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Covasim Resource1 #715327 COVID-19 Agent-based Simulator (Covasim): a model for exploring coronavirus dynamics and interventions. | - Covasim is a stochastic agent-based simulator for performing COVID-19 analyses. These include projections of indicators such as numbers of infections and peak hospital demand. Covasim can also be used to explore the potential impact of different interventions, including social distancing, school closures, testing, contact tracing, quarantine, and vaccination.
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+Verweise (2) - VerweiseHinzufĂŒgenList by: CiterankMapLink[2] Covasim: an agent-based model of COVID-19 dynamics and interventions
Zitieren: Cliff C. Kerr, Robyn M. Stuart, Dina Mistry, Romesh G. Abeysuriya, Katherine Rosenfeld, Gregory R. Hart, Rafael C. NĂșñez, Jamie A. Cohen, Prashanth Selvaraj, Brittany Hagedorn, Lauren George, MichaĆ JastrzÄbski, Amanda S. Izzo, Greer Fowler, Anna Palmer, Dominic Delport, Nick Scott, Sherrie L. Kelly, Caroline S. Bennette, Bradley G. Wagner, Stewart T. Chang, Assaf P. Oron, Edward A. Wenger, Jasmina Panovska-Griffiths, Michael Famulare, Daniel J. Klein Publication date: 26 July 2021 Publication info: PLOS Computational Biology 17 (7): e1009149. Zitiert von: David Price 6:51 PM 15 November 2023 GMT URL: DOI: https://doi.org/10.1371/journal.pcbi.1009149
| Auszug - [PLOS Computational Biology, 26 July 2021]
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. |
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