125360 20241125101148.0 doi 10.1016/j.heliyon.2023.e13545 sideral 133073 ART-2023-133073 eng Lafuente, M. Universidad de Zaragoza (orcid)0000-0001-8471-3224 A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19 2023 Access copy available to the general public Unrestricted Objective: This study aims to build a multistate model and describe a predictive tool for estimating the daily number of intensive care unit (ICU) and hospital beds occupied by patients with coronavirus 2019 disease (COVID-19). Material and methods: The estimation is based on the simulation of patient trajectories using a multistate model where the transition probabilities between states are estimated via competing risks and cure models. The input to the tool includes the dates of COVID-19 diagnosis, admission to hospital, admission to ICU, discharge from ICU and discharge from hospital or death of positive cases from a selected initial date to the current moment. Our tool is validated using 98,496 cases positive for severe acute respiratory coronavirus 2 extracted from the Aragón Healthcare Records Database from July 1, 2020 to February 28, 2021. Results: The tool demonstrates good performance for the 7- and 14-days forecasts using the actual positive cases, and shows good accuracy among three scenarios corresponding to different stages of the pandemic: 1) up-scenario, 2) peak-scenario and 3) down-scenario. Long term predictions (two months) also show good accuracy, while those using Holt-Winters positive case estimates revealed acceptable accuracy to day 14 onwards, with relative errors of 8.8%. Discussion: In the era of the COVID-19 pandemic, hospitals must evolve in a dynamic way. Our prediction tool is designed to predict hospital occupancy to improve healthcare resource management without information about clinical history of patients. Conclusions: Our easy-to-use and freely accessible tool (https://github.com/peterman65) shows good performance and accuracy for forecasting the daily number of hospital and ICU beds required for patients with COVID-19. info:eu-repo/grantAgreement/ES/DGA/E46-20R info:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00 info:eu-repo/semantics/openAccess by-nc-nd http://creativecommons.org/licenses/by-nc-nd/3.0/es/ 3.4 2023 MULTIDISCIPLINARY SCIENCES 28 / 134 = 0.209 2023 Q1 T1 0.617 2023 Multidisciplinary 2023 Q1 4.5 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion López, F. J. Universidad de Zaragoza (orcid)0000-0002-7615-2559 Mateo, P. M. Universidad de Zaragoza (orcid)0000-0003-2988-7728 Cebrián, A. C. Universidad de Zaragoza (orcid)0000-0002-9052-9674 Asín, J. Universidad de Zaragoza (orcid)0000-0002-0174-789X Moler, J. A. Borque-Fernando, Á. Universidad de Zaragoza (orcid)0000-0003-0178-4567 Esteban, L. M. (orcid)0000-0002-3007-302X Pérez-Palomares, A. Universidad de Zaragoza (orcid)0000-0003-0953-6277 Sanz, G. Universidad de Zaragoza (orcid)0000-0002-6474-2252 2007 265 Universidad de Zaragoza Dpto. Métodos Estadísticos Área Estadís. Investig. Opera. 1013 817 Universidad de Zaragoza Dpto. Cirugía Área Urología 9, 2 (2023), e13545 [18 pp.] Heliyon Heliyon 2405-8440 4943488 http://zaguan.unizar.es/record/125360/files/texto_completo.pdf Versión publicada 1814812 http://zaguan.unizar.es/record/125360/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125360 articulos driver 2024-11-22-12:05:23 ARTICLE