A look at the spatio-temporal mortality patterns in Italy during the COVID-19 pandemic through the lens of mortality densities
Friday 14th May 2021
Scimone, R.; Menafoglio, A.; Sangalli, L.M.; Secchi, P.
With the tools and perspective of Object Oriented Spatial Statistics, we analyze official daily data on mortality from all causes in the provinces and municipalities of Italy for the year 2020, the first of the COVID-19 pandemic. By comparison with mortality data from 2011 to 2019, we assess the local impact of the pandemic as perturbation factor of the natural spatio-temporal death process. For each Italian province, mortality data are represented by the densities of time of death in the year. Densities are regarded as functional data belonging to the Bayes space B^2 where we use functional-on-functional linear models to predict the expected mortality in 2020, based on mortality in previous years, and we compare predictions with actual observations to assess the impact of the pandemic. Through spatial downscaling of the provincial data down to the municipality level, we identify spatial clusters characterized by mortality densities anomalous with respect to those predicted based on mortality data of the nearby areas. This analysis pipeline could be extended to indexes different from death counts, measured at a granular spatio-temporal scale, and used as proxies for quantifying the local disruption generated by the pandemic.