  To reduce the spread and the effect of the COVID-19 global pandemic,
non-pharmaceutical interventions (NPIs) have been adopted on multiple occasions
by governments. In particular lockdown policies, i.e., generalized mobility
restrictions, have been employed to fight the first wave of the pandemic. We
analyze data reflecting mobility levels over time in Italy before, during and
after the national lockdown, in order to assess some direct and indirect
effects. By applying methodologies based on percolation and network science
approaches, we find that the typical network characteristics, while very
revealing, do not tell the whole story. In particular, the Italian mobility
network during lockdown has been damaged much more than node- and edge-level
metrics indicate. Additionally, many of the main Provinces of Italy are
affected by the lockdown in a surprisingly similar fashion, despite their
geographical and economic dissimilarity. Based on our findings we offer an
approach to estimate unavailable high-resolution economic dimensions, such as
real time Province-level GDP, based on easily measurable mobility information.
