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Socioeconomic inequalities significantly influence infectious disease outcomes, as seen with COVID-19, but the pathways through which socioeconomic conditions affect transmission dynamics remain unclear. To address this, we conducted a survey representative of the Italian population, stratified by age, gender, geographical area, city size, employment status, and education level. The survey’s final aim was to estimate differences in contact and protective behaviors across various population strata, both of which are crucial for understanding transmission dynamics. Our initial insights based on the survey indicate that years after the pandemic began, the perceived impact of COVID-19 on professional, economic, social, and psychological dimensions vary across socioeconomic strata, extending beyond the epidemiological outcomes. This reinforces the need for approaches that systematically consider socioeconomic determinants. In this context, using generalized linear models, we identified associations between socioeconomic factors and vaccination status for both COVID-19 and influenza, as well as the influence of socioeconomic conditions on mask-wearing and social distancing. Importantly, we also observed differences in contact behaviors based on employment status while education level did not show a significant association. These findings highlight the complex interplay of socioeconomic and demographic factors in shaping protective behavior and contact patterns. Understanding these dynamics can contribute to the improvement of epidemic models and better guide public health efforts for at-risk groups.

The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule.

Italy has been severely affected by the COVID-19 pandemic, reporting the highest death toll in Europe as of April 2020. Following the identification of the first infections, on February 21, 2020, national authorities have put in place an increasing number of restrictions aimed at containing the outbreak and delaying the epidemic peak. On March 12, the government imposed a national lockdown. To aid the evaluation of the impact of interventions, we present daily time-series of three different aggregated mobility metrics: the origin-destination movements between Italian provinces, the radius of gyration, and the average degree of a spatial proximity network. All metrics were computed by processing a large-scale dataset of anonymously shared positions of about 170,000 de-identified smartphone users before and during the outbreak, at the sub-national scale. This dataset can help to monitor the impact of the lockdown on the epidemic trajectory and inform future public health decision making.Measurement(s)mobilityTechnology Type(s)GPS navigation systemFactor Type(s)temporal intervalSample Characteristic - OrganismHomo sapiensSample Characteristic - LocationItalyMachine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12515256

The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics, yet these concepts are often at the margins of the computational modeling community. Building on recent research studies in the area of digital and computational epidemiology, we provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models. The COVID-19 pandemic has highlighted how structural social inequities fundamentally shape disease dynamics. Here, the authors provide a set of practical and methodological recommendations to address socioeconomic vulnerabilities in epidemic models.

Mobile phone data have been extensively used to study urban mobility. However, studies based on gender-disaggregated large-scale data are still lacking, limiting our understanding of gendered aspects of urban mobility and our ability to design policies for gender equality. Here we study urban mobility from a gendered perspective, combining commercial and open datasets for the city of Santiago, Chile. We analyze call detail records for a large cohort of anonymized mobile phone users and reveal a gender gap in mobility: women visit fewer unique locations than men, and distribute their time less equally among such locations. Mapping this mobility gap over administrative divisions, we observe that a wider gap is associated with lower income and lack of public and private transportation options. Our results uncover a complex interplay between gendered mobility patterns, socio-economic factors and urban affordances, calling for further research and providing insights for policymakers and urban planners.

Network embedding techniques are powerful to capture structural regularities in networks and to identify similarities between their local fabrics. However, conventional network embedding models are developed for static structures, commonly consider nodes only and they are seriously challenged when the network is varying in time. Temporal networks may provide an advantage in the description of real systems, but they code more complex information, which could be effectively represented only by a handful of methods so far. Here, we propose a new method of event embedding of temporal networks, called weg2vec , which builds on temporal and structural similarities of events to learn a low dimensional representation of a temporal network. This projection successfully captures latent structures and similarities between events involving different nodes at different times and provides ways to predict the final outcome of spreading processes unfolding on the temporal structure.

