Title: Emergent circulation patterns from anonymized mobility data: Clustering Italy in the time of Covid Show Chinese title

Title: 从匿名移动数据中出现的循环模式：疫情时期的意大利聚类

Abstract: Using anonymized mobility data from Facebook users and publicly available information on the Italian population, we model the circulation of people in Italy 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 the level of fluxes across provinces on a daily basis. The resulting partition in time successfully identifies the first two lockdowns without any prior information. Similarly, the spatial clustering returns 11 to 23 clusters depending on the period ("standard" mobility vs. lockdown) using the greedy modularity communities clustering method, and 16 to 30 clusters using the critical variable selection method. Fascinatingly, the spatial clusters obtained with both methods are strongly reminiscent of the 11 regions into which emperor Augustus had divided Italy according to Pliny the Elder. This work introduces and validates a data analysis pipeline that enables us: i) to assess the reliability of data obtained from a partial and potentially biased sample of the population in performing estimates of population mobility nationwide; ii) to identify areas of a Country with well-defined mobility patterns, and iii) to distinguish different patterns from one another, resolve them in time and find their optimal spatial extent. The proposed method is generic and can be applied to other countries, with different geographical scales, and also to similar networks (e.g. biological networks). The results can thus represent a relevant step forward in the development of methods and strategies for the containment of future epidemic phenomena. Show Chinese abstract

Abstract: 使用来自Facebook用户的匿名移动数据和意大利人口的公开信息，我们模拟了在SARS-CoV-2大流行早期阶段（COVID-19）之前和期间意大利的人口流动情况。 我们在每日基础上对省际流量的运动网络进行空间和时间聚类。 时间上的聚类结果成功地在没有任何先验信息的情况下识别出了前两次封锁。 同样，空间聚类在不同时间段（“标准”移动 vs. 封锁）中返回11到23个聚类（使用贪婪模块性社区聚类方法），以及16到30个聚类（使用关键变量选择方法）。 有趣的是，两种方法得到的空间聚类与奥古斯都皇帝根据老普林尼的记载将意大利划分为的11个地区非常相似。 这项工作介绍并验证了一个数据分析流程，使我们能够：i）评估从人口部分且可能有偏的样本中获得的数据在进行全国人口流动估计时的可靠性；ii）识别具有明确流动模式的国家区域；iii）区分不同的模式，解析它们的时间，并找到它们的最佳空间范围。 所提出的方法是通用的，可以应用于其他国家，不同地理尺度，也可以用于类似的网络（例如生物网络）。 因此，这些结果可以代表在开发未来流行病防控方法和策略方面的重大进展。