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Before the COVID-19 pandemic, ventilation in buildings was not always given its due importance. The World Health Organization has highlighted the important role of air exchange with the outdoors in improving the air quality in buildings; buildings should, therefore, be equipped with mechanical ventilation or adequate air conditioning systems. This paper aims to investigate different retrofit solutions for air conditioning, evaluating them in terms of energy consumption and cost and the impact of increased outdoor air exchange rates on countering the propagation of COVID-19; the latter is the main novelty of the paper. As a case study, we take an existing office building located in Central Italy that was previously not equipped with a mechanical ventilation system (a system with primary air was introduced during the study). The energy analysis was conducted using dynamic simulation software after validation through energy bills; energy and economic analyses were conducted considering different external-air exchange rates. An optimal number of outdoor air changes was found to mitigate the risk of COVID-19 infection, a finding in line with the international literature. The increase in air changes with outdoor air leads to a rise in energy consumption and costs. These values were evaluated for different air conditioning systems and operational schedules. These drawbacks can be made less significant by combining interventions in the system with energy-efficiency measures applied to the building envelope. [ABSTRACT FROM AUTHOR]

Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building's HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 pandemic has emerged as the primary global health challenge of the new millennium. Understanding its impact on health systems and learning from these experiences are crucial for improving system resilience against future health crises. This paper examines hospitalizations related to COVID-19 in Italy from 2020 to 2021, with a specific focus on the costs associated with these admissions. Design and methods: This is a retrospective, population-based study of Italian hospitalizations of patients diagnosed with COVID-19 during the 2020–2021 period, using data extracted from the National Hospital Discharge Registry. The outcome variables considered include hospital admissions, costs, and length of stay. Results: In Italy, hospitalizations for COVID-19 totaled 357,354 in 2020 and 399,043 in 2021, with the transfer rate being three times higher than that of other patients. Hospitalizations were predominantly concentrated in the northern regions, especially during the first year. Mortality rates increased with age, while hospitalization rates peaked in the youngest and oldest age groups. The financial impact of COVID-19 hospitalizations was approximately €3.1 billion in 2020 and €3.6 billion in 2021. The cost per admission was around €8000 for standard care and €24,000 for intensive therapy in both years. Conclusion: Conducting a cost-benefit analysis of implementing a protective pad around the entire health system, which leverages networks of family doctors and nurses connected in real-time, could be an important step in strengthening health system resilience. [ABSTRACT FROM AUTHOR]

At the beginning of 2023, the Italian former prime minister, the former health minister and 17 others including the current president of the Lombardy region were placed under investigation on suspicion of aggravated culpable epidemic in connection with the government's response at the start of the COVID-19 pandemic. The charges revolve around the failure by authorities to take adequate measures to prevent the spread of the virus in the Bergamo area, which experienced a significant excess of deaths during the initial outbreak. The aim of this paper is to analyse the pandemic data of Italy and the Lombardy region in the first 10 days of the pandemic, spanning from the 24th of February 2020 to the 4th of March 2020. The objective is to determine whether the use of early warning indicators could have facilitated the identification of a critical increase in infections. This identification, in turn, would have enabled the timely formulation of strategies for pandemic containment, thereby reducing the number of deaths. In conclusion, to translate our findings into practical guidelines, we propose a low-cost early warning method for infectious respiratory diseases with asymptomatic carriers. [ABSTRACT FROM AUTHOR]

Inverted distributions, also known as inverse distributions, are essential statistical models for analyzing real-life data in biomedical sciences, engineering, and other fields. In this paper, we use the odd beta prime-G family and the inverted Kumaraswamy distribution to introduce a new inverted distribution called the odd beta prime inverted Kumaraswamy. The new distribution exhibits right-skewed, J-shaped densities and features increasing-constant, concave-convex, and bathtub hazard functions. Some of its statistical properties are explored. The parameters are estimated via the maximum likelihood method. The empirical importance of the new model is proved through its application to COVID-19 mortality data from Italy. Numerical results demonstrate that the proposed model outperforms its competitors. We hope that this proposed distribution can be considered as a viable alternative to some well-established distributions for modeling real-life data across various application areas. [ABSTRACT FROM AUTHOR]

