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A Bayesian-Deep Learning Model for Estimating COVID-19 Evolution in Spain

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  • Stefano Cabras

    (Department of Statistics, Universidad Carlos III de Madrid, 28903 Madrid, Spain)

Abstract

This work proposes a semi-parametric approach to estimate the evolution of COVID-19 (SARS-CoV-2) in Spain. Considering the sequences of 14-day cumulative incidence of all Spanish regions, it combines modern Deep Learning (DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma model for counts. The DL model provides a suitable description of the observed time series of counts, but it cannot give a reliable uncertainty quantification. The role of expert elicitation of the expected number of counts and its reliability is DL predictions’ role in the proposed modelling approach. Finally, the posterior predictive distribution of counts is obtained in a standard Bayesian analysis using the well known Poisson-Gamma model. The model allows to predict the future evolution of the sequences on all regions or estimates the consequences of eventual scenarios.

Suggested Citation

  • Stefano Cabras, 2021. "A Bayesian-Deep Learning Model for Estimating COVID-19 Evolution in Spain," Mathematics, MDPI, vol. 9(22), pages 1-18, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:22:p:2921-:d:680700
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    References listed on IDEAS

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    1. Arianna Agosto & Paolo Giudici, 2020. "A Poisson Autoregressive Model to Understand COVID-19 Contagion Dynamics," Risks, MDPI, vol. 8(3), pages 1-8, July.
    2. Agosto, Arianna & Cavaliere, Giuseppe & Kristensen, Dennis & Rahbek, Anders, 2016. "Modeling corporate defaults: Poisson autoregressions with exogenous covariates (PARX)," Journal of Empirical Finance, Elsevier, vol. 38(PB), pages 640-663.
    3. Richard A. Davis & Konstantinos Fokianos & Scott H. Holan & Harry Joe & James Livsey & Robert Lund & Vladas Pipiras & Nalini Ravishanker, 2021. "Count Time Series: A Methodological Review," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1533-1547, May.
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    Cited by:

    1. Li Zhang & Ying-Ying Zhang, 2022. "The Bayesian Posterior and Marginal Densities of the Hierarchical Gamma–Gamma, Gamma–Inverse Gamma, Inverse Gamma–Gamma, and Inverse Gamma–Inverse Gamma Models with Conjugate Priors," Mathematics, MDPI, vol. 10(21), pages 1-27, October.

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