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10.1371/journal.pcbi.1009149 http://scihub22266oqcxt.onion/10.1371/journal.pcbi.1009149 34310589!8341708!34310589     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       PLoS+Comput+Biol 2021 ; 17 (7): e1009149 Nephropedia Template TP {{tp|p=34310589|t=2021. Covasim: An agent-based model of COVID-19 dynamics and interventions |pdf=|usr=}}{{34310589}} gab.com Text Covasim: An agent-based model of COVID-19 dynamics and interventions JO: PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34310589 #FOAvip The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America. Twit Text FOAVip Covasim: An agent-based model of COVID-19 dynamics and interventions ????PLoS Comput Biol http://www.kidney.de/pget.php?&tw=1&pmid=34310589 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34310589 #FOAvip English Wikipedia <ref>{{Cite pmid|34310589}}</ref> Covasim: An agent-based model of COVID-19 dynamics and interventions #MMPMID34310589 Kerr CC; Stuart RM; Mistry D; Abeysuriya RG; Rosenfeld K; Hart GR; Nunez RC; Cohen JA; Selvaraj P; Hagedorn B; George L; Jastrzebski M; Izzo AS; Fowler G; Palmer A; Delport D; Scott N; Kelly SL; Bennette CS; Wagner BG; Chang ST; Oron AP; Wenger EA; Panovska-Griffiths J; Famulare M; Klein DJ PLoS Comput Biol 2021[Jul]; 17 (7): e1009149 PMID34310589show ga The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America. |*COVID-19/etiology/prevention & control/transmission[MESH] |*Models, Biological[MESH] |*SARS-CoV-2[MESH] |*Systems Analysis[MESH] |Basic Reproduction Number[MESH] |COVID-19 Testing[MESH] |COVID-19 Vaccines[MESH] |Computational Biology[MESH] |Computer Simulation[MESH] |Contact Tracing[MESH] |Disease Progression[MESH] |Hand Disinfection[MESH] |Host Microbial Interactions[MESH] |Humans[MESH] |Masks[MESH] |Mathematical Concepts[MESH] |Pandemics[MESH] |Physical Distancing[MESH] |Quarantine[MESH]Covasim: An agent-based model of COVID-19 dynamics and interventions (epmc).pdf DeepDyve Pubget Overpricing