ABSTRACT
During the ongoing Covid-19 pandemic, understanding the spatiotemporal patterns of the virus is crucial for policymakers to intervene promptly. The relevance of spatial proximity in the spread of the pandemic necessitates adequate tools, and noisy data must be properly treated. This study proposes obtaining clusters of European regions using smoothed curves of daily deaths from March 2020-March 2022. A functional representation of the curves w<s implemented to extract the features used in a clustering algorithm that allows spatial proximity. In a spatial regression model, the authors also investigated the role of clusters and pre-existing conditions on cumulative deaths, and observed that air pollution, health conditions, and population age structure are significantly associated with Covid-19 confirmed deaths.
ABSTRACT
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.