Unraveling the COVID-19 hospitalization dynamics in Spain using Bayesian inference.

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

SUBMITTER: Aleta A 

PROVIDER: S-EPMC9875773 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature