论文标题
贝叶斯 - 深度学习模型，用于估计西班牙的共vid -19
A Bayesian - Deep Learning model for estimating Covid-19 evolution in Spain
论文作者
论文摘要
这项工作提出了一种半参数方法来估计西班牙的Covid-19（SARS-COV-2）进化。考虑到所有西班牙地区的14天累积发生率的序列，它结合了现代深度学习（DL）技术，用于与通常的贝叶斯Poisson-Gamma模型分析序列的计数。 DL模型提供了对观察到的序列的适当描述，但无法获得可靠的不确定性定量。为了克服这一点，我们使用DL的预测作为对预期数量数量的专家启发以及它们的不确定性，从而使用众所周知的Poisson-Gamma模型在正统的贝叶斯分析中获得了计数的后验预测分布。总体产生的模型使我们能够预测所有区域上序列的未来演变，并估计最终场景的后果。
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.
