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This paper explores the proximity of local media to audiences during the COVID-19 pandemic. It is based on a case study of Italian daily L'Eco di Bergamo combining two sets of data: a list of initiatives the newspaper took in 2019 and 2020 to be closer to audiences, and interviews shedding light on how staff viewed the paper's relationship with audiences during that time. An analysis of these two datasets shows that the pandemic increased the newspaper's proximity to audiences but did not fundamentally change the way in which it related with audiences, despite significant changes in journalists' work routines. Based on these findings, the paper proposes a conceptualization of proximity as a dynamic balance between three structuring dimensions (gatekeeping, social and commercial), with a focus on audiences as a plural figure determined by news organizations' strategies. [ABSTRACT FROM AUTHOR]

This paper discusses the protagonism expressed by immigrant women in religion via a series of leadership roles and expands on this theme by considering the pandemic as an emblematic period in which such female activism revealed itself. While the literature gives important details on gender inequalities generated by COVID-19, this article brings to attention agency, resilience and innovation. The case of catholic ethnic churches in Italy, a country particularly hit by the implications of COVID-19, is the empirical field. This paper uses qualitative data obtained through prolonged fieldwork (2018–2022), allowing to discuss the role of ethnic churches before and after the pandemic. The empowerment processes of women in religion and their leading role in terms of welfare provision and activism are detailed, concluding by considering the implications of these. While public institutions were in trouble, religious minorities, and notably their female members, acted to ensure the survival of non-Italian citizens. [ABSTRACT FROM AUTHOR]

Given the benefits both individuals and collectivity have achieved over the past few years thanks to Mobility-as-Service (MaaS) systems, various studies were conducted to predict the level of acceptance of MaaS bundles from different territorial scales and in different countries. Results obtained are in some cases contradictory. Literature is lacking in the study of small-to-medium-sized urban contexts and in the effects of the ongoing COVID-19 pandemic. This paper aims to understand (1) what factors influence respondents' preferences between their usual transportation means and a possible MaaS alternative and (2) what leads a user to prefer one MaaS bundle to another. A logistic regression and a mixed logit model were developed to reach the two aims, respectively. These models were calibrated using questionnaires administered to employees of the Municipality of Padua, a medium-sized city in Italy. Aspects concerning the perception of health safety in relation to the COVID-19 pandemic were included in the analyses. In 37% of the cases, users stated they would be willing to adopt at least one of the proposed MaaS bundles. The results suggest that MaaS solutions can be a useful tool for managing mobility even in medium-sized cities, provided users' biosecurity concerns are addressed by appropriate countermeasures. [ABSTRACT FROM AUTHOR]

This paper proposes a modelling approach to assess the cross-region and cross-sector economic impacts of the restrictions imposed by governments to contain the COVID-19 pandemic. The nationwide lockdown imposed in Italy during the first wave of the pandemic is used as a benchmark. However, the adopted approach allows an ex-ante assessment of alternative policy responses, in the event of successive pandemic waves, in order to rationalise the policy intervention and reach the best possible compromise between containing the risk of contagion and reducing economic losses. The used approach consists of a non-linear programming model based on a multiregional Input-Output (I-O) table, which guarantees greater flexibility than traditional I-O analysis. It is applied to estimate both direct and indirect losses of GDP and employment produced by alternative policy responses represented by general and differentiated lockdowns. The evidence deriving from the Italian experience shows a sort of learning process through successive waves based on the introduction of increasingly flexible and tailored policy responses to the pandemic. [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]

The COVID‐19 pandemic is a highly dramatic concern for mankind. In Italy, the pandemic exerted its major impact throughout the period of February to June 2020. To date, the awkward amount of more than 134,000 deaths has been reported. Yet, post‐mortem autopsy was performed on a very modest number of patients who died from COVID‐19 infection, leading to a first confirmation of an immune‐thrombosis of the lungs as the major COVID‐19 pathogenesis, likewise for SARS. Since then (June–August 2020), no targeted early therapy considering this pathogenetic issue was approached. The patients treated with early anti‐inflammatory, anti‐platelet, anticoagulant and antibiotic therapy confirmed that COVID‐19 was an endothelial inflammation with immuno‐thrombosis. Patients not treated or scarcely treated with the most proper and appropriate therapy and in the earliest, increased the hospitalization rate in the intensive care units and also mortality, due to immune‐thrombosis from the pulmonary capillary district and alveoli. The disease causes widespread endothelial inflammation, which can induce damage to various organs and systems. Therapy must be targeted in this consideration, and in this review, we demonstrate how early anti‐inflammatory therapy may treat endothelia inflammation and immune‐thrombosis caused by COVID‐19, by using drugs we are going to recommend in this paper. [ABSTRACT FROM AUTHOR]

The aim of this paper is to analyse the use of Catholic miraculous images during the Covid-19 pandemic in Italy, focusing on the ways in which they have been used and the related rituals performed, especially involving movement, temporality, and nature. I take a multidisciplinary approach, based on a historical perspective, but enriched by the strong influence of anthropology and semiotics. An interesting focus emerges on the devotional practices, both online and, when possible, in presence, in which the miraculous power of historical images is rediscovered and sometimes re-elaborated in light of contemporary sensibilities. At the same time, the rituals performed by clergy constitute a complex negotiation, with the aim of imploring the miraculous power of the supernatural helper that the image represents. The devotees' idea of a miraculous power involved in the image is itself part of the representation of their power. [ABSTRACT FROM AUTHOR]

