Computational Simulation Is a Vital Resource for Navigating the COVID-19 Pandemic
Autor: | Eric W Weisel, Wesley J. Wildman, George Hodulik, Neha Gondal, David Voas, Saikou Y. Diallo, Andrew Page |
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Rok vydání: | 2021 |
Předmět: |
Epidemiology
Distancing Computer science decision support tools Psychological intervention Medicine (miscellaneous) Education Compliance (psychology) Economic or Health Policy Articles Resource (project management) Pandemic Humans Computer Simulation Pandemics system dynamics models Computational model Social network business.industry SARS-CoV-2 dynamic simulation models COVID-19 health policy agent-based models Coronavirus Risk analysis (engineering) Modeling and Simulation Quarantine ComputingMethodologies_DOCUMENTANDTEXTPROCESSING business Contact tracing human factors |
Zdroj: | Simulation in Healthcare |
ISSN: | 1559-713X |
Popis: | Supplemental digital content is available in the text. Introduction COVID-19 has prompted the extensive use of computational models to understand the trajectory of the pandemic. This article surveys the kinds of dynamic simulation models that have been used as decision support tools and to forecast the potential impacts of nonpharmaceutical interventions (NPIs). We developed the Values in Viral Dispersion model, which emphasizes the role of human factors and social networks in viral spread and presents scenarios to guide policy responses. Methods An agent-based model of COVID-19 was developed with individual agents able to move between 3 states (susceptible, infectious, or recovered), with each agent placed in 1 of 7 social network types and assigned a propensity to comply with NPIs (quarantine, contact tracing, and physical distancing). A series of policy questions were tested to illustrate the impact of social networks and NPI compliance on viral spread among (1) populations, (2) specific at-risk subgroups, and (3) individual trajectories. Results Simulation outcomes showed large impacts of physical distancing policies on number of infections, with substantial modification by type of social network and level of compliance. In addition, outcomes on metrics that sought to maximize those never infected (or recovered) and minimize infections and deaths showed significantly different epidemic trajectories by social network type and among higher or lower at-risk age cohorts. Conclusions Although dynamic simulation models have important limitations, which are discussed, these decision support tools should be a key resource for navigating the ongoing impacts of the COVID-19 pandemic and can help local and national decision makers determine where, when, and how to invest resources. |
Databáze: | OpenAIRE |
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