A Bayesian - Deep Learning model for estimating Covid-19 evolution in\n Spain

2020/05/20 by Stefano Cabras, Cabras, Stefano
Computer Science · Mathematics · Medicine · #62P10 #Anomaly Detection Techniques and Applications #Applications (stat.AP) #COVID-19 diagnosis using AI #COVID-19 epidemiological studies #FOS: Computer and information sciences #G.3 #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.2005.10335

openalex publication_date 2020/05/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This work proposes a semi-parametric approach to estimate Covid-19\n(SARS-CoV-2) evolution in Spain. Considering the sequences of 14 days\ncumulative incidence of all Spanish regions, it combines modern Deep Learning\n(DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma\nmodel for counts. DL model provides a suitable description of observed\nsequences but no reliable uncertainty quantification around it can be obtained.\nTo overcome this we use the prediction from DL as an expert elicitation of the\nexpected number of counts along with their uncertainty and thus obtaining the\nposterior predictive distribution of counts in an orthodox Bayesian analysis\nusing the well known Poisson-Gamma model. The overall resulting model allows us\nto either predict the future evolution of the sequences on all regions, as well\nas, estimating the consequences of eventual scenarios.\n

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