doi:10.1016/j.heliyon.2023.e13545engLafuente, M.López, F. J.Mateo, P. M.Cebrián, A. C.Asín, J.Moler, J. A.Borque-Fernando, Á.Esteban, L. M.Pérez-Palomares, A.Sanz, G.A multistate model and its standalone tool to predict hospital and ICU occupancy by patients with COVID-19ART-2023-133073Objective: 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-19.2023http://zaguan.unizar.es/record/12536010.1016/j.heliyon.2023.e13545http://zaguan.unizar.es/record/125360oai:zaguan.unizar.es:125360info:eu-repo/grantAgreement/ES/DGA/E46-20Rinfo:eu-repo/grantAgreement/ES/MICINN/PID2020-116873GB-I00Heliyon 9, 2 (2023), e13545 [18 pp.]by-nc-ndhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccess