Reverse social contagion as a mechanism for regulating mass behaviors in highly integrated social systems.

Mass behavior is the rapid adoption of similar conduct by all group members, with potentially catastrophic outcomes such as mass panic. Yet, these negative consequences are rare in integrated social systems such as social insect colonies, thanks to mechanisms of social regulation. Here, we test the hypothesis that behavioral deactivation between active individuals is a powerful social regulator that reduces energetic spending in groups. Borrowing from scaling theories for human settlements and using behavioral data on harvester ants, we derive ties between the hypermetric scaling of the interaction network and the hypometric scaling of activity levels, both relative to the colony size. We use elements of economics theory and metabolic measurements collected with the behavioral data to link activity and metabolic scalings with group size. Our results support the idea that metabolic scaling across social systems is the product of different balances between their social regulation mechanisms.

Unveiling the Dark Nexus: A systematic review on the interplay of mental health, substance abuse, and socio-cultural factors in femicide.

A shared definition of femicide would help to distinguish it from the murder of a woman and understand its root causes favoring prevention. We conducted a Systematic Literature Review (SLR) to assess how (and if) femicide cases were related to mental disorders. Articles papers that explicitly define or discuss femicides or articles that, albeit not expressly mention femicides, thoroughly compare generic homicides and homicides with female victims. We analyse 3546 articles were retrieved from the databases, and 75 studies fulfilled the eligibility criteria and were included in the SLR. Many forms of femicide emerge worldwide as people's values, beliefs, attitudes, and behaviours evolve (intimate partner femicide, femicide-suicide, religious femicide, honour, revolt femicide) and state of vulnerability. A tiny percentage of femicides occur at the hands of subjects with diagnosed mental disorders, and controversies exist regarding the possible link between femicide and the use of drugs and/or alcohol and other factors. The complex problem of violence against women must be addressed with a transdisciplinary approach and targeted interventions for both the victims and the perpetrators. The present SLR shows that it is not possible to link femicides to mental disorders and that socio and cultural factors appear to be more relevant. Further quantitative research is warranted to disentangle the root causes of this heinous phenomenon plaguing our times. Our studies show that using the proposed definition of feminicide would help to delimit and adequately recognise violence in courtrooms, promote the culture of equality, and identify adequate policy strategies for prevention.

Emergence and Control of Synchronization in Networks with Directed Many-Body Interactions.

The emergence of collective behaviors in networks of dynamical units in pairwise interaction has been explained as the effect of diffusive coupling. How does the presence of higher-order interaction impact the onset of spontaneous or induced synchronous behavior? Inspired by actuation and measurement constraints typical of physical and engineered systems, we propose a diffusion mechanism over hypergraphs that explains the onset of synchronization through a clarifying analogy with signed graphs. Our findings are mathematically backed by general conditions for convergence to the synchronous state.

Migrants' mental health recovery in Italian reception facilities.

BACKGROUND: Forced migration leaves deep marks on the psychological well-being of migrants, with post-traumatic stress disorder (PTSD) and other psychological conditions being prevalent among them. While research has clarified the extent to which pre-migration trauma is a predictor of mental health outcomes, the role of post-migration stressors in the settlement environment are yet to be fully characterized. METHODS: We monitored mental health of a cohort of 100 asylum-seekers during their 14-day COVID-19-related quarantine in reception facilities in Rome, Italy, through the administration of six questionnaires (a demographic survey, the WHO-5 well-being index, the Primary Care PTSD Screen for Diagnostic and Statistical Manual of Mental Disorders 5 (DSM-5), the Harvard Trauma Questionnaire, the Trauma and Loss Spectrum-Self Report, and the LiMEs-Italian version). Through the combination of statistical analysis and supervised learning, we studied the impact of the first contact with the reception system on asylum-seekers' mental health and sought for possible risk and shielding factors for PTSD. RESULTS: We find that sheltering in refugee centers has a positive impact on migrants' mental health; asylum-seekers with PTSD reported more traumatic events and personality characteristics related to loss and trauma; life events are predictors of PTSD in asylum-seekers. CONCLUSIONS: We identify past traumatic experiences as predictors of PTSD, and establish the positive role the immediate post-migration environment can play on migrants' psychological well-being. We recommend for host countries to implement reception models that provide effective protection and integration of asylum-seekers, similar to those in the Italian system.

Traumatic experiences before and during migration can impact the psychological well-being of migrants. This can result in post-traumatic stress disorder (PTSD), a disorder in which prior experience of traumatic events can lead to severe anxiety. We asked migrants to fill in questionnaires about their well-being during a 14-day quarantine in reception facilities in Rome, Italy. We evaluated whether the migrants had symptoms of PTSD and the impact of the quarantine on their mental health. We found that migrants who had experienced past traumatic events were more likely to have PTSD, and that staying in a safe and welcoming place in Italy helped improve their mental wellbeing. These findings underline the importance of designing suitable policies to support migrants' mental health when they arrive in host countries.

Synchronizing network systems in the presence of limited resources via edge snapping.

