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The fans' importance in sports is acknowledged by the term 'the 12th man', a figurative extra player for the home team. Sport teams are indeed more successful when they play in front of their fans than when they play away. The supposed mechanism behind this phenomenon, termed Home Advantage (HA), is that fans' support spurs home players to better performance and biases referees, which in turn determines the outcome. The inference about the importance of fans' support is, however, indirect as there is normally a 12th man of this kind, even if it is an opponent's. The current pandemic, which forced sporting activities to take place behind closed doors, provides the necessary control condition. Here we employ a novel conceptual HA model on a sample of over 4000 soccer matches from 12 European leagues, some played in front of spectators and some in empty stadia, to demonstrate that fans are indeed responsible for the HA. However, the absence of fans reduces the HA by a third, as the home team's performance suffers and the officials' bias disappears. The current pandemic reveals that the figurative 12th man is no mere fan hyperbole, but is in fact the most important player in the home team., (© 2021. The Author(s).)

The process of implementation of the UN Sustainable Development Goals (SDGs) which were approved by the UN General Assembly in 2015 has not been simple, being influenced by variety of social, economic, and logistical problems. It has also been negatively affected by the COVID-19 pandemic. There are to date no specific studies aimed at assessing the extent to which higher universities institutions in Europe are active in the SDGs implementation process. Departing from this research need, this paper reports on a study aimed at examining the current degree of engagement of European universities in the implementing the SDGs. By using a multi-methods approach, which entails a review of existing documents, a survey involving participants from 22 countries and case studies, the paper maps, documents and disseminates examples of what European universities are doing to implement the SDGs, the challenges they face, and the solutions being deployed to overcome them., (© 2024. The Author(s).)

Air transport has been identified as one of the primary means whereby COVID-19 spread throughout Europe during the early stages of the pandemic. In this paper we analyse two categories of methods - dynamic network markers (DNMs) and network analysis-based methods - as potential early warning signals for detecting and anticipating COVID-19 outbreaks in Europe on the basis of accuracy regarding the daily confirmed cases. The analysis was carried out from 15 February 2020, around two weeks before the first COVID-19 cases appeared in Europe, and 1 May 2020, approximately two weeks after all the air traffic in Europe had been shut down. Daily European COVID-19 information sourced from the World Health Organization was used, whereas air traffic data from Flightradar24 has been incorporated into the analyses by means of four alternative adjacency matrices. Some DNMs have been discarded since they output multiple time series, which makes it very difficult to interpret their results. The only DNM outputting a single time series does not emulate the COVID-19 trend: it does not detect all the main peaks, which means that peak heights do not match up with the increase in the number of infected people. However, many combinations of network analysis based methods and adjacency matrices output good results (with high accuracy and 20-day advance forecasts), with only minor differences from one to another. The number of edges and the network density methods are slightly better when dynamic flight frequency information is used., (© 2023. Springer Nature Limited.)

Using a unique harmonized real-time data set from the COME-HERE longitudinal survey that covers five European countries (France, Germany, Italy, Spain, and Sweden) and applying a non-parametric machine learning model, this paper identifies the main individual and macro-level predictors of self-protecting behaviors against the coronavirus disease 2019 (COVID-19) during the first wave of the pandemic. Exploiting the interpretability of a Random Forest algorithm via Shapely values, we find that a higher regional incidence of COVID-19 triggers higher levels of self-protective behavior, as does a stricter government policy response. The level of individual knowledge about the pandemic, confidence in institutions, and population density also ranks high among the factors that predict self-protecting behaviors. We also identify a steep socioeconomic gradient with lower levels of self-protecting behaviors being associated with lower income and poor housing conditions. Among socio-demographic factors, gender, marital status, age, and region of residence are the main determinants of self-protective measures., (© 2023. The Author(s).)

