Your browser doesn't support javascript.
Covasim: An agent-based model of COVID-19 dynamics and interventions.
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; Jastrzebski, Michal; 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.
  • Kerr CC; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Stuart RM; Department of Mathematical Sciences, University of Copenhagen, Copenhagen, Denmark.
  • Mistry D; Burnet Institute, Melbourne, Victoria, Australia.
  • Abeysuriya RG; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Rosenfeld K; Burnet Institute, Melbourne, Victoria, Australia.
  • Hart GR; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Núñez RC; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Cohen JA; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Selvaraj P; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Hagedorn B; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • George L; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Jastrzebski M; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Izzo AS; GitHub, Inc., San Francisco, California, United States of America.
  • Fowler G; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Palmer A; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Delport D; Burnet Institute, Melbourne, Victoria, Australia.
  • Scott N; Burnet Institute, Melbourne, Victoria, Australia.
  • Kelly SL; Burnet Institute, Melbourne, Victoria, Australia.
  • Bennette CS; Burnet Institute, Melbourne, Victoria, Australia.
  • Wagner BG; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Chang ST; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Oron AP; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Wenger EA; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Panovska-Griffiths J; Institute for Disease Modeling, Global Health Division, Bill & Melinda Gates Foundation, Seattle, Washington, United States of America.
  • Famulare M; Big Data Institute, University of Oxford, Oxford, United Kingdom.
  • Klein DJ; Wolfson Centre for Mathematical Biology and The Queen's College, University of Oxford, Oxford, United Kingdom.
PLoS Comput Biol ; 17(7): e1009149, 2021 07.
Article in English | MEDLINE | ID: covidwho-1325366
Preprint
This scientific journal article is probably based on a previously available preprint. It has been identified through a machine matching algorithm, human confirmation is still pending.
See preprint
ABSTRACT
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.
Subject(s)

Full text: Available Collection: International databases Database: MEDLINE Main subject: Systems Analysis / SARS-CoV-2 / COVID-19 / Models, Biological Type of study: Etiology study / Prognostic study Topics: Long Covid / Vaccines Limits: Humans Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Journal.pcbi.1009149

Similar

MEDLINE

...
LILACS

LIS


Full text: Available Collection: International databases Database: MEDLINE Main subject: Systems Analysis / SARS-CoV-2 / COVID-19 / Models, Biological Type of study: Etiology study / Prognostic study Topics: Long Covid / Vaccines Limits: Humans Language: English Journal: PLoS Comput Biol Journal subject: Biology / Medical Informatics Year: 2021 Document Type: Article Affiliation country: Journal.pcbi.1009149