Several animal species have been found to be susceptible to SARS-CoV-2 infection. The occurrence of infection in dogs and cats living in close contact with owners deserves particular attention from public health authorities in a One Health approach. In this study, we conducted serological screening to identify SARS-CoV-2 exposure in the sera from dogs and cats in three regions of southern Italy sampled during the years 2021 and 2022. We collected 100 serum samples in 2021 (89 from dogs and 11 from cats) and 640 in 2022 (577 from dogs and 63 from cats). Overall, the ELISA positivity rate was found to be 2.7% (20/740), with higher seroprevalence in dogs. Serum neutralization tests confirmed positivity only in two samples collected from dogs, and the assays, performed with serologically distinct SARS-CoV-2 variants, showed variant-specific positivity. This paper shows that monitoring SARS-CoV-2 exposure in animals might be affected by the viral antigenic evolution, which requires continuous updates to the serological tests used. Serological surveys are useful in understanding the true extent of exposure occurring in specific animal populations, not suffering the same limitations as molecular tests, and could help in identifying the infecting virus if tests able to characterize the immune response are used. The use of variant-specific validated serological methods should always be considered in serosurvey studies in order to determine the real impact of emerging variants on animal populations and its implications for veterinary and human health, as well as to identify potential reservoirs of the virus and its evolutionary changes. [ABSTRACT FROM AUTHOR]

Infectious disease epidemics are challenging for medical and public health practitioners. They require prompt treatment, but it is challenging to recognize and define epidemics in real time. Knowing the prediction of an infectious disease epidemic can evaluate and prevent the disease's impact. Mathematical models of epidemics that work in real time are important tools for preventing disease, and data-driven deep learning enables practical algorithms for identifying parameters in mathematical models. In this paper, the SIR model was reduced to a logistic differential equation involving a constant parameter and a time-dependent function. The time-dependent function leads to constant, rational, and birational models. These models use several constant parameters from the available data to predict the time and number of people reported to be infected with the COVID-19 Omicron variant. Two out of these three models, rational and birational, provide accurate predictions for countries that practice strict mitigation measures, but fail to provide accurate predictions for countries that practice partial mitigation measures. Therefore, we introduce a time-series model based on neural networks to predict the time and number of people reported to be infected with the COVID-19 Omicron variant in a given country that practices both partial and strict mitigation measures. A logistics-informed neural network algorithm was also introduced. This algorithm takes as input the daily and cumulative number of people who are reported to be infected with the COVID-19 Omicron variant in the given country. The algorithm helps determine the analytical solution involving several constant parameters for each model from the available data. The accuracy of these models is demonstrated using error metrics on Omicron variant data for Portugal, Italy, and China. Our findings demonstrate that the constant model could not accurately predict the daily or cumulative infections of the COVID-19 Omicron variant in the observed country because of the long series of existing data of the epidemics. However, the rational and birational models accurately predicted cumulative infections in countries adopting strict mitigation measures, but they fell short in predicting the daily infections. Furthermore, both models performed poorly in countries with partial mitigation measures. Notably, the time-series model stood out for its versatility, effectively predicting both daily and cumulative infections in countries irrespective of the stringency of their mitigation measures. [ABSTRACT FROM AUTHOR]

COVID-19, which broke out globally in 2019, is an infectious disease caused by a novel strain of coronavirus, and its spread is highly contagious and concealed. Environmental vectors play an important role in viral infection and transmission, which brings new difficulties and challenges to disease prevention and control. In this paper, a type of differential equation model is constructed according to the spreading functions and characteristics of exposed individuals and environmental vectors during the virus infection process. In the proposed model, five compartments were considered, namely, susceptible individuals, exposed individuals, infected individuals, recovered individuals, and environmental vectors (contaminated with free virus particles). In particular, the re-positive factor was taken into account (i.e., recovered individuals who have lost sufficient immune protection may still return to the exposed class). With the basic reproduction number R0 of the model, the global stability of the disease-free equilibrium and uniform persistence of the model were completely analyzed. Furthermore, sufficient conditions for the global stability of the endemic equilibrium of the model were also given. Finally, the effective predictability of the model was tested by fitting COVID-19 data from Japan and Italy.

