@article {1339,
	title = {The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy},
	journal = {arXiv preprint arXiv:2006.03141},
	year = {2020},
	abstract = {We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region{\textquoteright}s incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.},
	url = {https://arxiv.org/abs/2006.03141},
	author = {Paolo Cintia and Daniele Fadda and Fosca Giannotti and Luca Pappalardo and Giulio Rossetti and Dino Pedreschi and S Rinzivillo and Bonato, Pietro and Fabbri, Francesco and Penone, Francesco and Savarese, Marcello and Checchi, Daniele and Chiaromonte, Francesca and Vineis , Paolo and Guzzetta, Giorgio and Riccardo, Flavia and Marziano, Valentina and Poletti, Piero and Trentini, Filippo and Bella, Antonio and Andrianou, Xanthi and Del Manso, Martina and Fabiani, Massimo and Bellino, Stefania and Boros, Stefano and Mateo Urdiales, Alberto and Vescio, Maria Fenicia and Brusaferro, Silvio and Rezza, Giovanni and Pezzotti, Patrizio and Ajelli, Marco and Merler, Stefano}
}
@article {723,
	title = {Returners and explorers dichotomy in human mobility},
	journal = {Nat Commun},
	volume = {6},
	year = {2015},
	month = {09},
	abstract = {The availability of massive digital traces of human whereabouts has offered a series of novel insights on the quantitative patterns characterizing human mobility. In particular, numerous recent studies have lead to an unexpected consensus: the considerable variability in the characteristic travelled distance of individuals coexists with a high degree of predictability of their future locations. Here we shed light on this surprising coexistence by systematically investigating the impact of recurrent mobility on the characteristic distance travelled by individuals. Using both mobile phone and GPS data, we discover the existence of two distinct classes of individuals: returners and explorers. As existing models of human mobility cannot explain the existence of these two classes, we develop more realistic models able to capture the empirical findings. Finally, we show that returners and explorers play a distinct quantifiable role in spreading phenomena and that a correlation exists between their mobility patterns and social interactions.},
	url = {http://dx.doi.org/10.1038/ncomms9166},
	author = {Luca Pappalardo and Filippo Simini and S Rinzivillo and Dino Pedreschi and Fosca Giannotti and Barabasi, Albert-Laszlo}
}
@article {828,
	title = {The retail market as a complex system},
	journal = {EPJ Data Science},
	volume = {3},
	number = {1},
	year = {2014},
	pages = {1{\textendash}27},
	abstract = {Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country{\textquoteright}s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products{\textquoteright} volumes of sales with the customers{\textquoteright} volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.},
	doi = {10.1140/epjds/s13688-014-0033-x},
	url = {http://link.springer.com/article/10.1140/epjds/s13688-014-0033-x},
	author = {Diego Pennacchioli and Michele Coscia and S Rinzivillo and Fosca Giannotti and Dino Pedreschi}
}
@inbook {636,
	title = {Retrieving Points of Interest from Human Systematic Movements},
	booktitle = {Software Engineering and Formal Methods},
	year = {2014},
	pages = {294{\textendash}308},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {Human mobility analysis is emerging as a more and more fundamental task to deeply understand human behavior. In the last decade these kind of studies have become feasible thanks to the massive increase in availability of mobility data. A crucial point, for many mobility applications and analysis, is to extract interesting locations for people. In this paper, we propose a novel methodology to retrieve efficiently significant places of interest from movement data. Using car drivers{\textquoteright} systematic movements we mine everyday interesting locations, that is, places around which people life gravitates. The outcomes show the empirical evidence that these places capture nearly the whole mobility even though generated only from systematic movements abstractions.},
	doi = {10.1007/978-3-319-15201-1_19},
	author = {Riccardo Guidotti and Anna Monreale and S Rinzivillo and Dino Pedreschi and Fosca Giannotti}
}
