A scenario modeling pipeline for COVID-19 emergency planning

Abstract Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to fe...

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Autores principales:	Joseph C. Lemaitre, Kyra H. Grantz, Joshua Kaminsky, Hannah R. Meredith, Shaun A. Truelove, Stephen A. Lauer, Lindsay T. Keegan, Sam Shah, Josh Wills, Kathryn Kaminsky, Javier Perez-Saez, Justin Lessler, Elizabeth C. Lee
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/efe1ffd16d8b4332a6ef3fd6568f9711
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id	oai:doaj.org-article:efe1ffd16d8b4332a6ef3fd6568f9711
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spelling	oai:doaj.org-article:efe1ffd16d8b4332a6ef3fd6568f97112021-12-02T14:26:07ZA scenario modeling pipeline for COVID-19 emergency planning10.1038/s41598-021-86811-02045-2322https://doaj.org/article/efe1ffd16d8b4332a6ef3fd6568f97112021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-86811-0https://doaj.org/toc/2045-2322Abstract Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.Joseph C. LemaitreKyra H. GrantzJoshua KaminskyHannah R. MeredithShaun A. TrueloveStephen A. LauerLindsay T. KeeganSam ShahJosh WillsKathryn KaminskyJavier Perez-SaezJustin LesslerElizabeth C. LeeNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Joseph C. Lemaitre Kyra H. Grantz Joshua Kaminsky Hannah R. Meredith Shaun A. Truelove Stephen A. Lauer Lindsay T. Keegan Sam Shah Josh Wills Kathryn Kaminsky Javier Perez-Saez Justin Lessler Elizabeth C. Lee A scenario modeling pipeline for COVID-19 emergency planning
description	Abstract Coronavirus disease 2019 (COVID-19) has caused strain on health systems worldwide due to its high mortality rate and the large portion of cases requiring critical care and mechanical ventilation. During these uncertain times, public health decision makers, from city health departments to federal agencies, sought the use of epidemiological models for decision support in allocating resources, developing non-pharmaceutical interventions, and characterizing the dynamics of COVID-19 in their jurisdictions. In response, we developed a flexible scenario modeling pipeline that could quickly tailor models for decision makers seeking to compare projections of epidemic trajectories and healthcare impacts from multiple intervention scenarios in different locations. Here, we present the components and configurable features of the COVID Scenario Pipeline, with a vignette detailing its current use. We also present model limitations and active areas of development to meet ever-changing decision maker needs.
format	article
author	Joseph C. Lemaitre Kyra H. Grantz Joshua Kaminsky Hannah R. Meredith Shaun A. Truelove Stephen A. Lauer Lindsay T. Keegan Sam Shah Josh Wills Kathryn Kaminsky Javier Perez-Saez Justin Lessler Elizabeth C. Lee
author_facet	Joseph C. Lemaitre Kyra H. Grantz Joshua Kaminsky Hannah R. Meredith Shaun A. Truelove Stephen A. Lauer Lindsay T. Keegan Sam Shah Josh Wills Kathryn Kaminsky Javier Perez-Saez Justin Lessler Elizabeth C. Lee
author_sort	Joseph C. Lemaitre
title	A scenario modeling pipeline for COVID-19 emergency planning
title_short	A scenario modeling pipeline for COVID-19 emergency planning
title_full	A scenario modeling pipeline for COVID-19 emergency planning
title_fullStr	A scenario modeling pipeline for COVID-19 emergency planning
title_full_unstemmed	A scenario modeling pipeline for COVID-19 emergency planning
title_sort	scenario modeling pipeline for covid-19 emergency planning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/efe1ffd16d8b4332a6ef3fd6568f9711
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A scenario modeling pipeline for COVID-19 emergency planning

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