HUMAN RIGHTS Evolution in the digital era

This publication contains studies conducted by authors from several European countries that have cooperated with each other for many years in the field of human rights. The fruit of this cooperation are numerous conferences and publications in various languages. What is most important, however, is the exchange of experiences and opinions on understanding and application of individual human rights from the perspective of the experiences of societies living in the European cultural circle, and at the same time functioning in different historical and geographical conditions. This publication is an attempt to look at human rights from the perspective of the dynamic progress that is connected with the development of ICT tools. It is not only about digitization or automation of human work, but above all about creating a virtual society, in which artificial intelligence plays an important role. A significant part of human activity, especially interpersonal communication, takes place with the use of social media. Moreover, individual contact with public authorities are being gradually replaced by intelligent computer programs. In the United States, there is already an IT system, which adjudicates in minor misdemeanor cases. Modern researches in IT sector aim to build programs that allow to support human thinking through recommendation algorithms or suggesting automatically learned solutions, and even aim at autonomous decision-making. This last level of shifting responsibility for decisions to artificial intelligence is assessed extremely positive by many people, but also brings many fears. A virtual society built with the use of artificial intelligence changes the perception of many human rights, such as the right to good name, the right to freely express one’s opinion, the right to property, the right to state or national identity. Hence this publication contains various opinions on the artificial intelligence and its role in the functioning of society and importance for the life of an individual. The added value of this publication is the fact that it contains balanced views and assessments of authors from various European countries and academic societies conducting research on digital reality. This publication will certainly allow the reader to form his or her own opinion on human rights in the context of artificial intelligence.

Effects of COVID-19 pandemic on the psychological states of youth and adult elite male athletes

Objective: Although the psychological states of athletes were negatively affected during the COVID-19 pandemic, there is a lack of studies examining the psychological impact on athletes according to age, sports discipline, and contact requirement of the sport. The aim of this study was to evaluate the psychological effects of the COVID-19 pandemic on elite athletes according to age, sport discipline (team-individual), and contact requirements of the sports (contact and non-contact). Material and methods: Three questionnaires (Depression anxiety stress scale-21, Impact of events scale (IES), International physical activity questionnaire (IPAQ)) were sent to participants’ smartphones, six weeks after professional football leagues were postponed. The online survey was completed by 440 athletes and 126 non-athlete volunteers. Athletes were divided into three groups as follows; team sports, individual contact, and individual non-contact-sports. Also, each group was subdivided into two age groups as youth and adult. Results: Depression, anxiety, and stress scores for the team sports were significantly lower than individual contact-sports (p<0.01 each) and individual non-contact-sports (p=0.02, p<0.01, p<0.01, respectively). IES score for the team sports was significantly lower than the individual contact sports, individual non-contact sports, and non-athlete volunteers (p<0.01, p=0.02, p=0.04, respectively). There was no other significant difference between the scale scores of the groups (athletes-control, or youth-adult) (p>0.05). Conclusion: Mental health status of team sport athletes were relatively protected, whereas, individual contact sports athletes were highly affected. The psychological states of individual contact athletes should be monitored closely, and necessary psychosocial support should be provided to create coping strategies.

Information spreading on metapopulation networks with heterogeneous contacting

Extensive real-data reveals that individuals exhibit heterogeneous contacting frequency in social systems. We propose a mathematical model to investigate the effects of heterogeneous contacting for information spreading in metapopulation networks. In the proposed model, we assume the number of contacting (NOC) distribution follows a specific distribution, including the normal, exponential, and power-law distributions. We utilize the Markov chain method to study the information spreading dynamics and find that mean and variance display no significant effect on the outbreak threshold for all the considered distributions. Under the same values of NOC distribution’s mean and variance, the information prevalence is largest when the distribution of NOC follows the normal distribution and second-largest for the exponential distribution, the smallest for the power-law distribution. When the distribution of NOC obeys the normal distribution, experimental results show that the information prevalence will decrease with individual contact ability heterogeneity. We observe similar phenomena when the distribution of NOC follows a power-law and exponential distribution. Furthermore, a larger mean of individual contact capacity distribution will result in higher information prevalence.