During the first wave of the COVID-19 pandemic in Italy, practitioners had to make tragic decisions regarding the allocation of scarce resources in the ICU. The Italian debate has paid a lot of attention to identifying the specific regulatory criteria for the allocation of resources in the ICU; in this paper, however, we argue that deciding such criteria is not enough for the implementation of fair and transparent allocative decisions. In this respect, we discuss three ethical issues: (a) in the Italian context, the treating physician, rather than a separate committee, was generally the one responsible for the allocation decision; (b) although many allocative guidelines have supported moral equivalence between withholding and withdrawing treatments, some health professionals have continued to consider it a morally problematic aspect; and (c) the health workers who have had to make the aforementioned decisions or even only worked in ICU during the pandemic often experienced moral distress. We conclude by arguing that, even if these problems are not directly related to the above-mentioned issues of distributive justice, they can nevertheless directly affect the quality and ethics of the implementation of allocative criteria, regardless of those chosen. [ABSTRACT FROM AUTHOR]

Acknowledging the extreme risk COVID-19 poses to humans, this paper attempted to analyze and compare case fatality rates, identify the existence of learning curves for COVID-19 medical treatments, and examine the impact of vaccination on fatality rate reduction. Confirmed cases and deaths were extracted from the "Daily Situation Report" provided by the World Health Organization. The results showed that low registration and low viral test rates resulted in low fatality rates, and the learning curve was significant for all countries except China. Treatment for COVID-19 can be improved through repeated experience. Vaccinations in the U.K. and U.S.A. are highly effective in reducing fatality rates, but not in other countries. The positive impact of vaccines may be attributed to higher vaccination rates. In addition to China, this study identified the existence of learning curves for the medical treatment of COVID-19 that can explain the effect of vaccination rates on fatalities. [ABSTRACT FROM AUTHOR]

2020 will be remembered worldwide as the year of COVID-19 outbreak. The onset of this pandemic abruptly changed everybody's life and, in a particular manner, doctors' lives. Our hand surgery department became rapidly one of the first COVID-19-specialized wards in Italy, impacting considerably the authors' routines and activities. In this paper, the authors focus on how the demographics of patients with hand trauma changed and how they had to modify their activity. The authors retrospectively took into consideration all patients reaching their emergency department (ED) with hand trauma between 9 March 2020 (the day of the beginning of the first lockdown in Italy) and 8 March 2021 and compared them to those who reached the ED in the three previous years. Authors have analyzed the number of patients, their gender and age, the severity of their trauma, where the trauma occurred, the type of lesion, the percentage of patients who underwent surgery, and the percentage of patients who had an emergency admission. In the last year, the number of patients reaching the ED for a hand trauma has been reduced by two thirds (975 patients during the past year), the mean age of those patients has slightly increased, the severity of cases has increased, places of trauma and type of lesions have changed, and, lastly, the percentage of patients needing surgery who were admitted immediately has increased. This paper shows how the type of patients reaching the ED changed and discusses how surgeons evolved and modified their habits in treating those patients during the first lockdown and the year that followed. [ABSTRACT FROM AUTHOR]

