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

Authors :
Kerr CC
Stuart RM
Mistry D
Abeysuriya RG
Rosenfeld K
Hart GR
Núñez RC
Cohen JA
Selvaraj P
Hagedorn B
George L
Jastrzębski 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
Source :
PLoS computational biology [PLoS Comput Biol] 2021 Jul 26; Vol. 17 (7), pp. e1009149. Date of Electronic Publication: 2021 Jul 26 (Print Publication: 2021).
Publication Year :
2021

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.<br />Competing Interests: The authors have declared that no competing interests exist.

Details

Language :
English
ISSN :
1553-7358
Volume :
17
Issue :
7
Database :
MEDLINE
Journal :
PLoS computational biology
Publication Type :
Academic Journal
Accession number :
34310589
Full Text :
https://doi.org/10.1371/journal.pcbi.1009149