This paper investigates the role of large outbreaks on the persistence of Covid-19 over time. Using data from 650 European regions in 14 countries, I first show that winter school holidays in late February/early March 2020 (weeks 8, 9 and 10) led to large regional outbreaks of Covid-19 in the spring with the spread being 60% and up-to over 90% higher compared to regions with earlier school holidays. While the impact of these initial large outbreaks fades away over the summer months, it systematically reappears from the fall as regions with school holidays in weeks 8, 9 and 10 had 30–70% higher spread. This suggests that following a large outbreak, there is a strong element of underlying (latent) regional persistence of Covid-19. The strong degree of persistence highlights the long-term benefits of effective (initial) containment policies, as once a large outbreak has occurred, Covid-19 persists. This result emphasizes the need for vaccinations against Covid-19 in regions that have recently experienced large outbreaks but are well below herd-immunity, to avoid a new surge of cases. [ABSTRACT FROM AUTHOR]

Although globalization has left its mark on economic dynamism, causing conditionalities among various aspects (market openness, production networks, technological and information developments, migratory flows, international cooperation, humanitarian support, etc.), the less pleasant side of it should not be omitted, i.e. the emergence of the framework for the faster diffusion of epidemiological diseases. Thus, with the onset of the SARS-CoV-2 virus, its widespread circulation is a serious challenge for the provision of efficient solutions to combat it, especially in countries with fragile health systems, poor institutional quality and lack of resources. In this paper we aim to investigate the implications of globalization on the COVID-19 vaccination of the population. The period under analysis is January 1, 2021-January 1, 2022, using montly data, and the object of our study are 48 European states. To capture the relationship between globalization and the vaccination rate, we applied regression models, including a number of factors that may influence the progress of vaccination. In order to test the robustness of the results, the two-stage least squares (2SLS) regressions was used. The regression models developed underlined that globalization impacts the degree of vaccination. More globalized economies are more competitive in COVID-19 management, and the significance of this effect comes from better interconnection in global markets and easier access to medical discoveries. At the same time, countries with a higher vaccination rate are associated with higher levels of development. Based on the results obtained, we proposed some policy recommendations to increase the propensity to vaccinate, ensure equity in the distribution of vaccines and provide financial support to developing countries., (© 2022. The Author(s).)

A better understanding of various patterns in the coronavirus disease 2019 (COVID-19) spread in different parts of the world is crucial to its prevention and control. Motivated by the previously developed Global Epidemic and Mobility (GLEaM) model, this paper proposes a new stochastic dynamic model to depict the evolution of COVID-19. The model allows spatial and temporal heterogeneity of transmission parameters and involves transportation between regions. Based on the proposed model, this paper also designs a two-step procedure for parameter inference, which utilizes the correlation between regions through a prior distribution that imposes graph Laplacian regularization on transmission parameters. Experiments on simulated data and real-world data in China and Europe indicate that the proposed model achieves higher accuracy in predicting the newly confirmed cases than baseline models., (© 2022. The Author(s).)

Comparison of COVID-19 trends in space and over time is essential to monitor the pandemic and to indirectly evaluate non-pharmacological policies aimed at reducing the burden of disease. Given the specific age- and sex- distribution of COVID-19 mortality, the underlying sex- and age-distribution of populations need to be accounted for. The aim of this paper is to present a method for monitoring trends of COVID-19 using adjusted mortality trend ratios (AMTRs). Age- and sex-mortality distribution of a reference European population (N = 14,086) was used to calculate age- and sex-specific mortality rates. These were applied to each country to calculate the expected deaths. Adjusted Mortality Trend Ratios (AMTRs) with 95% confidence intervals (C.I.) were calculated for selected European countries on a daily basis from 17th March 2020 to 29th April 2021 by dividing observed cumulative mortality, by expected mortality, times the crude mortality of the reference population. These estimated the sex- and age-adjusted mortality for COVID-19 per million population in each country. United Kingdom experienced the highest number of COVID-19 related death in Europe. Crude mortality rates were highest Hungary, Czech Republic, and Luxembourg. Accounting for the age-and sex-distribution of the underlying populations with AMTRs for each European country, four different patterns were identified: countries which experienced a two-wave pandemic, countries with almost undetectable first wave, but with either a fast or a slow increase of mortality during the second wave; countries with consistently low rates throughout the period. AMTRs were highest in Eastern European countries (Hungary, Czech Republic, Slovakia, and Poland). Our methods allow a fair comparison of mortality in space and over time. These might be of use to indirectly estimating the efficacy of non-pharmacological health policies. The authors urge the World Health Organisation, given the absence of age and sex-specific mortality data for direct standardisation, to adopt this method to estimate the comparative mortality from COVID-19 pandemic worldwide., (© 2021. The Author(s).)

