Lorenzo Lucchini

The collection of updated post-COVID-19 data on social contact patterns is critical for future epidemiological assessment and evaluation of non-pharmaceutical interventions. We conducted two waves of an online survey in Italy (March 2022 and March 2023), collecting representative data on direct (verbal/physical) and indirect (indoor co-location) contacts. Using a generalised linear mixed model, we analysed social contact determinants and the impact of work-from-home and distance learning on pathogen transmissibility. In-person work or school attendance significantly increased contacts: adults attending in person had 1.69 times (95% CI: 1.56–1.84) more contacts than those staying home, while for children and adolescents, this ratio was 2.38 (95% CI: 1.98–2.87). Even suspending all non-essential work had a marginal effect on transmissibility. However, combining work-from-home with distance learning (from primary school onwards) could reduce transmissibility by up to 23.7% (95% CI: 18.2–29.0%), with minimal additional benefit from suspending early childcare. These findings offer key data for modelling respiratory pathogen transmission in Italy post-COVID-19 and provide insights into the epidemiological impact of tailored distancing measures. They support a nuanced approach to social distancing policies, balancing public health benefits with economic and social considerations.

Infection control requires integrating behavioural dynamics into epidemic models. However, models often overlook behavioural complexity due to limited empirical data. We adopted an interdisciplinary approach to develop a modelling-oriented behavioural dataset. We applied the Capability , Opportunity , Motivation -Behaviour (COM-B) framework to assess COVID-19 vaccination behaviour from 22,228 survey responses across six European countries (March–July 2024). We examined how willingness to vaccinate aligned with uptake and timeliness, and traced country-specific temporal changes in perceived COVID-19 severity and willingness. To capture peer-driven opinion formation, we introduced a novel indicator – discussion contacts – measuring interactions on relevant topics. Willingness correlated with both uptake and timeliness, yet 6-18% of initially unwilling respondents ultimately received ≥2 doses. Lower willingness was associated with a 2.0-month vaccination delay. Perceived severity and willingness to vaccinate declined throughout the pandemic. Behavioural indicators systematically varied by vaccination status, particularly in the Motivation domain. Daily discussion contacts followed the patterns of in-person contacts, ranging from 1.4–2.9 for adults <60 years to 0.5–1.2 for those ≥60 years. This dataset offers theory-informed and time-sensitive inputs to support the development of more realistic and policy-relevant behavioural-epidemic models.

Non-pharmaceutical measures such as preventive quarantines, remote working, school and workplace closures, lockdowns, etc. have shown effectiveness from an epidemic control perspective; however, they have also significant negative consequences on social life and relationships, work routines and community engagement. In particular, complex ideas, work and school collaborations, innovative discoveries and resilient norms formation and maintenance, which often require face-to-face interactions of two or more parties to be developed and synergically coordinated, are particularly affected. In this study, we propose an alternative hybrid solution that balances the slowdown of epidemic diffusion with the preservation of face-to-face interactions, that we test simulating a disease and a knowledge spreading simultaneously on a network of contacts. Our approach involves a two-step partitioning of the population. First, we tune the level of node clustering, creating ‘social bubbles’ with increased contacts within each bubble and fewer outside, while maintaining the average number of contacts in each network. Second, we tune the level of temporal clustering by pairing, for a certain time interval, nodes from specific social bubbles. Our results demonstrate that a hybrid approach can achieve better trade-offs between epidemic control and complex knowledge diffusion. The versatility of our model enables tuning and refining clustering levels to optimally achieve the desired trade-off, based on the potentially changing characteristics of a disease or knowledge diffusion process.

In January 2021, retail investors coordinated on Reddit to target short-selling activity by hedge funds on GameStop shares, causing a surge in the share price and triggering significant losses for the funds involved. Such an effective collective action was unprecedented in finance, and its dynamics remain unclear. Here, we analyse Reddit and financial data and rationalize the events based on recent findings describing how a small fraction of committed individuals may trigger behavioural cascades. First, we operationalize the concept of individual commitment in financial discussions. Second, we show that the increase of commitment within Reddit pre-dated the initial surge in price. Third, we reveal that initial committed users occupied a central position in the network of Reddit conversations. Finally, we show that the social identity of the broader Reddit community grew as the collective action unfolded. These findings shed light on financial collective action, as several observers anticipate it will grow in importance.

The collaborative nature of code development affects trading of cryptocurrencies, showing that transparency is a network property.

The steady growth of digitized historical information is continuously stimulating new different approaches to the fields of Digital Humanities and Computational Social Science. In this work, we use Natural Language Processing techniques to retrieve large amounts of historical information from Wikipedia. In particular, the pages of a set of historically notable individuals are processed to catch the locations and the date of people's movements. This information is then structured in a geographical network of mobility patterns. We analyze the mobility of historically notable individuals from different perspectives to better understand the role of migrations and international collaborations in the context of innovation and cultural development. In this work, we first present some general characteristics of the dataset from a social and geographical perspective. Then, we build a spatial network of cities, and we model and quantify the tendency to explore by a set of people that can be considered historically and culturally notable. In this framework, we show that by using a multilevel radiation model for human mobility, we are able to catch important features of migration's behavior. Results show that the choice of the target migration place for historically and culturally relevant people is limited to a small number of locations and that it depends on the discipline a notable is interested in and on the number of opportunities she/he can find there.

The steady growth of digitized historical information is continuously stimulating new different approaches to the fields of Digital Humanities and Computational Social Science. In this work we use Natural Language Processing techniques to retrieve large amounts of historical information from Wikipedia. In particular, the pages of a set of historically notable individuals are processed to catch the locations and the date of people’s movements. This information is then structured in a geographical network of mobility patterns.We analyze the mobility of historically notable individuals from different perspectives to better understand the role of migrations and international collaborations in the context of innovation and cultural development. In this work, we first present some general characteristics of the dataset from a social and geographical perspective. Then, we build a spatial network of cities, and we model and quantify the tendency to explore of a set of people that can be considered as historically and culturally notable. In this framework, we show that by using a multilevel radiation model for human mobility, we are able to catch important features of migration’s behavior. Results show that the choice of the target migration place for historically and culturally relevant people is limited to a small number of locations and that it depends on the discipline a notable is interested in and on the number of opportunities she/he can find there.