Covasim: An agent-based model of COVID-19 dynamics and interventions

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. Cov...

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Veröffentlicht in:PLoS computational biology 2021-07, Vol.17 (7), p.e1009149
Hauptverfasser: 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
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container_issue 7
container_start_page e1009149
container_title PLoS computational biology
container_volume 17
creator 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
description 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.
doi_str_mv 10.1371/journal.pcbi.1009149
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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. 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Brittany</au><au>George, Lauren</au><au>Jastrzebski, Michal</au><au>Izzo, Amanda S</au><au>Fowler, Greer</au><au>Palmer, Anna</au><au>Delport, Dominic</au><au>Scott, Nick</au><au>Kelly, Sherrie L</au><au>Bennette, Caroline S</au><au>Wagner, Bradley G</au><au>Chang, Stewart T</au><au>Oron, Assaf P</au><au>Wenger, Edward A</au><au>Panovska-Griffiths, Jasmina</au><au>Famulare, Michael</au><au>Klein, Daniel J</au><au>Marz, Manja</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Covasim: An agent-based model of COVID-19 dynamics and interventions</atitle><jtitle>PLoS computational biology</jtitle><date>2021-07-26</date><risdate>2021</risdate><volume>17</volume><issue>7</issue><spage>e1009149</spage><pages>e1009149-</pages><issn>1553-7358</issn><issn>1553-734X</issn><eissn>1553-7358</eissn><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.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>34310589</pmid><doi>10.1371/journal.pcbi.1009149</doi><orcidid>https://orcid.org/0000-0001-8902-9990</orcidid><orcidid>https://orcid.org/0000-0002-0496-4689</orcidid><orcidid>https://orcid.org/0000-0001-8729-9268</orcidid><orcidid>https://orcid.org/0000-0002-1598-1388</orcidid><orcidid>https://orcid.org/0000-0001-5482-9584</orcidid><orcidid>https://orcid.org/0000-0001-7565-2300</orcidid><orcidid>https://orcid.org/0000-0002-1896-6079</orcidid><orcidid>https://orcid.org/0000-0002-7418-9446</orcidid><orcidid>https://orcid.org/0000-0003-0437-7916</orcidid><orcidid>https://orcid.org/0000-0003-2517-2354</orcidid><orcidid>https://orcid.org/0000-0002-7720-1121</orcidid><orcidid>https://orcid.org/0000-0002-6232-5586</orcidid><orcidid>https://orcid.org/0000-0002-7604-9797</orcidid><orcidid>https://orcid.org/0000-0002-9618-6457</orcidid><orcidid>https://orcid.org/0000-0001-9275-7454</orcidid><orcidid>https://orcid.org/0000-0002-8479-1860</orcidid><orcidid>https://orcid.org/0000-0002-1489-7168</orcidid><orcidid>https://orcid.org/0000-0002-9213-0798</orcidid><orcidid>https://orcid.org/0000-0003-0576-7430</orcidid><oa>free_for_read</oa></addata></record>
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issn 1553-7358
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1553-7358
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source DOAJ Directory of Open Access Journals; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central
subjects Age
Age composition
Agent based models
Analysis
Australia
Biology and Life Sciences
Computer and Information Sciences
Contact tracing
Control
Coronaviruses
COVID-19
COVID-19 vaccines
Decision making
Denmark
Disease transmission
Epidemics
Households
Immunization
Intervention
Long-term care
Medical research
Medicine and Health Sciences
Pandemics
Pharmaceuticals
Physical Sciences
Population
Population number
Protective equipment
Quarantine
Research and Analysis Methods
Schools
Social distancing
Social organization
Social Sciences
United Kingdom
Vaccination
Viral diseases
Workplaces
title Covasim: An agent-based model of COVID-19 dynamics and interventions
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