Healthcare-related SARS-CoV-2 infection is an issue of particular concern during the pandemic. It has important repercussions on the National Health System, which represents a source of medical-legal health disputes. In the healthcare context, there are reports of negative screening at hospital admission (via nasopharyngeal swabs) and subsequent diagnosis of SARS-CoV-2 infection during hospitalization. Such cases cannot be considered a priori of healthcare-related infections but require extensive in-depth evaluation. In this study, we propose an empirical classification to frame cases of SARS-CoV-2 infection diagnosed in the hospital (first negative admission swab, with subsequent positive test during hospitalization). The classification is based on five categories: nosocomial, probably nosocomial, indeterminate, probably community, and community cases. We analyzed patients who died after testing positive for SARS-CoV-2 during hospitalization (with initial negative screening) in the largest hospital in Northwest Italy from February 2020 to 31 December 2021. A total of 383 cases were tracked and are listed as follows: 41 cases (11%) were classified as nosocomial (i.e., 3.2% of COVID-19 deaths). In contrast, 71 cases (19%) were classified as probably nosocomial, 69 (18%) were indeterminate (i.e., the clinical, radiological, and laboratory characteristics did not provide information on the genesis of the infection), 166 (43%) were classified as probably community cases, and 36 (9%) were defined as community cases. Deceased patients with nosocomial SARS-CoV-2 infection constituted the following: 3.23% (41/1266) with respect to the total number of COVID-19 deaths, 1.1% (41/3789) with respect to those who entered the hospital with a negative swab and 0.82% (41/4672) with respect to the total of deaths from any cause of death. In this paper we discuss the topic and issues of nosocomial COVID-19 in hospitalized patients and address the medicolegal implications.

Colorectal cancer (RC) is the third most common cancer, with an increasing incidence in recent years. Digital health solutions supporting multidisciplinary tumor boards (MTBs) could improve positive outcomes for RC patients. This paper describes the implementation process of a digital solution within the RC-MTB and its impact analysis in the context of the Fondazione Policlinico 'A. Gemelli' in Italy. Adopting a two-phase methodological approach, the first phase qualitatively describes each phase of the implementation of the IT platform, while the second phase quantitatively describes the analysis of the impact of the IT platform. Descriptive and inferential analyses were performed for all variables, with a p -value < 0.05 being considered statistically significant. The implementation of the platform allowed more healthcare professionals to attend meetings and resulted in a decrease in patients sent to the RC-MTB for re-staging and further diagnostic investigations and an increase in patients sent to the RC-MTB for treatment strategies. The results could be attributed to the facilitated access to the platform remotely for specialists, partly compensating for the restrictions imposed by the COVID-19 pandemic, as well as to the integration of the platform into the hospital's IT system. Furthermore, the early involvement of healthcare professionals in the process of customizing the platform to the specific needs of the RC-MTB may have facilitated its use and contributed to the encouraging quantitative results.

Italian Long-Term Care is considered largely inadequate, and the recent COVID-19 pandemic has dramatically exposed its limitations. Public Home Care Services in particular were revealed as under-financed and unable to cover the potential demand for care from the older population. But does the type of municipality and its geographic location play a role in creating or mitigating unmet demand? This is the first study addressing this research question in Italy. Our hypothesis is that older people's care preferences and care possibilities may vary between small, medium and metropolitan areas, as will the organisation, funding and availability of services, and the combination will influence (unmet) demand for public home care services. In this paper, using nationally representative survey data collected by the Italian National Statistical Institute in 2003 and 2016, we investigate changes and differences in the use of public and private home care services among people aged 75 or older in Italy by size of the municipality. Our results reveal inequalities in service use between Northern and Southern areas of the country and in particular between metropolitan areas, medium and small municipalities. Such differences reinforce post-pandemic calls for new investment and changes in the design of the Italian Long-Term Care system.

Residences for elderly and sick people, self-sufficient or dependent, are varied. To date, the liability profiles of these structures are not clearly delineated, and increasingly often, their operating and organization criteria are entrusted to subnational, regional, or local regulations. Among the various deficits, there is the keeping of a complete and detailed documentation/diary of the patient, the lack of which can generate medico-legal problems. In this paper, we present three cases of guests in residences for a dependent person brought to the attention of the Institute of Forensic Medicine of the University Hospital of Palermo due to criminal proceedings, where the lack of existing documentation in the structure and, in some cases, the behavior of the professionals working there, led the evaluator to deduce the organization's culpability. [ABSTRACT FROM AUTHOR]

The response to the COVID-19 pandemic has been highly variable. Governments have applied different mitigation policies with varying effect on social and economic measures, over time. This article presents a methodology for examining the effect of mobility restriction measures and the association between health and population activity data. As case studies, we refer to the pre-vaccination experience in Italy and Israel. Facing the pandemic, Israel and Italy implemented different policy measures and experienced different population behavioral patterns. Data from these countries are used to demonstrate the proposed methodology. The analysis we introduce in this paper is a staged approach using Bayesian Networks and Structural Equations Models. The goal is to assess the impact of pandemic management and mitigation policies on pandemic spread and population activity. The proposed methodology models data from health registries and Google mobility data and then shows how decision makers can conduct scenario analyses to help design adequate pandemic management policies.

