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Using anonymized mobility data from Facebook users and publicly availableinformation on the Italian population, we model the circulation of people inItaly before and during the early phase of the SARS-CoV-2 pandemic (COVID-19).We perform a spatial and temporal clustering of the movement network at thelevel of fluxes across provinces on a daily basis. The resulting partition intime successfully identifies the first two lockdowns without any priorinformation. Similarly, the spatial clustering returns 11 to 23 clustersdepending on the period ("standard" mobility vs. lockdown) using the greedymodularity communities clustering method, and 16 to 30 clusters using thecritical variable selection method. Fascinatingly, the spatial clustersobtained with both methods are strongly reminiscent of the 11 regions intowhich emperor Augustus had divided Italy according to Pliny the Elder. Thiswork introduces and validates a data analysis pipeline that enables us: i) toassess the reliability of data obtained from a partial and potentially biasedsample of the population in performing estimates of population mobilitynationwide; ii) to identify areas of a Country with well-defined mobilitypatterns, and iii) to distinguish different patterns from one another, resolvethem in time and find their optimal spatial extent. The proposed method isgeneric and can be applied to other countries, with different geographicalscales, and also to similar networks (e.g. biological networks). The resultscan thus represent a relevant step forward in the development of methods andstrategies for the containment of future epidemic phenomena.

Emergent circulation patterns from anonymized mobility data: Clustering Italy in the time of Covid