In this work, we propose a multilayer control protocol for the synchronization of network dynamical systems under limited resources. In addition to the layer where the interactions of the system take place, i.e., the backbone network, we propose a second, adaptive layer, where the edges are added or removed according to the edge snapping mechanism. Different from classic edge snapping, the inputs to the edge dynamics are modified to cap the number of edges that can be activated. After studying the local stability of the overall network dynamics, we illustrate the effectiveness of the approach on a network of Rössler oscillators and then show its robustness in a more general setting, exemplified with a model of the Italian high-voltage power grid.

Femicide and forensic pathology: Proposal for a shared medico-legal methodology.

In a previous work, authors have proposed a medico-legal definition of femicide as the murder due to the failure to recognize the right of self-determination of women. The aim of this paper was to apply the proposed definition to a cohort of cases to characterise femicides and female homicides and assess whether femicides can be considered a distinct entity or not. A comparison between female and male homicides was performed to assess common and distinctive features. Femicides were identified and compared to the cohort of non-femicide female murder. Results were compared to those reported in published forensic studies. Significant associations between female and male homicides were found for sex and partner/ex-partner offender, sex and indoor homicide and sex and asphyxia as dynamic of death emerged. A higher prevalence of indoor homicides and asphyxiation and of partner relationships were documented in female homicides. Gunshot, blunt injuries and cut wounds are well represented in both types of homicides. Most affected sites are back and chest in male homicides, and head, breasts, pubis, and limbs in female homicides. When comparing femicides and female homicides, a positive association between strangulation as harmful mean and a negative one between femicides and indoor homicides were found. Male and female homicides can be considered as two distinct victimological phenomena. Focusing on femicide allows to establish injuries and circumstantial patterns, that could represent evidence of a specific murder. More studies with a standardized data collection are needed to corroborate the theory of this paper.

A model-based opinion dynamics approach to tackle vaccine hesitancy.

Uncovering the mechanisms underlying the diffusion of vaccine hesitancy is crucial in fighting epidemic spreading. Toward this ambitious goal, we treat vaccine hesitancy as an opinion, whose diffusion in a social group can be shaped over time by the influence of personal beliefs, social pressure, and other exogenous actions, such as pro-vaccine campaigns. We propose a simple mathematical model that, calibrated on survey data, can predict the modification of the pre-existing individual willingness to be vaccinated and estimate the fraction of a population that is expected to adhere to an immunization program. This work paves the way for enabling tools from network control towards the simulation of different intervention plans and the design of more effective targeted pro-vaccine campaigns. Compared to traditional mass media alternatives, these model-based campaigns can exploit the structural properties of social networks to provide a potentially pivotal advantage in epidemic mitigation.

Quantifying the role of the COVID-19 pandemic in the 2020 U.S. presidential elections.

In the media, a prevalent narrative is that the incumbent United States President Donald J. Trump lost the 2020 elections because of the way he handled the COVID-19 pandemic. Quantitative evidence to support this narrative is, however, limited. We put forward a spatial, information-theoretic approach to critically examine the link between voting behavior and COVID-19 incidence in the 2020 presidential elections. The approach overcomes classical limitations of traditional regression analysis, where it does not require an underlying mathematical model and it can capture nonlinear interactions. From the analysis of county-level data, we uncovered a robust association between voting behavior and prevalence of COVID-19 cases. Surprisingly, such an association points in the opposite direction from the accepted narrative: in counties that experienced less COVID-19 cases, the incumbent President lost more ground to his opponent, now President Joseph R. Biden Jr. A tenable explanation of this observation is the different attitude of liberal and conservative voters toward the pandemic, which led to more COVID-19 spreading in counties with a larger share of republican voters.

Spontaneous emergence of leadership patterns drives synchronization in complex human networks.

Synchronization of human networks is fundamental in many aspects of human endeavour. Recently, much research effort has been spent on analyzing how motor coordination emerges in human groups (from rocking chairs to violin players) and how it is affected by coupling structure and strength. Here we uncover the spontaneous emergence of leadership (based on physical signaling during group interaction) as a crucial factor steering the occurrence of synchronization in complex human networks where individuals perform a joint motor task. In two experiments engaging participants in an arm movement synchronization task, in the physical world as well as in the digital world, we found that specific patterns of leadership emerged and increased synchronization performance. Precisely, three patterns were found, involving a subtle interaction between phase of the motion and amount of influence. Such patterns were independent of the presence or absence of physical interaction, and persisted across manipulated spatial configurations. Our results shed light on the mechanisms that drive coordination and leadership in human groups, and are consequential for the design of interactions with artificial agents, avatars or robots, where social roles can be determinant for a successful interaction.

The inherent uncertainty of temporal networks is a true challenge for control.

