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Covasim: an agent-based model of COVID-19 dynamics and interventions
Cliff C. Kerr; Robyn M. Stuart; Dina Mistry; Romesh G. Abeysuriya; Katherine Rosenfeld; Gregory R. Hart; Rafael C. Nunez; Jamie A. Cohen; Prashanth Selvaraj; Brittany Hagedorn; Lauren George; Michal Jastrzebski; Amanda Izzo; Greer Fowler; Anna Palmer; Dominic Delport; Nick Scott; Sherrie Kelly; Caroline S Bennette; Bradley Wagner; Stewart Chang; Assaf P. Oron; Edward Wenger; Jasmina Panovska-Griffiths; Michael Famulare; Daniel J. Klein.
  • Cliff C. Kerr; Institute for Disease Modeling
  • Robyn M. Stuart; University of Copenhagen
  • Dina Mistry; Institute for Disease Modeling
  • Romesh G. Abeysuriya; Burnet Institute
  • Katherine Rosenfeld; Institute for Disease Modeling
  • Gregory R. Hart; Institute for Disease Modeling
  • Rafael C. Nunez; Institute for Disease Modeling
  • Jamie A. Cohen; Institute for Disease Modeling
  • Prashanth Selvaraj; Institute for Disease Modeling
  • Brittany Hagedorn; Institute for Disease Modeling
  • Lauren George; Institute for Disease Modeling
  • Michal Jastrzebski; GitHub, Inc.
  • Amanda Izzo; Institute for Disease Modeling
  • Greer Fowler; Institute for Disease Modeling
  • Anna Palmer; Burnet Institute
  • Dominic Delport; Burnet Institute
  • Nick Scott; Burnet Institute
  • Sherrie Kelly; Burnet Institute
  • Caroline S Bennette; Institute for Disease Modeling
  • Bradley Wagner; Institute for Disease Modeling
  • Stewart Chang; Institute for Disease Modeling
  • Assaf P. Oron; Institute for Disease Modeling
  • Edward Wenger; Institute for Disease Modeling
  • Jasmina Panovska-Griffiths; University College London
  • Michael Famulare; Institute for Disease Modeling
  • Daniel J. Klein; Institute for Disease Modeling
Preprint in English | medRxiv | ID: ppmedrxiv-20097469
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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.
Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Topics: Long Covid / Vaccines Language: English Year: 2020 Document Type: Preprint





Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study Topics: Long Covid / Vaccines Language: English Year: 2020 Document Type: Preprint