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Modeling COVID-19 for Lifting Non-Pharmaceutical Interventions

Matthew Koehler (), David M Slater (), Garry Jacyna () and James R Thompson ()

Journal of Artificial Societies and Social Simulation, 2021, vol. 24, issue 2, 9

Abstract: As a result of the COVID-19 worldwide pandemic, the United States instituted various non-pharmaceutical interventions (NPIs) in an effort to slow the spread of the disease. Although necessary for public safety, these NPIs can also have deleterious effects on the economy of a nation. State and federal leaders need tools that provide insight into which combination of NPIs will have the greatest impact on slowing the disease and at what point in time it is reasonably safe to start lifting these restrictions to everyday life. In the present work, we outline a modeling process that incorporates the parameters of the disease, the effects of NPIs, and the characteristics of individual communities to offer insight into when and to what degree certain NPIs should be instituted or lifted based on the progression of a given outbreak of COVID-19. We apply the model to the 24 county-equivalents of Maryland and illustrate that different NPI strategies can be employed in different parts of the state. Our objective is to outline a modeling process that combines the critical disease factors and factors relevant to decision-makers who must balance the health of the population with the health of the economy.

Keywords: Agent-Based Modeling; Covid-19; Contact Networks; Non-Pharmaceutical Interventions (search for similar items in EconPapers)
Date: 2021-03-31
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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2020-165-3

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