Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference
Aleta, Alberto (Universidad de Zaragoza) ; Blas-Laína, Juan Luis (Universidad de Zaragoza) ; Tirado Anglés, Gabriel ; Moreno, Yamir (Universidad de Zaragoza)
Resumen: 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.
Idioma: Inglés
DOI: 10.1186/s12874-023-01842-7
Año: 2023
Publicado en: BMC Medical Research Methodology 23 (2023), 24 [11 pp.]
ISSN: 1471-2288
Factor impacto JCR: 3.9 (2023)
Categ. JCR: HEALTH CARE SCIENCES & SERVICES rank: 24 / 174 = 0.138 (2023) - Q1 - T1
Factor impacto CITESCORE: 6.5 - Epidemiology (Q1) - Health Informatics (Q2)
Factor impacto SCIMAGO: 1.632 - Health Informatics (Q1) - Epidemiology (Q1)
Financiación: info:eu-repo/grantAgreement/ES/DGA/E36-20R
Financiación: info:eu-repo/grantAgreement/ES/MCIN-AEI-FEDER/PID2020-115800GB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RYC2021-033226-I
Financiación: info:eu-repo/grantAgreement/ES/UZ/UZ-SANTANDER/2020-0274
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Cirugía (Dpto. Cirugía)
Área (Departamento): Área Física Teórica (Dpto. Física Teórica)
Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace.
Exportado de SIDERAL (2024-11-22-12:07:56)
Visitas y descargas
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.
Idioma: Inglés
DOI: 10.1186/s12874-023-01842-7
Año: 2023
Publicado en: BMC Medical Research Methodology 23 (2023), 24 [11 pp.]
ISSN: 1471-2288
Factor impacto JCR: 3.9 (2023)
Categ. JCR: HEALTH CARE SCIENCES & SERVICES rank: 24 / 174 = 0.138 (2023) - Q1 - T1
Factor impacto CITESCORE: 6.5 - Epidemiology (Q1) - Health Informatics (Q2)
Factor impacto SCIMAGO: 1.632 - Health Informatics (Q1) - Epidemiology (Q1)
Financiación: info:eu-repo/grantAgreement/ES/DGA/E36-20R
Financiación: info:eu-repo/grantAgreement/ES/MCIN-AEI-FEDER/PID2020-115800GB-I00
Financiación: info:eu-repo/grantAgreement/ES/MICINN/RYC2021-033226-I
Financiación: info:eu-repo/grantAgreement/ES/UZ/UZ-SANTANDER/2020-0274
Tipo y forma: Artículo (Versión definitiva)
Área (Departamento): Área Cirugía (Dpto. Cirugía)
Área (Departamento): Área Física Teórica (Dpto. Física Teórica)
Debe reconocer adecuadamente la autoría, proporcionar un enlace a la licencia e indicar si se han realizado cambios. Puede hacerlo de cualquier manera razonable, pero no de una manera que sugiera que tiene el apoyo del licenciador o lo recibe por el uso que hace. Exportado de SIDERAL (2024-11-22-12:07:56)
Enlace permanente:
Visitas y descargas
Este artículo se encuentra en las siguientes colecciones:
Artículos > Artículos por área > Física Teórica
Artículos > Artículos por área > Cirugía
Registro creado el 2023-03-23, última modificación el 2024-11-25