125313 20241125101153.0 doi 10.1186/s12874-023-01842-7 sideral 132908 ART-2023-132908 eng Aleta, Alberto Universidad de Zaragoza (orcid)0000-0002-1192-8707 Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference 2023 Access copy available to the general public Unrestricted Background: One of the main challenges of the COVID-19 pandemic is to make sense of available, but often heterogeneous and noisy data. This contribution presents a data-driven methodology that allows exploring the hospitalization dynamics of COVID-19, exemplified with a study of 17 autonomous regions in Spain from summer 2020 to summer 2021. Methods: We use data on new daily cases and hospitalizations reported by the Spanish Ministry of Health to implement a Bayesian inference method that allows making short-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country. Results: We show how to use the temporal series for the number of daily admissions and discharges from hospital to reproduce the hospitalization dynamics of COVID-19 patients. For the case-study of the region of Aragon, we estimate that the probability of being admitted to hospital care upon infection is 0.090 [0.086-0.094], (95% C.I.), with the distribution governing hospital admission yielding a median interval of 3.5 days and an IQR of 7 days. Likewise, the distribution on the length of stay produces estimates of 12 days for the median and 10 days for the IQR. A comparison between model parameters for the regions analyzed allows to detect differences and changes in policies of the health authorities. Conclusions: We observe important regional differences, signaling that to properly compare very different populations, it is paramount to acknowledge all the diversity in terms of culture, socio-economic status, and resource availability. To better understand the impact of this pandemic, much more data, disaggregated and properly annotated, should be made available. info:eu-repo/grantAgreement/ES/DGA/E36-20R info:eu-repo/grantAgreement/ES/MCIN-AEI-FEDER/PID2020-115800GB-I00 info:eu-repo/grantAgreement/ES/MICINN/RYC2021-033226-I info:eu-repo/grantAgreement/ES/UZ/UZ-SANTANDER/2020-0274 info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 3.9 2023 HEALTH CARE SCIENCES & SERVICES 24 / 174 = 0.138 2023 Q1 T1 1.632 2023 Health Informatics 2023 Q1 Epidemiology 2023 Q1 6.5 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Blas-Laína, Juan Luis Universidad de Zaragoza (orcid)0000-0001-7004-4664 Tirado Anglés, Gabriel Moreno, Yamir Universidad de Zaragoza (orcid)0000-0002-0895-1893 1013 090 Universidad de Zaragoza Dpto. Cirugía Área Cirugía 2004 405 Universidad de Zaragoza Dpto. Física Teórica Área Física Teórica 23 (2023), 24 [11 pp.] BMC Medical Research Methodology BMC Medical Research Methodology 1471-2288 1196773 http://zaguan.unizar.es/record/125313/files/texto_completo.pdf Versión publicada 2451801 http://zaguan.unizar.es/record/125313/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:125313 articulos driver 2024-11-22-12:07:56 ARTICLE