Beta regression with spatio-temporal effects as a tool for hospital impact analysis of initial phase epidemics: the case of COVID-19 in Spain

  1. Gil Jannes
  2. Jesús Barreal

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Abstract

COVID-19 has put an extraordinary strain on medical staff around the world, but also on hospital facilities and the global capacity of national healthcare systems. In this paper, Beta regression is introduced as a tool to analyze the rate of hospitalization and the proportion of Intensive Care Unit admissions over both hospitalized and diagnosed patients, with the aim of explaining as well as predicting, and thus allowing to better anticipate, the impact on hospital resources during an early-phase epidemic. This is applied to the initial phase COVID-19 pandemic in Spain and its different regions from 20-Feb to 08-Apr of 2020. Spatial and temporal factors are included in the Beta distribution through a precision factor. The model reveals the importance of the lagged data of hospital occupation, as well as the rate of recovered patients. Excellent agreement is found for next-day predictions, while even for multiple-day predictions (up to 12 days), robust results are obtained in most cases in spite of the limited reliability and consistency of the data.

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    SciScore for 10.1101/2020.06.27.20141614: (What is this?)

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    Table 1: Rigor

    NIH rigor criteria are not applicable to paper type.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

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  2. Version published to 10.1101/2020.06.27.20141614v1 on medRxiv