Early warning tools are crucial for the timely application of intervention strategies and the mitigation of the adverse health, social and economic effects associated with outbreaks of epidemic potential such as COVID-19. This paper introduces, the Epidemic Volatility Index (EVI), a new, conceptually simple, early warning tool for oncoming epidemic waves. EVI is based on the volatility of newly reported cases per unit of time, ideally per day, and issues an early warning when the volatility change rate exceeds a threshold. Data on the daily confirmed cases of COVID-19 are used to demonstrate the use of EVI. Results from the COVID-19 epidemic in Italy and New York State are presented here, based on the number of confirmed cases of COVID-19, from January 22, 2020, until April 13, 2021. Live daily updated predictions for all world countries and each of the United States of America are publicly available online. For Italy, the overall sensitivity for EVI was 0.82 (95% Confidence Intervals: 0.75; 0.89) and the specificity was 0.91 (0.88; 0.94). For New York, the corresponding values were 0.55 (0.47; 0.64) and 0.88 (0.84; 0.91). Consecutive issuance of early warnings is a strong indicator of main epidemic waves in any country or state. EVI's application to data from the current COVID-19 pandemic revealed a consistent and stable performance in terms of detecting new waves. The application of EVI to other epidemics and syndromic surveillance tasks in combination with existing early warning systems will enhance our ability to act swiftly and thereby enhance containment of outbreaks. [ABSTRACT FROM AUTHOR]

This paper deals with the quantification of the different rates in epidemiological models from a function estimation framework, with the objective of identifying the desired unknowns without defining a priori basis functions for describing its behaviour. This approach is used to analyze data for the Covid-19 pandemic in Italy and Brazil. The forward problem is written in terms of the SIRD model, while the inverse problem is solved by combining the Levenberg–Marquardt method with Tikhonov regularization. A very good agreement was achieved between data and the calculated values. The resulting methodology is robust and very versatile, being easily applicable to other epidemiology models and data from other countries. [ABSTRACT FROM AUTHOR]

The COVID-19 epidemic is the latest in a long list of pandemics that have affected humankind in the last century. In this paper, we propose a novel mathematical epidemiological model named SUIHTER from the names of the seven compartments that it comprises: susceptible uninfected individuals (S), undetected (both asymptomatic and symptomatic) infected (U), isolated infected (I), hospitalized (H), threatened (T), extinct (E) and recovered (R). A suitable parameter calibration that is based on the combined use of the least-squares method and the Markov chain Monte Carlo method is proposed with the aim of reproducing the past history of the epidemic in Italy, which surfaced in late February and is still ongoing to date, and of validating SUIHTER in terms of its predicting capabilities. A distinctive feature of the new model is that it allows a one-to-one calibration strategy between the model compartments and the data that are made available daily by the Italian Civil Protection Department. The new model is then applied to the analysis of the Italian epidemic with emphasis on the second outbreak, which emerged in autumn 2020. In particular, we show that the epidemiological model SUIHTER can be suitably used in a predictive manner to perform scenario analysis at a national level. [ABSTRACT FROM AUTHOR]

• The effect of the lockdown due to COVID-19 on the Italian power system are discussed. • A critical analysis of the reduction in the Italian power consumption and electricity cost in 2020 is presented. • Some countermeasures against the effect of generation and load reduction are proposed. • A blockchain-based architecture for Demand Response is proposed as a countermeasure. The paper analyses the dramatic effects of the first wave of the COVID-19 pandemic on the Italian power system, considering the impacts on load and generation, market issues and ancillary services provision. During the period between March and May 2020, the Italian scenario was considered as exemplary due to the large reduction of loads and thermal generation caused by the hard lockdown across the Country. Italy has experienced the most severe effects of pandemic in terms of increase of death rates and of pressure on the health system as well as severity of the countermeasures. As a domino effect, the total lockdown of one of the most industrialized countries in the world has created an emergency situation that has put a big stress on the power system, reducing the possibility for the system to recover under contingencies. The weakness of the system in such an extreme situation is analyzed in depth using open data, while some countermeasures for providing resilience to the power systems in such cases are analyzed in simulation. Two technologies are here considered as exemplary countermeasures for providing resilience to the Italian power system: Fast Frequency Response by Photovoltaic and Wind plants with rated power above 10 MVA and the massive implementation of Demand Response programs through Energy communities and aggregation using Blockchain technology. The simulations show, on one hand, that the frequency recovery under contingency seems compromised in the current situation and that the support from such innovative technologies can provide a significant relief bringing the operational features close to those before the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

In this paper, antecedents and consequences of conspiracy beliefs are investigated in the context of the COVID-19 pandemic in Italy. 618 individuals residing in different geographical area of Italy participated in the study. We found that perceived mortality rate of COVID-19 is positively associated with adherence to conspiracy beliefs and, in turn, with negative outcomes such as: (a) a reduced support for the measures taken to deal with the pandemic emergency, (b) a weaker feeling of guilt for the violation of anti-COVID-19 government rules, and (c) a stronger utilitarian stance which prioritizes economic over health-related outcomes of lockdown. Also, framing pandemic within the "natural order of things" – fundamental and implicit expectations concerning how life and the world should function – was expected to moderate the relationship between perceived mortality rate and conspiracy beliefs: we found this relationship to be weaker when people believe that pandemic falls into the "natural order of things". [ABSTRACT FROM AUTHOR]