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:	Stacy Endres-Dighe, Kasey Jones, Emily Hadley, Alexander Preiss, Caroline Kery, Marie Stoner, Susan Eversole, Sarah Rhea
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/00380dbae74b41289d09483dfcd29cf8
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spelling	oai:doaj.org-article:00380dbae74b41289d09483dfcd29cf82021-12-02T20:19:09ZLessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity.1932-620310.1371/journal.pone.0260310https://doaj.org/article/00380dbae74b41289d09483dfcd29cf82021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0260310https://doaj.org/toc/1932-6203The 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.Stacy Endres-DigheKasey JonesEmily HadleyAlexander PreissCaroline KeryMarie StonerSusan EversoleSarah RheaPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11, p e0260310 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Stacy Endres-Dighe Kasey Jones Emily Hadley Alexander Preiss Caroline Kery Marie Stoner Susan Eversole Sarah Rhea Lessons learned from the rapid development of a statewide simulation model for predicting COVID-19's impact on healthcare resources and capacity.
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	article
author	Stacy Endres-Dighe Kasey Jones Emily Hadley Alexander Preiss Caroline Kery Marie Stoner Susan Eversole Sarah Rhea
author_facet	Stacy Endres-Dighe Kasey Jones Emily Hadley Alexander Preiss Caroline Kery Marie Stoner Susan Eversole Sarah Rhea
author_sort	Stacy Endres-Dighe
title	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_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_sort	lessons learned from the rapid development of a statewide simulation model for predicting covid-19's impact on healthcare resources and capacity.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/00380dbae74b41289d09483dfcd29cf8
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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.

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