Abstract
To reduce the spread and the effect of the COVID-19 global pandemic, non-pharmaceutical interventions 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.
Original language | English |
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Article number | 21783 |
Journal | Scientific Reports |
Volume | 11 |
Issue number | 1 |
DOIs | |
State | Published - 8 Nov 2021 |
Bibliographical note
Publisher Copyright:© 2021, The Author(s).
Funding
We thank the Israel Science Foundation, the Binational Israel-China Science Foundation (Grant No. 3132/19), the NSF-BSF (Grant No. 2019740), the EU H2020 project RISE (Project No. 821115), the EU H2020 DIT4TRAM, and DTRA (Grant No. HDTRA-1-19-1-0016) for financial support.
Funders | Funder number |
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Binational Israel-China Science Foundation | 3132/19 |
EU H2020 | |
EU H2020 DIT4TRAM | HDTRA-1-19-1-0016 |
NSF-BSF | 2019740 |
Horizon 2020 Framework Programme | 821115 |
Israel Science Foundation |