Abstract :When COVID-19 first struck the provinces of North-ern Italy in early 2020 (especially in Lombardy and in Emilia-Romagna), the conditions there made it a perfect storm. The virus outbreak spread with an unusual violence (in the periodfrom late February to April 2020), with a catastrophic toll interms of human deaths. Taken by surprise, Italy mandated a complete nation-wide lockdown, successively resorting to minis-terial decrees alleviating and postponing the restrictions. Now more than ever, there is an increased awareness onICT used to combat the pandemic. In this article, we presenta quantitative analysis evidencing the impact of restrictions onmobility. To this end, we rely on a vehicular mobility datasetconfined in the downtown area of Bologna, Italy. Pursuing the objective, we propose a modified version of a state-of-the-art data mining algorithm, allowing us to efficiently identify and quantify mobility flows. The proposal, if combined withadditional data sources, could allow for a fine-grained and timelydecision making, combating the pandemic.
Index terms :Big data, COVID-19, pattern mining, vehicular mobility.