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 |
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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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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.</description><identifier>ISSN: 1553-7358</identifier><identifier>ISSN: 1553-734X</identifier><identifier>EISSN: 1553-7358</identifier><identifier>DOI: 10.1371/journal.pcbi.1009149</identifier><identifier>PMID: 34310589</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>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</subject><ispartof>PLoS computational biology, 2021-07, Vol.17 (7), p.e1009149</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Kerr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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.</description><subject>Age</subject><subject>Age composition</subject><subject>Agent based models</subject><subject>Analysis</subject><subject>Australia</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>Contact tracing</subject><subject>Control</subject><subject>Coronaviruses</subject><subject>COVID-19</subject><subject>COVID-19 vaccines</subject><subject>Decision making</subject><subject>Denmark</subject><subject>Disease transmission</subject><subject>Epidemics</subject><subject>Households</subject><subject>Immunization</subject><subject>Intervention</subject><subject>Long-term care</subject><subject>Medical research</subject><subject>Medicine and Health 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An agent-based model of COVID-19 dynamics and interventions</title><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 ; 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</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c671t-87917d8c87c6b794f375ed9a0f6b47d78d8ab0b897405fa2faaad44ef20ed3bc3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Age</topic><topic>Age composition</topic><topic>Agent based models</topic><topic>Analysis</topic><topic>Australia</topic><topic>Biology and Life 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Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><collection>Genetics Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PLoS computational biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Kerr, Cliff C</au><au>Stuart, Robyn M</au><au>Mistry, Dina</au><au>Abeysuriya, Romesh G</au><au>Rosenfeld, Katherine</au><au>Hart, Gregory R</au><au>Núñez, Rafael C</au><au>Cohen, Jamie A</au><au>Selvaraj, Prashanth</au><au>Hagedorn, 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> |
fulltext | fulltext |
identifier | ISSN: 1553-7358 |
ispartof | PLoS computational biology, 2021-07, Vol.17 (7), p.e1009149 |
issn | 1553-7358 1553-734X 1553-7358 |
language | eng |
recordid | cdi_plos_journals_2561943650 |
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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