000125313 001__ 125313
000125313 005__ 20241125101153.0
000125313 0247_ $$2doi$$a10.1186/s12874-023-01842-7
000125313 0248_ $$2sideral$$a132908
000125313 037__ $$aART-2023-132908
000125313 041__ $$aeng
000125313 100__ $$0(orcid)0000-0002-1192-8707$$aAleta, Alberto$$uUniversidad de Zaragoza
000125313 245__ $$aUnraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference
000125313 260__ $$c2023
000125313 5060_ $$aAccess copy available to the general public$$fUnrestricted
000125313 5203_ $$aBackground: 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.
000125313 536__ $$9info:eu-repo/grantAgreement/ES/UZ/UZ-SANTANDER/2020-0274$$9info:eu-repo/grantAgreement/ES/MICINN/RYC2021-033226-I$$9info:eu-repo/grantAgreement/ES/MCIN-AEI-FEDER/PID2020-115800GB-I00$$9info:eu-repo/grantAgreement/ES/DGA/E36-20R
000125313 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000125313 590__ $$a3.9$$b2023
000125313 592__ $$a1.632$$b2023
000125313 591__ $$aHEALTH CARE SCIENCES & SERVICES$$b24 / 174 = 0.138$$c2023$$dQ1$$eT1
000125313 593__ $$aHealth Informatics$$c2023$$dQ1
000125313 593__ $$aEpidemiology$$c2023$$dQ1
000125313 594__ $$a6.5$$b2023
000125313 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000125313 700__ $$0(orcid)0000-0001-7004-4664$$aBlas-Laína, Juan Luis$$uUniversidad de Zaragoza
000125313 700__ $$aTirado Anglés, Gabriel
000125313 700__ $$0(orcid)0000-0002-0895-1893$$aMoreno, Yamir$$uUniversidad de Zaragoza
000125313 7102_ $$11013$$2090$$aUniversidad de Zaragoza$$bDpto. Cirugía$$cÁrea Cirugía
000125313 7102_ $$12004$$2405$$aUniversidad de Zaragoza$$bDpto. Física Teórica$$cÁrea Física Teórica
000125313 773__ $$g23 (2023), 24 [11 pp.]$$pBMC Medical Research Methodology$$tBMC Medical Research Methodology$$x1471-2288
000125313 8564_ $$s1196773$$uhttps://zaguan.unizar.es/record/125313/files/texto_completo.pdf$$yVersión publicada
000125313 8564_ $$s2451801$$uhttps://zaguan.unizar.es/record/125313/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000125313 909CO $$ooai:zaguan.unizar.es:125313$$particulos$$pdriver
000125313 951__ $$a2024-11-22-12:07:56
000125313 980__ $$aARTICLE