Behavioural interventions for CVD risk reduction for blue-collar workers: a systematic review

ObjectivesIndividuals working in blue-collar occupations experience high rates of cardiovascular disease (CVD). The purpose of this systematic review is to describe the characteristics and efficacy of behavioural interventions that have targeted CVD risk factors in this high-risk group.MethodsFollowing Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, we searched seven databases to find interventions focused on changing the following: blood pressure, cholesterol, diet, physical activity, smoking or weight. Eligible studies tested a behavioural intervention (not exclusively policy, environmental, or pharmaceutical), in individuals working in blue-collar occupations using a randomised study design. Study quality was evaluated using the National Heart, Lung, and Blood Institute’s study quality assessment tool.Results22 studies evaluating 31 interventions were included: 11 were rated as ‘good’ or ‘fair’ quality. Intervention intensity ranged from a single contact via a mailed letter to studies that included individual-level contacts at multiple time points between staff and participants. Studies that included at least some individual contact generally yielded the greatest effects. Interventions had the greatest observed effects on self-report changes in diet, regardless of intervention intensity. Four of the five higher quality studies that explicitly tailored the intervention to the occupational group were successful at reducing at least one risk factor.ConclusionsInterventions that used individual contact and tailored the intervention to the occupational setting yielded the greatest effects on CVD risk-factor reduction in individuals working in blue-collar occupations. Generally, studies were low quality but showed promising effects for reaching this high-risk population. Future work should incorporate these promising findings in higher quality studies.PROSPERO registration numberCRD42019136183.

A Continuous Bayesian Model for the Stimulation COVID-19 Epidemic Dynamics

It is of great theoretical and application value to accurately forecast the spreading dynamics of COVID-19 epidemic. We first proposed and established a Bayesian model to predict the epidemic spreading behavior. In this model, the infection probability matrix is estimated according to the individual contact frequency in certain population group. This infection probability matrix is highly correlated with population geographic distribution, population age structure and so on. This model can effectively avoid the prediction malfunction by using the traditional ordinary differential equation methods such as SIR (susceptible, infectious and recovered) model and so on. Meanwhile, it would forecast the epidemic distribution and predict the epidemic hot spots geographically at different time. According to the results revealed by Bayesian model, the effect of population geographical distribution should be considered in the prediction of epidemic situation, and there is no simple derivation relationship between the threshold of group immunity and the virus reproduction numberR0. If we further consider the virus mutation effect and the antibody attenuation effect, with a large global population spatial distribution, it will be difficult for us to eliminate Covid-19 in a short time even with vaccination endeavor. Covid-19 may exist in human society for a long time, and the epidemic caused by re-infection is characterized by a wild-geometric && low- probability distribution with no epidemic hotspots.

Different Effects of the COVID-19 Pandemic on Exercise Indexes and Mood States Based on Sport Types, Exercise Dependency and Individual Characteristics

Exercise indexes have been affected by the coronavirus disease 2019 (COVID-19) pandemic and its related restrictions among athletes. In the present study, we investigated the exercise frequency and intensity before and during the COVID-19 pandemic, and also current exercise dependency and mood state among non-contact individual, contact individual, and team sports athletes. A total of 1353 athletes from non-contact individual sports athletes (NCISA), contact individual sports athletes (CISA) and team sport athletes (TSA) participated; 45.4% of them were females that completed a series of self-rating questionnaires covering sociodemographic information, former and current exercise patterns, exercise dependency and mood states. NCISA had less exercise frequency than CISA, both before and during the COVID-19 pandemic, and NCISA had less exercise frequency than TSA during the COVID-19 pandemic. Regarding exercise intensity, CISA had higher scores than NCISA and TSA before the COVID-19 pandemic, and CISA had more exercise intensity than TSA during the COVID-19 pandemic. Frequency and intensity were reduced from before to during the COVID-19 pandemic in the three groups, except for TSA intensity. In addition, positive and negative mood states were correlated with exercise dependency. CISA were more discouraged and vigorous than NCISA and TSA, respectively. For NCISA, CISA, and TSA, ordinal regressions separately showed that adherence to quarantine and exercise dependency were better predictors of exercise indexes. Finally, exercise dependency subscales were different among sports, but it was not in exercise dependency itself. Although the decrease in exercise indexes was noticeable, there was no consistent pattern of change in exercise behavior in all sports. Additionally, during the COVID-19 pandemic, negative moods were predominant among all athletes. The results discussed are based on exercise nonparticipating, sport type, and affect regulation hypothesis.

