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Radionuclide contamination is a serious health issue caused by nuclear experiments and plant accidents, as seen for the Chernobyl and Fukushima nuclear plants. Italy has been especially interested in northwestern alpine regions, as have several other nations. The aim of this work was to indagate 134Cs and 137Cs contamination in wild boars, which were considered bioindicators sampled in the Chisone/Germanasca Valley and the Pellice Valley districts (Piedmont, Italy) in two hunting seasons (2014 and 2016). In the 2014 season, only the livers of the animals (n = 48) were sampled, whereas in 2016, five different anatomical sampling sites were sampled for each animal (n = 16). The analyses were conducted in an accredited laboratory (Agenzia Regionale per la Protezione dell'Ambiente–ARPA) by the aid of an HPGe detector (Ortec) with a relative efficiency of 50%. In general, the contamination levels registered in 2014 were under the detection limit for 134Cs and low for 137Cs (Chisone/Germanasca valley: min: 0.0, max: 23.9 median 11.0 Bq/kg vs Pellice valley: min 0, max: 31.7, median: 9.6 Bq/kg) and no health concern can be supposed. In the first-year samples, the liver showed a negative correlation between age and contamination level. In the second year of sampling, low levels were confirmed (min: 3.1 Bq/kg, max: 113.3; median 17.7 Bq/kg). Multiple sampling from the same animal showed that the diaphragm (median = 27.7 Bq/kg) kidney (27.4) and tongue (27.6) were more contaminated than the liver (17.7) and spleen (15.3). Moreover, a linear mixed model revealed a negative organ-by-age interaction, meaning that interorgan differences in contamination level were greater in younger (5–11 months) than in older (18–36 months) animals. Different feeding habits can be the explanation. Our paper shows that muscle sites (diaphragm and tongue) can be useful for radionuclide pollution surveillance in wild boar populations and that younger animals show more interorgan variability in contamination levels than older animals. More investigations are needed to confirm this correlation and to fulfill the request for more data to achieve better risk assessment. [ABSTRACT FROM AUTHOR]

Understanding lay theories on the causes of economic inequality is the first step to comprehending why people tolerate, justify, or react against it. Accordingly, this paper aims to develop and validate with two cross-sectional studies the Attributions for Cross-Country Inequality Scale (ACIS), which assesses how people explain cross-country economic inequality–namely, the uneven distribution of income and wealth between poor and rich countries. After selecting and adapting items from existing scales of attributions for poverty and wealth, in Study 1, we tested the factorial structure of this initial pool of items in three countries with different levels of economic development and inequality, namely, Italy (n = 246), the UK (n = 248), and South Africa (n = 228). Three causal dimensions emerged from the Exploratory Factor Analysis: "rich countries" (blaming the systematic advantage of and exploitation by rich countries), "poor countries" (blaming the dispositional inadequacy and faults of poor countries), and "fate" (blaming destiny and luck). The retained items were administered in Study 2 to three new samples from Italy (n = 239), the UK (n = 249), and South Africa (n = 248). Confirmatory Factor Analysis (CFA) corroborated the factorial structure of the ACIS, and Multi-Group CFA supported configural and metric invariances of the scale across countries. In addition, we show internal consistency and construct validity of the scale: the scale correlates with relevant constructs (e.g., beliefs about cross-country inequality and ideological orientation) and attitudes toward relevant policies related to international redistribution and migration. Overall, the scale is a valid instrument to assess causal attribution for cross-national inequality and is reliable across countries. By focusing on resource distribution from an international perspective, this scale will allow researchers to broaden the discussion on economic inequality to a global level. [ABSTRACT FROM AUTHOR]

When the COVID-19 pandemic first emerged in early 2020, healthcare and bureaucratic systems worldwide were caught off guard and largely unprepared to deal with the scale and severity of the outbreak. In Italy, this led to a severe underreporting of infections during the first wave of the spread. The lack of accurate data is critical as it hampers the retrospective assessment of nonpharmacological interventions, the comparison with the following waves, and the estimation and validation of epidemiological models. In particular, during the first wave, reported cases of new infections were strikingly low if compared with their effects in terms of deaths, hospitalizations and intensive care admissions. In this paper, we observe that the hospital admissions during the second wave were very well explained by the convolution of the reported daily infections with an exponential kernel. By formulating the estimation of the actual infections during the first wave as an inverse problem, its solution by a regularization approach is proposed and validated. In this way, it was possible to compute corrected time series of daily infections for each age class. The new estimates are consistent with the serological survey published in June 2020 by the National Institute of Statistics (ISTAT) and can be used to speculate on the total number of infections occurring in Italy during 2020, which appears to be about double the number officially recorded. [ABSTRACT FROM AUTHOR]

Covid-19 outbreak led all organizations to reorganize their processes to prevent infection and contagion risk. All healthcare facilities, included penitentiary mental health services, had to redesign their processes to safely deliver care services. In this paper, the case of a Penitentiary Mental Health Division located in southern Italy is presented. Soft System Methodology and Business process management principles and techniques are adopted to analyse and redesign the detainees' mental health care process. The process, characterized by direct, close and prolonged contact with patients, exposes detainees and healthcare staff to a high Covid-19 infection risk. Through document analysis, interviews with the actors involved in the process and direct observation, the process's inefficiencies and criticalities are identified. The process is redesigned to make it compliant with Covid-19 prevention provisions and national penitentiary regulations and address the other criticalities. The proposed methodological approach–which innovatively combines Soft System Methodology and Business Process Management–constitutes a human-centered process-based redesign approach that can be used both in healthcare and other organizational settings. [ABSTRACT FROM AUTHOR]

Prospect Theory, proposed and developed by Kahneman and Tversky, demonstrated that people do not make rational decisions based on expected utility, but are instead biased by specific cognitive tendencies leading to neglect, under- or over- consider information, depending on the context of presentation. In this vein, the present paper focuses on whether and how individual decision-making attitudes are prone to change in the presence of globally challenging events. We ran three partial replications of the Kahneman and Tversky (1979) paper, focusing on a set of eight prospects, after a terror attack (Paris, November 2015, 134 subjects) and during the Covid-19 pandemic, both during the first lockdown in Italy (Spring 2020, 176 subjects) and after the first reopening (140 subjects). The results confirm patterns of choice characterizing uncertain times, as shown by previous literature. In particular, we note significant increase of risk aversion, both in the gain and in the loss domains, that consistently emerged in the three replications. Given the nature of our sample, and the heterogeneity between the three periods investigated, we suggest that the phenomenon we present can be explained stress-related effects on decision making rather than by other economic effects, such as the income effect. [ABSTRACT FROM AUTHOR]

During the COVID-19 pandemic, science has been prominently featured in institutional communication and political agendas as never before. Governments substantially relied on scientific experts to analyze pandemic trends, develop anti-COVID-19 vaccines and adopt containment strategies. In this paper, we analyze speeches by three political leaders–Boris Johnson (Prime Minister, UK), Sergio Mattarella (President of the Republic, Italy), and Ursula von der Leyen (President, European Commission)–between February 20, 2020, and February 20, 2022, to identify how science was addressed and framed. The results of the quantitative and qualitative exploration of the speeches highlight three main ways in which political leaders view science: a national pride narrative–i.e., science as an instrument and indicator of national pride and international standing of the country; an ethical narrative–i.e., science as an agent of social growth; an integration narrative–i.e., science as a driving force of both European integration and stronger collaboration between knowledge production and industry. The predominant narrative varies in relation to the political leaders' different institutional contexts and roles. [ABSTRACT FROM AUTHOR]

Long bone breakage for bone marrow recovery is a commonly observed practice in Middle Palaeolithic contexts, regardless of the climatic conditions. While lithic technology is largely used to define cultural patterns in human groups, despite dedicating research by zooarchaeologists, for now butchering techniques rarely allowed the identification of clear traditions, notably for ancient Palaeolithic periods. In this paper, we test the hypothesis of butchery traditions among Neandertal groupsusing the bone assemblages from three sites in southwestern Europe. These sites are located in southeastern France and northern Italy and are dated to the Late Middle Palaeolithic: Abri du Maras (Marine Isotopic Stages (MIS) 4–3, Ardèche), Saint-Marcel (MIS 3, Ardèche), and Riparo Tagliente (MIS 4–3, Verona). The detection of culturally-induced patterns of bone breakage involves differentiating them from intuitively generated patterns. To tackle this issue, we used a zooarchaeological approach focusing on the percussion marks produced during the bone breakage process. Statistical analyses as the chi-square test of independence were employed to verify if percussion mark locations were randomly distributed, and if these distributions were different from the intuitive ones. For femurs and humeri, our results demonstrate that Neandertal groups occupying the Abri du Maras (levels 4.1 and 4.2) and the Saint-Marcel Cave (levels g and h) sites in France applied butchery traditions to recover yellow marrow. However, the traditions developed at each site were different. On the contrary, in Riparo Tagliente, in Italy, several groups or individuals of a same group did not share the same butchery traditions over time. Regarding the Abri du Maras and Saint Marcel Cave assemblages, our research demonstrates that Neandertal groups applied intense standardized bone breakage, far from the intuitive practice observed experimentally and related to bone density and/or skeletal morphology. These standardized patterns, which are systematic and counter-intuitive, can be interpreted as culturally induced for the Abri du Maras and Saint Marcel Cave. The diversity of Neandertal traditions should be considered by taking into account the butchery, in particular the practice of bone marrow extraction, and not only technological behaviours and types of tool kits. [ABSTRACT FROM AUTHOR]

MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated. [ABSTRACT FROM AUTHOR]

The last year of Covid-19 pandemic has been characterized by the continuous chase between the vaccination campaign and the appearance of new variants that puts further obstacles to the possibility of eradicating the virus and returning to normality in a short period. In the present paper we develop a deterministic compartmental model to describe the evolution of the Covid-19 in Italy as a combined effect of vaccination campaign, new variant spreading and mobility restrictions. Particular attention is given to the mechanism of waning immunity, appropriately timed with respect to the effective progress of the vaccination campaign in Italy. We perform a retrospective analysis in order to explore the role that different mechanisms, such as behavioral changes, variation of the population mobility, seasonal variability of the virus infectivity, and spreading of new variants have had in shaping the epidemiological curve. We find that, in the large time window considered, the most relevant mechanism is the seasonal variation in the stability of the virus, followed by the awareness mechanism, that induces individuals to increase/relax self-protective measures when the number of active cases increases/decreases. The appearance of the Delta variant and the mobility variations have had instead only marginal effects. In absence of vaccines the emerging scenario would have been dramatic with a percentage difference in the number of total infections and total deaths, in both cases, larger than fifty per cent. The model also predicts the appearance of a more contagious variant (the Omicron variant) and its becoming dominant in January 2022. [ABSTRACT FROM AUTHOR]

COVID-19 has got us to face a new situation where, for the lack of ready-to-use vaccines, it is necessary to support vaccination with complex non-pharmaceutical strategies. In this paper, we provide a novel Mixed Integer Nonlinear Programming formulation for fine-grained optimal intervention planning (i.e., at the level of the single day) against newborn epidemics like COVID-19, where a modified SIR model accounting for heterogeneous population classes, social distancing and several types of vaccines (each with its efficacy and delayed effects), allows us to plan an optimal mixed strategy (both pharmaceutical and non-pharmaceutical) that takes into account both the vaccine availability in limited batches at selected time instants and the need for second doses while keeping hospitalizations and intensive care occupancy below a threshold and requiring that new infections die out at the end of the planning horizon. In order to show the effectiveness of the proposed formulation, we analyze a case study for Italy with realistic parameters. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been characterized by a social media "infodemic": an overabundance of information whose authenticity may not always be guaranteed. With the potential to lead individuals to harmful decisions for the society, this infodemic represents a severe threat to information security, public health and democracy. In this paper, we assess the interplay between the infodemic and specific aspects of the pandemic, such as the number of cases, the strictness of containment measures, and the news media coverage. We perform a comparative study on three countries that employed different managements of the COVID-19 pandemic in 2020—namely Italy, the United Kingdom, and New Zealand. We first analyze the three countries from an epidemiological perspective to characterize the impact of the pandemic and the strictness of the restrictions adopted. Then, we collect a total of 6 million posts from Facebook to describe user news consumption behaviors with respect to the reliability of such posts. Finally, we quantify the relationship between the number of posts published in each of the three countries and the number of confirmed cases, the strictness of the restrictions adopted, and the online news media coverage about the pandemic. Our results show that posts referring to reliable sources are consistently predominant in the news circulation, and that users engage more with reliable posts rather than with posts referring to questionable sources. Furthermore, our modelling results suggest that factors related to the epidemiological and informational ecosystems can serve as proxies to assess the evolution of the infodemic. [ABSTRACT FROM AUTHOR]

Introduction: REsilience and Activities for every DaY (READY) is an Acceptance and Commitment Therapy-based group resilience-training program that has preliminary empirical support in promoting quality of life and other psychosocial outcomes in people with multiple sclerosis (PwMS). Consistent with the Medical Research Council framework for developing and evaluating complex interventions, we conducted a pilot randomized controlled trial (RCT), followed by a phase III RCT. The present paper describes the phase III RCT protocol. Methods and analysis: This is a multi-centre cluster RCT comparing READY with a group relaxation program (1:1 ratio) in 240 PwMS from eight centres in Italy (trial registration: isrctn.org Identifier: ISRCTN67194859). Both interventions are composed of 7 weekly sessions plus a booster session five weeks later. Resilience (primary outcome), mood, health-related quality of life, well-being and psychological flexibility will be assessed at baseline, after the booster session, and at three and six month follow-ups. If face-to-face group meetings are interrupted because of COVID-19 related-issues, participants will be invited to complete their intervention via teleconferencing. Relevant COVID-19 information will be collected and the COVID-19 Peritraumatic Distress scale will be administered (ancillary study) at baseline and 3-month follow-up. Analysis will be by intention-to-treat to show superiority of READY over relaxation. Longitudinal changes will be compared between the two arms using repeated-measures, hierarchical generalized linear mixed models. Conclusion: It is expected that his study will contribute to the body of evidence on the efficacy and effectiveness of READY by comparing it with an active group intervention in frontline MS rehabilitation and clinical settings. Results will be disseminated in peer-reviewed journals and at other relevant conferences. [ABSTRACT FROM AUTHOR]

Introduction: This paper describes how mortality among hospitalised COVID-19 patients changed during the first three waves of the epidemic in Italy. Methods: This prospective cohort study used the Kaplan-Meier method to analyse the time-dependent probability of death of all of the patients admitted to a COVID-19 referral centre in Milan, Italy, during the three consecutive periods of: 21 February-31 July 2020 (first wave, W1), 1 August 2020–31 January 2021 (second wave, W2), and 1 February-30 April 2021 (third wave, W3). Cox models were used to examine the association between death and the period of admission after adjusting for age, biological sex, the time from symptom onset to admission, disease severity upon admission, obesity, and the comorbidity burden. Results: Of the 2,023 COVID-19 patients admitted to our hospital during the study period, 553 (27.3%) were admitted during W1, 838 (41.5%) during W2, and 632 (31.2%) during W3. The crude mortality rate during W1, W2 and W3 was respectively 21.3%, 23.7% and 15.8%. After adjusting for potential confounders, hospitalisation during W2 or W3 was independently associated with a significantly lower risk of death than hospitalisation during W1 (adjusted hazard ratios [AHRs]: 0.75, 95% confidence interval [CI] 0.59–0.95, and 0.58, 95% CI 0.44–0.77). Among the patients aged >75 years, there was no significant difference in the probability of death during the three waves (AHRs during W2 and W3 vs W1: 0.93, 95% CI 0.65–1.33, and 0.88, 95% CI 0.59–1.32), whereas those presenting with critical disease during W2 and W3 were at significantly lower risk of dying than those admitted during W1 (AHRs 0.61, 95% CI 0.43–0.88, and 0.44, 95% CI 0.28–0.70). Conclusions: Hospitalisation during W2 and W3 was associated with a reduced risk of COVID-19 death in comparison with W1, but there was no difference in survival probability in patients aged >75 years. [ABSTRACT FROM AUTHOR]

