About: This work proposes a semi-parametric approach to estimate Covid-19 (SARS-CoV-2) evolution in Spain. Considering the sequences of cases, deaths, and recovered of all Spanish regions, it combines modern Deep Learning (DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma model for counts. DL model provides a suitable description of observed sequences but no reliable uncertainty quantification around it can be obtained. To overcome this we use the prediction from DL as an expert elicitation of the expected number of counts and thus obtaining the posterior predictive distribution of counts in an orthodox Bayesian analysis. The overall resulting model allows us to either predict the future evolution of the sequences on all regions, as well as, estimating the consequences of eventual future scenarios.   Goto Sponge  NotDistinct  Permalink

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  • This work proposes a semi-parametric approach to estimate Covid-19 (SARS-CoV-2) evolution in Spain. Considering the sequences of cases, deaths, and recovered of all Spanish regions, it combines modern Deep Learning (DL) techniques for analyzing sequences with the usual Bayesian Poisson-Gamma model for counts. DL model provides a suitable description of observed sequences but no reliable uncertainty quantification around it can be obtained. To overcome this we use the prediction from DL as an expert elicitation of the expected number of counts and thus obtaining the posterior predictive distribution of counts in an orthodox Bayesian analysis. The overall resulting model allows us to either predict the future evolution of the sequences on all regions, as well as, estimating the consequences of eventual future scenarios.
subject
  • Deep learning
  • Emerging technologies
  • Southern European countries
  • Artificial intelligence
  • Applied mathematics
  • Artificial neural networks
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