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

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Standard

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; Jastrzȩbski, Michał; 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.

I: PLOS Computational Biology, Bind 17, Nr. 7, e1009149, 2021.

Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt

Harvard

Kerr, CC, Stuart, RM, Mistry, D, Abeysuriya, RG, Rosenfeld, K, Hart, GR, Núñez, RC, Cohen, JA, Selvaraj, P, Hagedorn, B, George, L, Jastrzȩbski, M, Izzo, AS, Fowler, G, Palmer, A, Delport, D, Scott, N, Kelly, SL, Bennette, CS, Wagner, BG, Chang, ST, Oron, AP, Wenger, EA, Panovska-Griffiths, J, Famulare, M & Klein, DJ 2021, 'Covasim: An agent-based model of COVID-19 dynamics and interventions', PLOS Computational Biology, bind 17, nr. 7, e1009149. https://doi.org/10.1371/journal.pcbi.1009149

APA

Kerr, C. C., Stuart, R. M., Mistry, D., Abeysuriya, R. G., Rosenfeld, K., Hart, G. R., Núñez, R. C., Cohen, J. A., Selvaraj, P., Hagedorn, B., George, L., Jastrzȩbski, M., Izzo, A. S., Fowler, G., Palmer, A., Delport, D., Scott, N., Kelly, S. L., Bennette, C. S., ... Klein, D. J. (2021). Covasim: An agent-based model of COVID-19 dynamics and interventions. PLOS Computational Biology, 17(7), [e1009149]. https://doi.org/10.1371/journal.pcbi.1009149

Vancouver

Kerr CC, Stuart RM, Mistry D, Abeysuriya RG, Rosenfeld K, Hart GR o.a. Covasim: An agent-based model of COVID-19 dynamics and interventions. PLOS Computational Biology. 2021;17(7). e1009149. https://doi.org/10.1371/journal.pcbi.1009149

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 ; Jastrzȩbski, Michał ; 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. / Covasim : An agent-based model of COVID-19 dynamics and interventions. I: PLOS Computational Biology. 2021 ; Bind 17, Nr. 7.

Bibtex

@article{3ff9cd276c234d0994c44e3220c0d7db,
title = "Covasim: An agent-based model of COVID-19 dynamics and interventions",
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-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. ",
author = "Kerr, {Cliff C.} and Stuart, {Robyn M.} and Dina Mistry and Abeysuriya, {Romesh G.} and Katherine Rosenfeld and Hart, {Gregory R.} and N{\'u}{\~n}ez, {Rafael C.} and Cohen, {Jamie A.} and Prashanth Selvaraj and Brittany Hagedorn and Lauren George and Micha{\l} Jastrzȩbski and Izzo, {Amanda S.} and Greer Fowler and Anna Palmer and Dominic Delport and Nick Scott and Kelly, {Sherrie L.} and Bennette, {Caroline S.} and Wagner, {Bradley G.} and Chang, {Stewart T.} and Oron, {Assaf P.} and Wenger, {Edward A.} and Jasmina Panovska-Griffiths and Michael Famulare and Klein, {Daniel J.}",
note = "Publisher Copyright: {\textcopyright} 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.",
year = "2021",
doi = "10.1371/journal.pcbi.1009149",
language = "English",
volume = "17",
journal = "P L o S Computational Biology (Online)",
issn = "1553-734X",
publisher = "Public Library of Science",
number = "7",

}

RIS

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

Y1 - 2021

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 - Journal article

C2 - 34310589

AN - SCOPUS:85111759630

VL - 17

JO - P L o S Computational Biology (Online)

JF - P L o S Computational Biology (Online)

SN - 1553-734X

IS - 7

M1 - e1009149

ER -

ID: 276336775