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Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions

The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to...

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Autores principales: Perkins, T. Alex, España, Guido
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473596/
https://www.ncbi.nlm.nih.gov/pubmed/32888118
http://dx.doi.org/10.1007/s11538-020-00795-y
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author Perkins, T. Alex
España, Guido
author_facet Perkins, T. Alex
España, Guido
author_sort Perkins, T. Alex
collection PubMed
description The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control.
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spelling pubmed-74735962020-09-08 Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions Perkins, T. Alex España, Guido Bull Math Biol Original Article The COVID-19 pandemic has forced societies across the world to resort to social distancing to slow the spread of the SARS-CoV-2 virus. Due to the economic impacts of social distancing, there is growing desire to relax these measures. To characterize a range of possible strategies for control and to understand their consequences, we performed an optimal control analysis of a mathematical model of SARS-CoV-2 transmission. Given that the pandemic is already underway and controls have already been initiated, we calibrated our model to data from the USA and focused our analysis on optimal controls from May 2020 through December 2021. We found that a major factor that differentiates strategies that prioritize lives saved versus reduced time under control is how quickly control is relaxed once social distancing restrictions expire in May 2020. Strategies that maintain control at a high level until at least summer 2020 allow for tapering of control thereafter and minimal deaths, whereas strategies that relax control in the short term lead to fewer options for control later and a higher likelihood of exceeding hospital capacity. Our results also highlight that the potential scope for controlling COVID-19 until a vaccine is available depends on epidemiological parameters about which there is still considerable uncertainty, including the basic reproduction number and the effectiveness of social distancing. In light of those uncertainties, our results do not constitute a quantitative forecast and instead provide a qualitative portrayal of possible outcomes from alternative approaches to control. Springer US 2020-10-07 2020 /pmc/articles/PMC7473596/ /pubmed/32888118 http://dx.doi.org/10.1007/s11538-020-00795-y Text en © Society for Mathematical Biology 2020 This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Original Article
Perkins, T. Alex
España, Guido
Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions
title Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions
title_full Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions
title_fullStr Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions
title_full_unstemmed Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions
title_short Optimal Control of the COVID-19 Pandemic with Non-pharmaceutical Interventions
title_sort optimal control of the covid-19 pandemic with non-pharmaceutical interventions
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7473596/
https://www.ncbi.nlm.nih.gov/pubmed/32888118
http://dx.doi.org/10.1007/s11538-020-00795-y
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