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A scenario modeling pipeline for COVID-19 emergency planning
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 age...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024322/ https://www.ncbi.nlm.nih.gov/pubmed/33824358 http://dx.doi.org/10.1038/s41598-021-86811-0 |
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author | Lemaitre, Joseph C. Grantz, Kyra H. Kaminsky, Joshua Meredith, Hannah R. Truelove, Shaun A. Lauer, Stephen A. Keegan, Lindsay T. Shah, Sam Wills, Josh Kaminsky, Kathryn Perez-Saez, Javier Lessler, Justin Lee, Elizabeth C. |
author_facet | Lemaitre, Joseph C. Grantz, Kyra H. Kaminsky, Joshua Meredith, Hannah R. Truelove, Shaun A. Lauer, Stephen A. Keegan, Lindsay T. Shah, Sam Wills, Josh Kaminsky, Kathryn Perez-Saez, Javier Lessler, Justin Lee, Elizabeth C. |
author_sort | Lemaitre, Joseph C. |
collection | PubMed |
description | 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 | Online Article Text |
id | pubmed-8024322 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-80243222021-04-08 A scenario modeling pipeline for COVID-19 emergency planning Lemaitre, Joseph C. Grantz, Kyra H. Kaminsky, Joshua Meredith, Hannah R. Truelove, Shaun A. Lauer, Stephen A. Keegan, Lindsay T. Shah, Sam Wills, Josh Kaminsky, Kathryn Perez-Saez, Javier Lessler, Justin Lee, Elizabeth C. Sci Rep Article 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. Nature Publishing Group UK 2021-04-06 /pmc/articles/PMC8024322/ /pubmed/33824358 http://dx.doi.org/10.1038/s41598-021-86811-0 Text en © The Author(s) 2021 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Lemaitre, Joseph C. Grantz, Kyra H. Kaminsky, Joshua Meredith, Hannah R. Truelove, Shaun A. Lauer, Stephen A. Keegan, Lindsay T. Shah, Sam Wills, Josh Kaminsky, Kathryn Perez-Saez, Javier Lessler, Justin Lee, Elizabeth C. A scenario modeling pipeline for COVID-19 emergency planning |
title | 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_short | A scenario modeling pipeline for COVID-19 emergency planning |
title_sort | scenario modeling pipeline for covid-19 emergency planning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8024322/ https://www.ncbi.nlm.nih.gov/pubmed/33824358 http://dx.doi.org/10.1038/s41598-021-86811-0 |
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