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.
Highlights
To reduce the spread and the effect of the COVID-19 global pandemic, non-pharmaceutical interventions have been adopted on multiple occasions by governments
Among non-pharmaceutical interventions (NPIs), limitations to mobility of various degree have proven to be a successful strategy towards mitigating the spread of COVID-19 in populations across the world[1,2,3]
In this work we develop a framework based on methodologies from network science and percolation theory[22,42] to explore the effects of the COVID-19 pandemic on the network structure of human mobility in Italy before and during the epidemic of 2020, from its very beginning to the second wave
Summary
To reduce the spread and the effect of the COVID-19 global pandemic, non-pharmaceutical interventions have been adopted on multiple occasions by governments. 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. Starting with the first epidemic cluster in Wuhan, China, lockdown restrictions, i.e. full limitations of mobility on an entire territory, have been widely and effectively a dopted[4,5]. In addition to the direct effect of aiding in the containment of COVID-19, one major side effect of the limitations was a severe economic impact induced by the lockdown that has been felt globally. With systems being as interconnected as they are, efficient trade-off evaluation of all possible effects (businesses closing, impact on education of all levels from primary school through higher education and research, strain on healthcare systems and the higher-order impact due to lack of doctors or equipment) is almost impossible in the short term
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