This paper investigates gender issues in Ph.D. entrepreneurship. The empirical analysis is based on data from a questionnaire survey run in 2014–15 in Italy. We analyse how Ph.D. students perceive the institutional entrepreneurial environment, the drivers and the factors hindering entrepreneurship and gender-equality among faculties at the parent institution. We find evidence of a gender bias in Ph.D. entrepreneurship and that the perception about the factors either hampering or supporting entrepreneurship is deeply different between sexes. The academic environment can have a fundamental impact on students' decisions to start new ventures and on the probability that they will abandon their entrepreneurial intentions. Female student entrepreneurs particularly benefit from the opportunity to engage with a gender-balanced work environment. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic is bringing disruptive effects on the healthcare systems, economy and social life of countries all over the world. Even though the elder portion of the population is the most severely affected by the COVID-19 disease, the counter-measures introduced so far by governments took into little account the age structure, with restrictions that act uniformly on the population irrespectively of age. In this paper, we introduce a SIRD model with age classes for studying the impact on the epidemic evolution of lockdown policies applied heterogeneously on the different age groups of the population. The proposed model is then applied to age-stratified COVID-19 Italian data. The simulation results suggest that control measures focused to specific age groups may bring benefits in terms of reduction of the overall mortality rate. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 epidemic in Italy has severely affected people aged more than 80, especially socially isolated. Aim of this paper is to assess whether a social and health program reduced mortality associated to the epidemic. Methods: An observational retrospective cohort analysis of deaths recorded among >80 years in three Italian cities has been carried out to compare death rate of the general population and "Long Live the Elderly!" (LLE) program. Parametric and non-parametric tests have been performed to assess differences of means between the two populations. A multivariable analysis to assess the impact of covariates on weekly mortality has been carried out by setting up a linear mixed model. Results: The total number of services delivered to the LLE population (including phone calls and home visits) was 34,528, 1 every 20 day per person on average, one every 15 days during March and April. From January to April 2019, the same population received one service every 41 days on average, without differences between January-February and March-April. The January-April 2020 cumulative crude death rate was 34.8‰ (9,718 deaths out of 279,249 individuals; CI95%: 34.1–35.5) and 28.9‰ (166 deaths out of 5,727 individuals; CI95%:24.7–33.7) for the general population and the LLE sample respectively. The general population weekly death rate increased after the 11th calendar week that was not the case among the LLE program participants (p<0.001). The Standardized Mortality Ratio was 0.83; (CI95%: 0.71–0.97). Mortality adjusted for age, gender, COVID-19 weekly incidence and prevalence of people living in nursing homes was lower in the LLE program than in the general population (p<0.001). Conclusions: LLE program is likely to limit mortality associated with COVID-19. Further studies are needed to establish whether it is due to the impact of social care that allows a better clients' adherence to the recommendations of physical distancing or to an improved surveillance of older adults that prevents negative outcomes associated with COVID-19. [ABSTRACT FROM AUTHOR]

The global impact of coronavirus disease 2019 (COVID-19) is unprecedented, and many control and prevention measures have been implemented to test for and trace COVID-19. However, invisible-spreaders, who are associated with nucleic acid detection and asymptomatic infections, have received insufficient attention in the current COVID-19 control efforts. In this paper, we analyze the time series infection data for Italy, Germany, Brazil, India and Sweden since the first wave outbreak to address the following issues through a series of experiments. We conclude that: 1) As of June 1, 2020, the proportion of invisible-spreaders is close to 0.4% in Sweden, 0.8% in early Italy and Germany, and 0.4% in the middle and late stages. However, in Brazil and India, the proportion still shows a gradual upward trend; 2) During the spread of this pandemic, even a slight increase in the proportion of invisible-spreaders could have large implications for the health of the community; and 3) On resuming work, the pandemic intervention measures will be relaxed, and invisible-spreaders will cause a new round of outbreaks. [ABSTRACT FROM AUTHOR]

The Covid-19 pandemic has highlighted the importance of citizens' behaviors in the containment of the virus. Individuals might change their intention to adhere to public health prescriptions depending on various personal characteristics, including their own emotional status, which has been recognized to be a crucial psychological factor in orienting people's adherence to public health recommendation during emergency settings. In particular, it is crucial to support citizens' alliance with authorities and feeling of trust: public engagement is a concept that refers to the general involvement of citizens into public affairs which is generally considered an effective approach to enhance citizens' understanding of their crucial role in public affairs. However, so far there is no agreement on the metrics and indexes that should be used to measures public engagement during a health crisis. The aim of this paper is to validate a psychometric scale (PHEs-E), which intends to measure the readiness of individuals to adhere to the prescribed behavioral change to contain the emergency. Data were collected throughout the pandemic in Italy: in particular, five independent samples were recruited starting from March 2020 to March 2021. Results showed that the proposed measure has good psychometric characteristics. A general linear model was computed to assess the differences of public engagement across the different data points and among citizens with different sociodemographic characteristics. Correlations with other psychological constructs (i.e. Anxiety, Depression and Self-Efficacy) were also tested, showing that more engaged citizens have a lower level of anxiety and depression, and a higher self-efficacy. This study's findings indicate that individuals' characteristics may differentiate citizens' motivation to engage in public health behavioral recommendation to prevent the COVID-19 contagion. However the scale could be useful to perform a psychological monitoring of psychological readiness to engage in public health strategies to face critical events and settings. [ABSTRACT FROM AUTHOR]

