COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease

Abstract One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensiv...

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Autores principales:	Matthias Ritter, Derek V. M. Ott, Friedemann Paul, John-Dylan Haynes, Kerstin Ritter
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/b4045b3cd80948ccb49ee74e3696e398
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spelling	oai:doaj.org-article:b4045b3cd80948ccb49ee74e3696e3982021-12-02T13:33:51ZCOVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease10.1038/s41598-021-83853-22045-2322https://doaj.org/article/b4045b3cd80948ccb49ee74e3696e3982021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83853-2https://doaj.org/toc/2045-2322Abstract One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0–15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease.Matthias RitterDerek V. M. OttFriedemann PaulJohn-Dylan HaynesKerstin RitterNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-12 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Matthias Ritter Derek V. M. Ott Friedemann Paul John-Dylan Haynes Kerstin Ritter COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
description	Abstract One major bottleneck in the ongoing COVID-19 pandemic is the limited number of critical care beds. Due to the dynamic development of infections and the time lag between when patients are infected and when a proportion of them enters an intensive care unit (ICU), the need for future intensive care can easily be underestimated. To infer future ICU load from reported infections, we suggest a simple statistical model that (1) accounts for time lags and (2) allows for making predictions depending on different future growth of infections. We have evaluated our model for three heavily affected regions in Europe, namely Berlin (Germany), Lombardy (Italy), and Madrid (Spain). Before extensive containment measures made an impact, we first estimate the region-specific model parameters, namely ICU rate, time lag between infection, and ICU admission as well as length of stay in ICU. Whereas for Berlin, an ICU rate of 6%, a time lag of 6 days, and a stay of 12 days in ICU provide the best fit of the data, for Lombardy and Madrid the ICU rate was higher (18% and 15%) and the time lag (0 and 3 days) and the stay in ICU (3 and 8 days) shorter. The region-specific models are then used to predict future ICU load assuming either a continued exponential phase with varying growth rates (0–15%) or linear growth. By keeping the growth rates flexible, this model allows for taking into account the potential effect of diverse containment measures. Thus, the model can help to predict a potential exceedance of ICU capacity depending on future growth. A sensitivity analysis for an extended time period shows that the proposed model is particularly useful for exponential phases of the disease.
format	article
author	Matthias Ritter Derek V. M. Ott Friedemann Paul John-Dylan Haynes Kerstin Ritter
author_facet	Matthias Ritter Derek V. M. Ott Friedemann Paul John-Dylan Haynes Kerstin Ritter
author_sort	Matthias Ritter
title	COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
title_short	COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
title_full	COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
title_fullStr	COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
title_full_unstemmed	COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
title_sort	covid-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/b4045b3cd80948ccb49ee74e3696e398
work_keys_str_mv	AT matthiasritter covid19asimplestatisticalmodelforpredictingintensivecareunitloadinexponentialphasesofthedisease AT derekvmott covid19asimplestatisticalmodelforpredictingintensivecareunitloadinexponentialphasesofthedisease AT friedemannpaul covid19asimplestatisticalmodelforpredictingintensivecareunitloadinexponentialphasesofthedisease AT johndylanhaynes covid19asimplestatisticalmodelforpredictingintensivecareunitloadinexponentialphasesofthedisease AT kerstinritter covid19asimplestatisticalmodelforpredictingintensivecareunitloadinexponentialphasesofthedisease
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COVID-19: a simple statistical model for predicting intensive care unit load in exponential phases of the disease

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