TY - JOUR
T1 - Covasim
T2 - An agent-based model of COVID-19 dynamics and interventions
AU - Kerr, Cliff C.
AU - Stuart, Robyn M.
AU - Mistry, Dina
AU - Abeysuriya, Romesh G.
AU - Rosenfeld, Katherine
AU - Hart, Gregory R.
AU - Núñez, Rafael C.
AU - Cohen, Jamie A.
AU - Selvaraj, Prashanth
AU - Hagedorn, Brittany
AU - George, Lauren
AU - Jastrzȩbski, Michał
AU - Izzo, Amanda S.
AU - Fowler, Greer
AU - Palmer, Anna
AU - Delport, Dominic
AU - Scott, Nick
AU - Kelly, Sherrie L.
AU - Bennette, Caroline S.
AU - Wagner, Bradley G.
AU - Chang, Stewart T.
AU - Oron, Assaf P.
AU - Wenger, Edward A.
AU - Panovska-Griffiths, Jasmina
AU - Famulare, Michael
AU - Klein, Daniel J.
N1 - Publisher Copyright:
© 2021 Kerr et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2021/7
Y1 - 2021/7
N2 - 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-loadbased 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.
AB - 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-loadbased 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.
UR - http://www.scopus.com/inward/record.url?scp=85111759630&partnerID=8YFLogxK
U2 - 10.1371/journal.pcbi.1009149
DO - 10.1371/journal.pcbi.1009149
M3 - Article
C2 - 34310589
AN - SCOPUS:85111759630
SN - 1553-734X
VL - 17
JO - PLoS Computational Biology
JF - PLoS Computational Biology
IS - 7
M1 - e1009149
ER -