Despite the availability of effective vaccines against SARS-CoV-2, non-pharmaceutical interventions remain an important part of the effort to reduce viral circulation caused by emerging variants with the capability of evading vaccine-induced immunity. With the aim of striking a balance between effective mitigation and long-term sustainability, several governments worldwide have adopted systems of tiered interventions, of increasing stringency, that are calibrated according to periodic risk assessments. A key challenge remains in quantifying temporal changes in adherence to interventions, which can decrease over time due to pandemic fatigue, under such kind of multilevel strategies. Here, we examine whether there was a reduction in adherence to tiered restrictions that were imposed in Italy from November 2020 through May 2021, and in particular we assess whether temporal trends in adherence depended on the intensity of the restrictions adopted. We analyzed daily changes in movements and in residential time, combining mobility data with the restriction tier enforced in the Italian regions. Through mixed-effects regression models, we identified a general trend of reduction in adherence and an additional effect of faster waning associated with the most stringent tier. We estimated both effects being of the same order of magnitude, suggesting that adherence decreased twice as fast during the strictest tier as in the least stringent one. Our results provide a quantitative measure of behavioral responses to tiered interventions—a metric of pandemic fatigue—that can be integrated into mathematical models to evaluate future epidemic scenarios.

Assessing the impact of mobility on epidemic spreading is of crucial importance for understanding the effect of policies like mass quarantines and selective re-openings. While many factors affect disease incidence at a local level, making it more or less homogeneous with respect to other areas, the importance of multi-seeding has often been overlooked. Multi-seeding occurs when several independent (non-clustered) infected individuals arrive at a susceptible population. This can lead to independent outbreaks that spark from distinct areas of the local contact (social) network. Such mechanism has the potential to boost incidence, making control efforts and contact tracing less effective. Here, through a modeling approach we show that the effect produced by the number of initial infections is non-linear on the incidence peak and peak time. When case importations are carried by mobility from an already infected area, this effect is further enhanced by the local demography and underlying mixing patterns: the impact of every seed is larger in smaller populations. Finally, both in the model simulations and the analysis, we show that a multi-seeding effect combined with mobility restrictions can explain the observed spatial heterogeneities in the first wave of COVID-19 incidence and mortality in five European countries. Our results allow us for identifying what we have called epidemic epicenter: an area that shapes incidence and mortality peaks in the entire country. The present work further clarifies the nonlinear effects that mobility can have on the evolution of an epidemic and highlight their relevance for epidemic control.

As the first decentralized digital currency introduced in 2009 together with the blockchain, Bitcoin offers new opportunities both for developed and developing countries. Bitcoin peer-to-peer transactions are independent of the banking system, facilitating foreign exchanges with low transaction fees, such as remittances, and offering a high degree of anonymity. These opportunities together with other key factors led the Bitcoin to become extremely popular and caused its price to skyrocket during 2017 (Henry et al. in J Digit Bank 2(4):311–337, 2018 ). However, while the Bitcoin blockchain attracts a lot of attention, it remains difficult to investigate where this attention comes from, due to the pseudo-anonymity of the system, and consequently to appreciate its social impact. Here we make an attempt to characterize the adoption of the Bitcoin blockchain by country. In the first part of the work we show that information about the number of Bitcoin software client downloads, the IP addresses that act as relays for the transactions, and the Internet searches about Bitcoin provide together a coherent picture of the system evolution in different countries. Using these quantities as a proxy for user adoption, we identify several socio-economic indexes such as the GDP per capita, freedom of trade and the Internet penetration as key variables correlated with the degree of user adoption. In the second part of the work, we build a network of Bitcoin transactions between countries using the IP addresses of nodes relaying transactions and we develop an augmented version of the gravity model of trade in order to identify socio-economic factors linked to the flow of Bitcoin between countries. In a nutshell our study provides a new insight on Bitcoin adoption by country and on the potential socio-economic drivers of the international Bitcoin flow.

One of the targets of the UN Sustainable Development Goals is to substantially reduce the number of global deaths and injuries from road traffic collisions. To this aim, European cities adopted various urban mobility policies, which has led to a heterogeneous number of injuries across Europe. Monitoring the discrepancies in injuries and understanding the most efficient policies are keys to achieve the objectives of Vision Zero, a multi-national road traffic safety project that aims at zero fatalities or serious injuries linked to road traffic. Here, we identify urban features that are determinants of vulnerable road user safety through the analysis of inter-mode collision data across European cities. We first build up a data set of urban road crashes and their participants from 24 cities in 5 European countries, using the widely recommended KSI indicator (killed or seriously injured individuals) as a safety performance metric. Modelling the casualty matrices including road infrastructure characteristics and modal share distribution of the different cities, we observe that cities with the highest rates of walking and cycling modal shares are the safest for the most vulnerable users. Instead, a higher presence of low-speed limited roads seems to only significantly reduce the number of injuries of car occupants. Our results suggest that policies aimed at increasing the modal share of walking and cycling are key to improve road safety for all road users.