Open AccessCovasim: An agent-based model of COVID-19 dynamics and interventions
Author(s) -
Cliff C Kerr,
Robyn M. Stuart,
Dina Mistry,
Romesh Abeysuriya,
Katherine Rosenfeld,
Gregory R. Hart,
Rafael C. Núñez,
Jamie Cohen,
Prashanth Selvaraj,
Brittany Hagedorn,
Lauren George,
Michał Jastrzębski,
Amanda S Izzo,
Greer Fowler,
Anna Palmer,
Dominic Delport,
Nick Scott,
Sherrie L Kelly,
Caroline S. Bennette,
Bradley G. Wagner,
Stewart T. Chang,
Assaf P. Oron,
E. A. Wenger,
Jasmina PanovskaGriffiths,
Michael Famulare,
Daniel J. Klein
Publication year - 2021
Publication title -
plos computational biology/plos computational biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.628
H-Index - 182
eISSN - 1553-7358
pISSN - 1553-734X
DOI - 10.1371/journal.pcbi.1009149
Subject(s) - psychological intervention , contact tracing , pandemic , population , flexibility (engineering) , computer science , medicine , environmental health , covid-19 , infectious disease (medical specialty) , disease , nursing , economics , management , pathology
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.