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Road to recovery: Managing an epidemic()

Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic mo...

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Detalles Bibliográficos
Autores principales: Loertscher, Simon, Muir, Ellen V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052986/
https://www.ncbi.nlm.nih.gov/pubmed/33897087
http://dx.doi.org/10.1016/j.jmateco.2021.102482
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author Loertscher, Simon
Muir, Ellen V.
author_facet Loertscher, Simon
Muir, Ellen V.
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description Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic models, taking as given that the maximum capacity of the healthcare system imposes a constraint that must not be exceeded. Treating the transmission rate as a decreasing function of the severity of the lockdown, we first determine the minimal lockdown that satisfies this constraint using an epidemiology model with a homogeneous population to predict future demand for healthcare. Allowing for a heterogeneous population, we then derive the optimal lockdown policy under the assumption of homogeneous mixing and show that it is characterized by a bang–bang solution. Possibilities such as the capacity of the healthcare system increasing or a vaccine arriving at some point in the future do not substantively impact the dynamically optimal policy until such an event actually occurs.
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spelling pubmed-80529862021-04-19 Road to recovery: Managing an epidemic() Loertscher, Simon Muir, Ellen V. J Math Econ Article Without widespread immunization, the road to recovery from the current COVID-19 lockdowns will optimally follow a path that finds the difficult balance between the social and economic benefits of liberty and the toll from the disease. We provide an approach that combines epidemiology and economic models, taking as given that the maximum capacity of the healthcare system imposes a constraint that must not be exceeded. Treating the transmission rate as a decreasing function of the severity of the lockdown, we first determine the minimal lockdown that satisfies this constraint using an epidemiology model with a homogeneous population to predict future demand for healthcare. Allowing for a heterogeneous population, we then derive the optimal lockdown policy under the assumption of homogeneous mixing and show that it is characterized by a bang–bang solution. Possibilities such as the capacity of the healthcare system increasing or a vaccine arriving at some point in the future do not substantively impact the dynamically optimal policy until such an event actually occurs. Elsevier B.V. 2021-03 2021-02-05 /pmc/articles/PMC8052986/ /pubmed/33897087 http://dx.doi.org/10.1016/j.jmateco.2021.102482 Text en © 2021 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active.
spellingShingle Article
Loertscher, Simon
Muir, Ellen V.
Road to recovery: Managing an epidemic()
title Road to recovery: Managing an epidemic()
title_full Road to recovery: Managing an epidemic()
title_fullStr Road to recovery: Managing an epidemic()
title_full_unstemmed Road to recovery: Managing an epidemic()
title_short Road to recovery: Managing an epidemic()
title_sort road to recovery: managing an epidemic()
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8052986/
https://www.ncbi.nlm.nih.gov/pubmed/33897087
http://dx.doi.org/10.1016/j.jmateco.2021.102482
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