Author: 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; Jastrzębski, Michał; 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.
Title: Covasim: An agent-based model of COVID-19 dynamics and interventions Cord-id: g3mrnp9d Document date: 2021_7_26
ID: g3mrnp9d
Snippet: 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
Document: 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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