The impact of the COVID-19 pandemic varied significantly across different countries, with important consequences in the definition of control and response strategies. In this work, to investigate the heterogeneity of this crisis, we analyse the spatial patterns of deaths attributed to COVID-19 in several European countries. To this end, we propose a Bayesian nonparametric approach, based on mixture of Gaussian processes coupled with Dirichlet process, to group the COVID-19 mortality curves. The model provides a flexible framework for the analysis of time series data, allowing the inclusion in the clustering procedure of different features of the series, such as spatial correlations, time varying parameters and measurement errors. We evaluate the proposed methodology on the death counts recorded at NUTS-2 regional level for several European countries in the period from March 2020 to February 2021.
