Article ; Online: Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference.
BMC medical research methodology
2023 Volume 23, Issue 1, Page(s) 24
Abstract: 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, ... ...
Abstract | 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. |
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MeSH term(s) | Humans ; COVID-19/epidemiology ; SARS-CoV-2 ; Spain/epidemiology ; Pandemics ; Bayes Theorem ; Hospitalization |
Language | English |
Publishing date | 2023-01-25 |
Publishing country | England |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 2041362-2 |
ISSN | 1471-2288 ; 1471-2288 |
ISSN (online) | 1471-2288 |
ISSN | 1471-2288 |
DOI | 10.1186/s12874-023-01842-7 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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