Pedrosa-Laza, MariaLópez-Cheda, AnaCao, Ricardo2023-12-262023-12-262022-01Pedrosa-Laza, M., López-Cheda, A. & Cao, R. Cure models to estimate time until hospitalization due to COVID-19. Appl Intell 52, 794–807 (2022). https://doi.org/10.1007/s10489-021-02311-81573-7497http://hdl.handle.net/2183/34641This version of the article has been accepted for publication, after peer review and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1007/s10489-021-02311-8[Abstract]: A short introduction to survival analysis and censored data is included in this paper. A thorough literature review in the field of cure models has been done. An overview on the most important and recent approaches on parametric, semiparametric and nonparametric mixture cure models is also included. The main nonparametric and semiparametric approaches were applied to a real time dataset of COVID-19 patients from the first weeks of the epidemic in Galicia (NW Spain). The aim is to model the elapsed time from diagnosis to hospital admission. The main conclusions, as well as the limitations of both the cure models and the dataset, are presented, illustrating the usefulness of cure models in this kind of studies, where the influence of age and sex on the time to hospital admission is shown.eng© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021Censored dataCOVID-19Hospital demandForecastingSurvival analysisjournal articleopen access