Gray Matter Sampling Differences Between Subdural Electrodes and Stereoelectroencephalography Electrodes

Objective: Stereoelectroencephalography (SEEG) has seen a recent increase in popularity in North America; however, concerns regarding the spatial sampling capabilities of SEEG remain. We aimed to quantify and compare the spatial sampling of subdural electrode (SDE) and SEEG implants.Methods: Patients with drug-resistant epilepsy who underwent invasive monitoring were included in this retrospective case-control study. Ten SEEG cases were compared with ten matched SDE cases based on clinical presentation and pre-implantation hypothesis. To quantify gray matter sampling, MR and CT images were coregistered and a 2.5mm radius sphere was superimposed over the center of each electrode contact. The estimated recording volume of gray matter was defined as the cortical voxels within these spherical models. Paired t-tests were performed to compare volumes and locations of SDE and SEEG recording. A Ripley's K-function analysis was performed to quantify differences in spatial distributions.Results: The average recording volume of gray matter by each individual contact was similar between the two modalities. SEEG implants sampled an average of 20% more total gray matter, consisted of an average of 17% more electrode contacts, and had 77% more of their contacts covering gray matter within sulci. Insular coverage was only achieved with SEEG. SEEG implants generally consist of discrete areas of dense local coverage scattered across the brain; while SDE implants cover relatively contiguous areas with lower density recording.Significance: Average recording volumes per electrode contact are similar for SEEG and SDE, but SEEG may allow for greater overall volumes of recording as more electrodes can be routinely implanted. The primary difference lies in the location and distribution of gray matter than can be sampled. The selection between SEEG and SDE implantation depends on sampling needs of the invasive implant.

Fundamental limitations of case isolation

Case isolation is a strategy with the potential to curb infectious disease epidemics. Expressions for the stability boundary of a case isolation scheme defined through the proportion of the infectious population that it isolates with a given delay have recently been established. Here we quantify how this stability boundary moves when heterogeneity of the inter-individual contact network increases, and explain the underlying mechanism through insightful examples.

Data-driven estimate of SARS-CoV-2 herd immunity threshold in populations with individual contact pattern variations

The development of efficacious vaccines has made it possible to envision mass vaccination programs aimed at suppressing SARS-CoV-2 transmission around the world. Here we use a data-driven age-structured multilayer representation of the population of 34 countries to estimate the disease induced immunity threshold, accounting for the contact variability across individuals. We show that the herd immunization threshold of random (un-prioritized) mass vaccination programs is generally larger than the disease induced immunity threshold. We use the model to test two additional vaccine prioritization strategies, transmission-focused and age-based, in which individuals are inoculated either according to their behavior (number of contacts) or infection fatality risk, respectively. Our results show that in the case of a sterilizing vaccine the behavioral strategy achieves herd-immunity at a coverage comparable to the disease-induced immunity threshold, but it appears to have inferior performance in averting deaths than the risk vaccination strategy. The presented results have potential use in defining the effects that the heterogeneity of social mixing and contact patterns has on herd immunity levels and the deployment of vaccine prioritization strategies.

Vaccine Prioritisation Using Bluetooth Exposure Notification Apps

After vaccinating health care workers and vulnerable groups against COVID-19, authorities will need to decide how to vaccinate everyone else. Prioritising individuals with more contacts can be disproportionately effective, in theory, but identifying these individuals is difficult. Here we show that the technology underlying Bluetooth exposure notification applications, such as used for digital contact tracing, can be leveraged to prioritise vaccination based on individual contact data. Our approach is based on the insight that these apps also act as local sensing devices measuring each user's total exposure time to other users, thereby enabling the implementation of a previously impossible strategy that prioritises potential super-spreaders. Furthermore, by generalising percolation theory and introducing a novel measure of vaccination efficiency, we demonstrate that this ``hot-spotting" strategy can achieve herd immunity with up to half as many vaccines as a non-targeted strategy, and is attractive even for relatively low rates of app usage.