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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....

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Autores principales: Endres-Dighe, Stacy, Jones, Kasey, Hadley, Emily, Preiss, Alexander, Kery, Caroline, Stoner, Marie, Eversole, Susan, Rhea, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
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.
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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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