Mobility restrictions have been identified as key non-pharmaceutical interventions to limit the spread of the SARS-COV-2 epidemics. However, these interventions present significant drawbacks to the social fabric and negative outcomes for the real economy. In this paper we propose a real-time monitoring framework for tracking the economic consequences of various forms of mobility reductions involving European countries. We adopt a granular representation of mobility patterns during both the first and second waves of SARS-COV-2 in Italy, Germany, France and Spain to provide an analytical characterization of the rate of losses of industrial production by means of a nowcasting methodology. Our approach exploits the information encoded in massive datasets of human mobility provided by Facebook and Google, which are published at higher frequencies than the target economic variables, in order to obtain an early estimate before the official data becomes available. Our results show, in first place, the ability of mobility-related policies to induce a contraction of mobility patterns across jurisdictions. Besides this contraction, we observe a substitution effect which increases mobility within jurisdictions. Secondly, we show how industrial production strictly follows the dynamics of population commuting patterns and of human mobility trends, which thus provide information on the day-by-day variations in countries' economic activities. Our work, besides shedding light on how policy interventions targeted to induce a mobility contraction impact the real economy, constitutes a practical toolbox for helping governments to design appropriate and balanced policy actions aimed at containing the SARS-COV-2 spread, while mitigating the detrimental effect on the economy. Our study reveals how complex mobility patterns can have unequal consequences to economic losses across countries and call for a more tailored implementation of restrictions to balance the containment of contagion with the need to sustain economic activities.

A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/, ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the difference between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34. [ABSTRACT FROM AUTHOR]

This article examines the main factors affecting COVID-19 lethality across 16 European Countries with a focus on the role of health system characteristics during the first phase of the diffusion of the virus. Specifically, we investigate the leading causes of lethality at 10, 20, 30, 40 days in the first hit of the pandemic. Using a random forest regression (ML), with lethality as outcome variable, we show that the percentage of people older than 65 years (with two or more chronic diseases) is the main predictor variable of lethality by COVID-19, followed by the number of hospital intensive care unit beds, investments in healthcare spending compared to GDP, number of nurses and doctors. Moreover, the variable of general practitioners has little but significant predicting quality. These findings contribute to provide evidence for the prediction of lethality caused by COVID-19 in Europe and open the discussion on health policy and management of health care and ICU beds during a severe epidemic. [ABSTRACT FROM AUTHOR]

A recent mathematical model has suggested that staying at home did not play a dominant role in reducing COVID-19 transmission. The second wave of cases in Europe, in regions that were considered as COVID-19 controlled, may raise some concerns. Our objective was to assess the association between staying at home (%) and the reduction/increase in the number of deaths due to COVID-19 in several regions in the world. In this ecological study, data from www.google.com/covid19/mobility/, ourworldindata.org and covid.saude.gov.br were combined. Countries with > 100 deaths and with a Healthcare Access and Quality Index of ≥ 67 were included. Data were preprocessed and analyzed using the difference between number of deaths/million between 2 regions and the difference between the percentage of staying at home. The analysis was performed using linear regression with special attention to residual analysis. After preprocessing the data, 87 regions around the world were included, yielding 3741 pairwise comparisons for linear regression analysis. Only 63 (1.6%) comparisons were significant. With our results, we were not able to explain if COVID-19 mortality is reduced by staying at home in ~ 98% of the comparisons after epidemiological weeks 9 to 34. [ABSTRACT FROM AUTHOR]

In Europe, multiple waves of infections with SARS-CoV-2 (COVID-19) have been observed. Here, we have investigated whether common patterns of cytokines could be detected in individuals with mild and severe forms of COVID-19 in two pandemic waves, and whether machine learning approach could be useful to identify the best predictors. An increasing trend of multiple cytokines was observed in patients with mild or severe/critical symptoms of COVID-19, compared with healthy volunteers. Linear Discriminant Analysis (LDA) clearly recognized the three groups based on cytokine patterns. Classification and Regression Tree (CART) further indicated that IL-6 discriminated controls and COVID-19 patients, whilst IL-8 defined disease severity. During the second wave of pandemics, a less intense cytokine storm was observed, as compared with the first. IL-6 was the most robust predictor of infection and discriminated moderate COVID-19 patients from healthy controls, regardless of epidemic peak curve. Thus, serum cytokine patterns provide biomarkers useful for COVID-19 diagnosis and prognosis. Further definition of individual cytokines may allow to envision novel therapeutic options and pave the way to set up innovative diagnostic tools. [ABSTRACT FROM AUTHOR]