Cremation 168 from the second half of the 8th century BCE (Pithekoussai's necropolis, Ischia Island, Italy), better known as the Tomb of Nestor's Cup, is widely considered as one of the most intriguing discoveries in the Mediterranean Pre-Classic archaeology. A drinking cup, from which the Tomb's name derives, bears one of the earliest surviving examples of written Greek, representing the oldest Homeric poetry ever recovered. According to previous osteological analyses, the Cup is associated with the cremated remains of a juvenile, aged approximately 10–14 years at death. Since then, a vast body of literature has attempted to explain the unique association between the exceptionality of the grave good complex, the symposiac and erotic evocation of the Nestor's Cup inscription with the young age of the individual buried with it. This paper reconsiders previous assessments of the remains by combining gross morphology with qualitative histology and histomorphometric analyses of the burnt bone fragments. This work reveals the commingled nature of the bone assemblage, identifying for the first time, more than one human individual mixed with faunal remains. These outcomes dramatically change previous reconstructions of the cremation deposit, rewriting the answer to the question: who was buried with Nestor's Cup?. [ABSTRACT FROM AUTHOR]

Scientific evidence plays an important role in the therapeutic decision-making process. What happens when physicians are forced to make therapeutic decisions under uncertainty? The absence of scientific guidelines at the beginning of a pandemic due to an unknown virus, such as COVID-19, could influence the perceived legitimacy of the application of non-evidence-based therapeutic approaches. This paper reports on a test of this hypothesis, in which we administered an ad hoc questionnaire to a sample of 64 Italian physicians during the first wave of the COVID-19 pandemic in Italy (April 2020). The questionnaire statements regarding the legitimacy of off-label or experimental drugs were framed according to three different scenarios (Normality, Emergency and COVID-19). Furthermore, as the perception of internal bodily sensations (i.e., interoception) modulates the decision-making process, we tested participants' interoceptive sensibility using the Multidimensional Assessment of Interoceptive Awareness (MAIA). The results showed that participants were more inclined to legitimate non-evidence-based therapeutic approaches in the COVID-19 and Emergency scenarios than the Normality scenario. We also found that scores on the MAIA Trusting subscale positively predicted this difference. Our findings demonstrate that uncertain medical scenarios, involving a dramatic increase in patient volume and acuity, can increase risk-taking in therapeutic decision-making. Furthermore, individual characteristics of health care providers, such as interoceptive ability, should be taken into account when constructing models to prevent the breakdown of healthcare systems in cases of severe emergency. [ABSTRACT FROM AUTHOR]

Notable cross-country differences exist in the diffusion of the Covid-19 and in its lethality. Contact patterns in populations, and in particular intergenerational contacts, have been argued to be responsible for the most vulnerable, the elderly, getting infected more often and thus driving up mortality in some context, like in the southern European one. This paper asks a simple question: is it between whom contacts occur that matters or is it simply how many contacts people have? Due to the high number of confounding factors, it is extremely difficult to empirically assess the impact of single network features separately. This is why we rely on a simulation exercise in which we counterfactually manipulate single aspects of countries' age distribution and network structures. We disentangle the contributions of the kind and of the number of contacts while holding constant the age structure. More precisely, we isolate the respective effects of inter-age contact patterns, degree distribution and clustering on the virus propagation across age groups. We use survey data on face-to-face contacts for Great Britain, Italy, and Germany, to reconstruct networks that mirror empirical contact patterns in these three countries. It turns out that the number of social contacts (degree distribution) largely accounts for the higher infection rates of the elderly in the Italian context, while differences in inter-age contacts patterns are only responsible for minor differences. This suggests that policies specifically targeting inter-age contacts would be little effective. [ABSTRACT FROM AUTHOR]

Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics. Author summary: Seasonal influenza is a major burden to the health care systems of countries. Machine learning approaches and data from external sources are increasingly used for flu forecasting in recent years. In this study, we explore whether the inclusion of retail records in a predictive model improves seasonal influenza forecasting. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. Our predictions outperform the baseline approaches thus proving the added value of incorporating retail market data in forecasting models. [ABSTRACT FROM AUTHOR]