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

Stefano Cabras
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Stefano Cabras: Department of Statistics, Universidad Carlos III de Madrid, 28903 Madrid, Spain

Mathematics, 2021, vol. 9, issue 22, 1-18

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.

Keywords: applied Bayesian methods; COVID-19; Deep Learning; Multivariate Time Series; LSTM; SARS-CoV-2 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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