  We investigate patterns of COVID-19 mortality across 20 Italian regions and
their association with mobility, positivity, and socio-demographic,
infrastructural and environmental covariates. Notwithstanding limitations in
accuracy and resolution of the data available from public sources, we pinpoint
significant trends exploiting information in curves and shapes with Functional
Data Analysis techniques. These depict two starkly different epidemics; an
"exponential" one unfolding in Lombardia and the worst hit areas of the north,
and a milder, "flat(tened)" one in the rest of the country -- including Veneto,
where cases appeared concurrently with Lombardia but aggressive testing was
implemented early on. We find that mobility and positivity can predict COVID-19
mortality, also when controlling for relevant covariates. Among the latter,
primary care appears to mitigate mortality, and contacts in hospitals, schools
and work places to aggravate it. The techniques we describe could capture
additional and potentially sharper signals if applied to richer data.
