| Literature DB >> 34310589 |
Cliff C Kerr1, Robyn M Stuart2,3, Dina Mistry1, Romesh G Abeysuriya3, Katherine Rosenfeld1, Gregory R Hart1, Rafael C Núñez1, Jamie A Cohen1, Prashanth Selvaraj1, Brittany Hagedorn1, Lauren George1, Michał Jastrzębski4, Amanda S Izzo1, Greer Fowler1, Anna Palmer3, Dominic Delport3, Nick Scott3, Sherrie L Kelly3, Caroline S Bennette1, Bradley G Wagner1, Stewart T Chang1, Assaf P Oron1, Edward A Wenger1, Jasmina Panovska-Griffiths5,6, Michael Famulare1, Daniel J Klein1.
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.Entities:
Year: 2021 PMID: 34310589 DOI: 10.1371/journal.pcbi.1009149
Source DB: PubMed Journal: PLoS Comput Biol ISSN: 1553-734X Impact factor: 4.475