Unraveling the COVID-19 hospitalization dynamics in Spain using publicly available data

  1. Alberto Aleta
  2. Juan Luis Blas-Laína
  3. Gabriel Tirado Anglés
  4. Yamir Moreno

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

One of the main challenges of the ongoing COVID-19 pandemic is to be able to make sense of available, but often heterogeneous and noisy data, to characterize the evolution of the SARS-CoV-2 infection dynamics, with the additional goal of having better preparedness and planning of healthcare services. 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.

Methods

We use data on new daily cases and hospitalizations reported by the Ministry of Health of Spain to implement a Bayesian inference method that allows making short and mid-term predictions of bed occupancy of COVID-19 patients in each of the autonomous regions of the country.

Findings

We show how to use given and generated 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.

Interpretation

The amount of data that is currently available is limited, and sometimes unreliable, hindering our understanding of many aspects of this pandemic. We have observed 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.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.09.03.21263086: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    At the same time, it has highlighted the weaknesses of the information systems of many governmental and healthcare institutions. Even when the data is openly shared, the lack of information on how it was obtained, continuous changes in government policies, and availability of resources, makes it challenging to use the data and perform proper comparisons between different regions. This can be easily seen when data from different sources, or from the same source but for different observables, are compared. The model presented here focuses on incidence and bed occupancy data. In particular, it reproduces the dynamics associated with hospitalization as a function of the number of detected cases. In the case of Aragon, we have obtained an admission probability of and the distribution governing hospital admission follows a Half-Cauchy distribution with βAR =3·557[2·579-4·564] (95% C.I.). This distribution yields a median interval of 3·5 days and an IQR of 7 days, which is remarkably close to the estimated values based on individual data provided by the regional government (median 4 days, IQR 6 days). Note that small differences are expected since the way in which hospitals record case detection and admission might not be the same as the one provided by the Ministry of Health. For instance, while the Ministry of Health reports new cases as a function of symptoms’ onset, the regional government provides new cases based on notification date. Similarly, the distribution on the length o...

    Results from TrialIdentifier: No clinical trial numbers were referenced.

    Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

    Results from JetFighter: We did not find any issues relating to colormaps.

    Results from rtransparent:
    • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2021.09.03.21263086v1 on medRxiv