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Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity
The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601549/ https://www.ncbi.nlm.nih.gov/pubmed/34793573 http://dx.doi.org/10.1371/journal.pone.0260310 |
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author | Endres-Dighe, Stacy Jones, Kasey Hadley, Emily Preiss, Alexander Kery, Caroline Stoner, Marie Eversole, Susan Rhea, Sarah |
author_facet | Endres-Dighe, Stacy Jones, Kasey Hadley, Emily Preiss, Alexander Kery, Caroline Stoner, Marie Eversole, Susan Rhea, Sarah |
author_sort | Endres-Dighe, Stacy |
collection | PubMed |
description | The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19’s impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC. |
format | Online Article Text |
id | pubmed-8601549 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-86015492021-11-19 Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity Endres-Dighe, Stacy Jones, Kasey Hadley, Emily Preiss, Alexander Kery, Caroline Stoner, Marie Eversole, Susan Rhea, Sarah PLoS One Research Article The first case of COVID-19 was detected in North Carolina (NC) on March 3, 2020. By the end of April, the number of confirmed cases had soared to over 10,000. NC health systems faced intense strain to support surging intensive care unit admissions and avert hospital capacity and resource saturation. Forecasting techniques can be used to provide public health decision makers with reliable data needed to better prepare for and respond to public health crises. Hospitalization forecasts in particular play an important role in informing pandemic planning and resource allocation. These forecasts are only relevant, however, when they are accurate, made available quickly, and updated frequently. To support the pressing need for reliable COVID-19 data, RTI adapted a previously developed geospatially explicit healthcare facility network model to predict COVID-19’s impact on healthcare resources and capacity in NC. The model adaptation was an iterative process requiring constant evolution to meet stakeholder needs and inform epidemic progression in NC. Here we describe key steps taken, challenges faced, and lessons learned from adapting and implementing our COVID-19 model and coordinating with university, state, and federal partners to combat the COVID-19 epidemic in NC. Public Library of Science 2021-11-18 /pmc/articles/PMC8601549/ /pubmed/34793573 http://dx.doi.org/10.1371/journal.pone.0260310 Text en © 2021 Endres-Dighe et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Endres-Dighe, Stacy Jones, Kasey Hadley, Emily Preiss, Alexander Kery, Caroline Stoner, Marie Eversole, Susan Rhea, Sarah Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity |
title | Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity |
title_full | Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity |
title_fullStr | Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity |
title_full_unstemmed | Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity |
title_short | Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19’s impact on healthcare resources and capacity |
title_sort | lessons learned from the rapid development of a statewide simulation model for predicting covid-19’s impact on healthcare resources and capacity |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8601549/ https://www.ncbi.nlm.nih.gov/pubmed/34793573 http://dx.doi.org/10.1371/journal.pone.0260310 |
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