COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications

Abstract The SARS-CoV-2 virus is responsible for the novel coronavirus disease 2019 (COVID-19), which has spread to populations throughout the continental United States. Most state and local governments have adopted some level of “social distancing” policy, but infections have continued to spread de...

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Autores principales:	Michael A. Rowland, Todd M. Swannack, Michael L. Mayo, Matthew Parno, Matthew Farthing, Ian Dettwiller, Glover George, William England, Molly Reif, Jeffrey Cegan, Benjamin Trump, Igor Linkov, Brandon Lafferty, Todd Bridges
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c160da7cd6fb4d2d82d293e5d95a52b8
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spelling	oai:doaj.org-article:c160da7cd6fb4d2d82d293e5d95a52b82021-12-02T14:47:38ZCOVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications10.1038/s41598-021-90227-12045-2322https://doaj.org/article/c160da7cd6fb4d2d82d293e5d95a52b82021-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-90227-1https://doaj.org/toc/2045-2322Abstract The SARS-CoV-2 virus is responsible for the novel coronavirus disease 2019 (COVID-19), which has spread to populations throughout the continental United States. Most state and local governments have adopted some level of “social distancing” policy, but infections have continued to spread despite these efforts. Absent a vaccine, authorities have few other tools by which to mitigate further spread of the virus. This begs the question of how effective social policy really is at reducing new infections that, left alone, could potentially overwhelm the existing hospitalization capacity of many states. We developed a mathematical model that captures correlations between some state-level “social distancing” policies and infection kinetics for all U.S. states, and use it to illustrate the link between social policy decisions, disease dynamics, and an effective reproduction number that changes over time, for case studies of Massachusetts, New Jersey, and Washington states. In general, our findings indicate that the potential for second waves of infection, which result after reopening states without an increase to immunity, can be mitigated by a return of social distancing policies as soon as possible after the waves are detected.Michael A. RowlandTodd M. SwannackMichael L. MayoMatthew ParnoMatthew FarthingIan DettwillerGlover GeorgeWilliam EnglandMolly ReifJeffrey CeganBenjamin TrumpIgor LinkovBrandon LaffertyTodd BridgesNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Michael A. Rowland Todd M. Swannack Michael L. Mayo Matthew Parno Matthew Farthing Ian Dettwiller Glover George William England Molly Reif Jeffrey Cegan Benjamin Trump Igor Linkov Brandon Lafferty Todd Bridges COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications
description	Abstract The SARS-CoV-2 virus is responsible for the novel coronavirus disease 2019 (COVID-19), which has spread to populations throughout the continental United States. Most state and local governments have adopted some level of “social distancing” policy, but infections have continued to spread despite these efforts. Absent a vaccine, authorities have few other tools by which to mitigate further spread of the virus. This begs the question of how effective social policy really is at reducing new infections that, left alone, could potentially overwhelm the existing hospitalization capacity of many states. We developed a mathematical model that captures correlations between some state-level “social distancing” policies and infection kinetics for all U.S. states, and use it to illustrate the link between social policy decisions, disease dynamics, and an effective reproduction number that changes over time, for case studies of Massachusetts, New Jersey, and Washington states. In general, our findings indicate that the potential for second waves of infection, which result after reopening states without an increase to immunity, can be mitigated by a return of social distancing policies as soon as possible after the waves are detected.
format	article
author	Michael A. Rowland Todd M. Swannack Michael L. Mayo Matthew Parno Matthew Farthing Ian Dettwiller Glover George William England Molly Reif Jeffrey Cegan Benjamin Trump Igor Linkov Brandon Lafferty Todd Bridges
author_facet	Michael A. Rowland Todd M. Swannack Michael L. Mayo Matthew Parno Matthew Farthing Ian Dettwiller Glover George William England Molly Reif Jeffrey Cegan Benjamin Trump Igor Linkov Brandon Lafferty Todd Bridges
author_sort	Michael A. Rowland
title	COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications
title_short	COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications
title_full	COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications
title_fullStr	COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications
title_full_unstemmed	COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications
title_sort	covid-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c160da7cd6fb4d2d82d293e5d95a52b8
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COVID-19 infection data encode a dynamic reproduction number in response to policy decisions with secondary wave implications

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