Recently, it has been suggested that network temporality can be exploited to substantially reduce the energy required to control complex networks. This somewhat counterintuitive finding was explained through an evocative example of the advantage of temporal networks: when navigating a sailboat, we raise the sails when the wind helps us while lowering them when it works against us. Unfortunately, controlling complex networks inherits a further analogy with navigating a sailboat: having to face the inherent uncertainty of future winds. We rarely, if ever, have deterministic knowledge of the evolution of the network we want to control. Here, our challenge is to exploit the potential advantages of temporality when only a probabilistic description of the future is available. We prove that, in this more realistic setting, exploiting temporality is no more a panacea for network control, but rather an asset of a wider toolbox made available by the scientific community. One that can indeed turn out useful, provided that the temporality of the network structure matches the intrinsic time scales of the nodes we want to control.

Modeling Frequency Reduction in Human Groups Performing a Joint Oscillatory Task.

In human groups performing oscillatory tasks, it has been observed that the frequency of participants' oscillations reduces when compared to that acquired in solo. This experimental observation is not captured by the standard Kuramoto oscillators, often employed to model human synchronization. In this work, we aim at capturing this observed phenomenon by proposing three alternative modifications of the standard Kuramoto model that are based on three different biologically-relevant hypotheses underlying group synchronization. The three models are tuned, validated and compared against experiments on a group synchronization task, which is a multi-agent extension of the so-called mirror game.

Unequal effects of the national lockdown on mental and social health in Italy.

With the exception of a few countries that chose a different approach, the worldwide reaction to the COVID-19 pandemic was a (longer or shorter) period of national lockdown. While the economic consequences of shutting down national economies were immediately evident, the sociopsychiatric implications of the social confinement of the entire population remain hidden and not fully understood. Italy has been the first European country to be severely impacted by the COVID-19 pandemic, to which it responded through strict lockdown measurements. The results of a timely survey on mental and social health, carried out by students and teachers of a middle school in Rome, might help identify the most vulnerable groups of the population. This evidence could be crucial in conceiving and enacting targeted public health policies to mitigate the consequences of the pandemic on mental health and to prevent intolerance to containment measures in some population segments, which could hamper worldwide efforts in the fight against COVID-19.

Moving in unison after perceptual interruption.

Humans interact in groups through various perception and action channels. The continuity of interaction despite a transient loss of perceptual contact often exists and contributes to goal achievement. Here, we study the dynamics of this continuity, in two experiments involving groups of participants ([Formula: see text]) synchronizing their movements in space and in time. We show that behavioural unison can be maintained after perceptual contact has been lost, for about 7s. Agent similarity and spatial configuration in the group modulated synchronization performance, differently so when perceptual interaction was present or when it was memorized. Modelling these data through a network of oscillators enabled us to clarify the double origin of this memory effect, of individual and social nature. These results shed new light into why humans continue to move in unison after perceptual interruption, and are consequential for a wide variety of applications at work, in art and in sport.

A network model of Italy shows that intermittent regional strategies can alleviate the COVID-19 epidemic.

The COVID-19 epidemic hit Italy particularly hard, yielding the implementation of strict national lockdown rules. Previous modelling studies at the national level overlooked the fact that Italy is divided into administrative regions which can independently oversee their own share of the Italian National Health Service. Here, we show that heterogeneity between regions is essential to understand the spread of the epidemic and to design effective strategies to control the disease. We model Italy as a network of regions and parameterize the model of each region on real data spanning over two months from the initial outbreak. We confirm the effectiveness at the regional level of the national lockdown strategy and propose coordinated regional interventions to prevent future national lockdowns, while avoiding saturation of the regional health systems and mitigating impact on costs. Our study and methodology can be easily extended to other levels of granularity to support policy- and decision-makers.

Using demographics toward efficient data classification in citizen science: a Bayesian approach.

Public participation in scientific activities, often called citizen science, offers a possibility to collect and analyze an unprecedentedly large amount of data. However, diversity of volunteers poses a challenge to obtain accurate information when these data are aggregated. To overcome this problem, we propose a classification algorithm using Bayesian inference that harnesses diversity of volunteers to improve data accuracy. In the algorithm, each volunteer is grouped into a distinct class based on a survey regarding either their level of education or motivation to citizen science. We obtained the behavior of each class through a training set, which was then used as a prior information to estimate performance of new volunteers. By applying this approach to an existing citizen science dataset to classify images into categories, we demonstrate improvement in data accuracy, compared to the traditional majority voting. Our algorithm offers a simple, yet powerful, way to improve data accuracy under limited effort of volunteers by predicting the behavior of a class of individuals, rather than attempting at a granular description of each of them.

Synchronization of complex networks through local adaptive coupling.

Two local adaptive strategies for the synchronization of complex networks are discussed in this paper. One, termed as vertex-based, uses local adaptive coupling gains at each node in the network. The other, named edge-based, associates to each edge in the network an adaptive coupling gain, determined solely on the basis of local information. The global asymptotic stability of the synchronous evolution is proven for both strategies using appropriate Lyapunov-based techniques. The effectiveness of the adaptive methodologies presented in the paper is shown via two representative examples: adaptive consensus and the adaptive synchronization of a network on N coupled Chua's circuits.