Ganesh
Younger, Kristofer
author_facet Yu, Bin
Wang, Jijun
McGowan, Michael
Vaidyanathan, Ganesh
Younger, Kristofer
author_sort Yu, Bin
collection PubMed
description In this paper we present Gryphon, a hybrid agent-based stochastic modeling and simulation platform developed for characterizing the geographic spread of infectious diseases and the effects of interventions. We study both local and non-local transmission dynamics of stochastic simulations based on the published parameters and data for SARS. The results suggest that the expected numbers of infections and the timeline of control strategies predicted by our stochastic model are in reasonably good agreement with previous studies. These preliminary results indicate that Gryphon is able to characterize other future infectious diseases and identify endangered regions in advance.
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spelling pubmed-71228632020-04-06 Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases Yu, Bin Wang, Jijun McGowan, Michael Vaidyanathan, Ganesh Younger, Kristofer Advances in Social Computing Article In this paper we present Gryphon, a hybrid agent-based stochastic modeling and simulation platform developed for characterizing the geographic spread of infectious diseases and the effects of interventions. We study both local and non-local transmission dynamics of stochastic simulations based on the published parameters and data for SARS. The results suggest that the expected numbers of infections and the timeline of control strategies predicted by our stochastic model are in reasonably good agreement with previous studies. These preliminary results indicate that Gryphon is able to characterize other future infectious diseases and identify endangered regions in advance. 2010 /pmc/articles/PMC7122863/ http://dx.doi.org/10.1007/978-3-642-12079-4_26 Text en © Springer-Verlag Berlin Heidelberg 2010 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Yu, Bin
Wang, Jijun
McGowan, Michael
Vaidyanathan, Ganesh
Younger, Kristofer
Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases
title Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases
title_full Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases
title_fullStr Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases
title_full_unstemmed Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases
title_short Gryphon: A Hybrid Agent-Based Modeling and Simulation Platform for Infectious Diseases
title_sort gryphon: a hybrid agent-based modeling and simulation platform for infectious diseases
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7122863/
http://dx.doi.org/10.1007/978-3-642-12079-4_26
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