A Bayesian - Deep Learning model for estimating Covid-19 evolution in Spain
This work addresses COVID-19 forecasting for public health in Spain, representing an incremental improvement by integrating existing methods.
The paper tackles the problem of estimating COVID-19 evolution in Spain by proposing a semi-parametric model that combines deep learning for sequence analysis with a Bayesian Poisson-Gamma model for counts, resulting in predictions of future evolution and scenario consequences with uncertainty quantification.
This work proposes a semi-parametric approach to estimate Covid-19 (SARS-CoV-2) evolution in Spain. Considering the sequences of 14 days 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. 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 along with their uncertainty and thus obtaining the posterior predictive distribution of counts in an orthodox Bayesian analysis using the well known Poisson-Gamma model. 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 scenarios.