An statistical analysis of COVID-19 intensive care unit bed occupancy data

Fuente: arXiv
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Main Authors: Diz-Rosales, Naomi, Lombardía, María-José, Morales, Domingo
Format: Preprint
Published: 2024
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author Diz-Rosales, Naomi
Lombardía, María-José
Morales, Domingo
author_facet Diz-Rosales, Naomi
Lombardía, María-José
Morales, Domingo
contents The COVID-19 pandemic has had far-reaching consequences, highlighting the urgency for explanatory and predictive tools to track infection rates and burden of care over time and space. However, the scarcity and inhomogeneity of data is a challenge. In this research we develop a robust framework for estimating and predicting the occupied beds of Intensive Care Units by presenting an innovative Small Area Estimation methodology based on the definition of mixed models with random regression coefficients. We applied it to estimate and predict the daily occupancy of Intensive Care Unit beds by COVID-19 in health areas of Castilla y León, from November 2020 to March 2022.
format Preprint
id arxiv_https___arxiv_org_abs_2404_18493
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle An statistical analysis of COVID-19 intensive care unit bed occupancy data
Diz-Rosales, Naomi
Lombardía, María-José
Morales, Domingo
Applications
The COVID-19 pandemic has had far-reaching consequences, highlighting the urgency for explanatory and predictive tools to track infection rates and burden of care over time and space. However, the scarcity and inhomogeneity of data is a challenge. In this research we develop a robust framework for estimating and predicting the occupied beds of Intensive Care Units by presenting an innovative Small Area Estimation methodology based on the definition of mixed models with random regression coefficients. We applied it to estimate and predict the daily occupancy of Intensive Care Unit beds by COVID-19 in health areas of Castilla y León, from November 2020 to March 2022.
title An statistical analysis of COVID-19 intensive care unit bed occupancy data
topic Applications
url https://arxiv.org/abs/2404.18493