(1) Background: Italy accounts for more than 150,000 deaths due to the COVID-19 pandemic, leading the top rank in SARS-CoV-2-caused deceases in Europe. A survey on the different ways by which the COVID-19 pandemic emergency was managed in the foreign European countries compared to Italy is the purpose of this paper. (2) Methods: A literature search and various mathematical algorithms to approach a rank scoring scale were used to describe in detail the different approaches used by European countries to manage the COVID-19 pandemic emergency. (3) Results: The study showed that Italy stands at the bottom ranking for COVID-19 management due to its high mortality rate. Possible causes of the observed huge numbers of hospitalization and deaths were (a) the demographic composition of the European country; (b) its decentralized healthcare system organization; (c) the role of correct pharmacology in the early stages before hospitalization. Post-mortem examinations were of paramount importance to elucidate the etiopathogenesis of COVID-19 and to tailor a suitable and proper therapy in the early symptomatic stages of COVID-19, preventing hospitalization. (4) Conclusions: Factors such as the significant impact on elderly people, the public health organization prevalently state-owned and represented mainly by hospitals, and criticism of the home therapy approach toward SARS-CoV-2-infected people, may have concurred in increasing the number of COVID-19 deaths in Italy.

Despite the stunning speed with which highly effective and safe vaccines have been developed, the emergence of new variants of SARS-CoV-2 causes high rates of (re)infection, a major impact on health care services, and a slowdown to the socio-economic system. For COVID-19, accurate and timely forecasts are therefore essential to provide the opportunity to rapidly identify risk areas affected by the pandemic, reallocate the use of health resources, design countermeasures, and increase public awareness. This paper presents the design and implementation of an approach based on autoregressive models to reliably forecast the spread of COVID-19 in Italian regions. Starting from the database of the Italian Civil Protection Department (DPC), the experimental evaluation was performed on real-world data collected from February 2020 to March 2022, focusing on Calabria, a region of Southern Italy. This evaluation shows that the proposed approach achieves a good predictive power for out-of-sample predictions within one week (R-squared > 0.9 at 1 day, R-squared > 0.7 at 7 days), although it decreases with increasing forecasted days (R-squared > 0.5 at 14 days). [ABSTRACT FROM AUTHOR]

In this paper, the fractional-order generalization of the susceptible-infected-recovered (SIR) epidemic model for predicting the spread of the COVID-19 disease is presented. The time-domain model implementation is based on the fixed-step method using the nabla fractional-order difference defined by Grünwald-Letnikov formula. We study the influence of fractional order values on the dynamic properties of the proposed fractional-order SIR model. In modeling the COVID-19 transmission, the model's parameters are estimated while using the genetic algorithm. The model prediction results for the spread of COVID-19 in Italy and Spain confirm the usefulness of the introduced methodology. [ABSTRACT FROM AUTHOR]

More than a year has passed since "patient 0" was found and COVID-19 is now an established reality that a lot of people have had to accept and coexist with. In Italy, the pandemic hit in "waves", but the studies assessing the longitudinal impact of the pandemic in the general population are not many. In this paper, we compared data collected during the first wave with data gathered during the second one, so that we can provide insights about the Italian population's psychological adaptation to the pandemic also by comparing them with pre-pandemic normative scores. From our results, it seemed that people were seeking a compromise: indeed, despite the still-relevant risk perception, people apparently "learned" how to deal with the pandemic as indicated by an increase in self-efficacy and a more internal locus of control.

Italy was a country severely hit by the first coronavirus disease 2019 (COVID-19) pandemic wave in early 2020. Mortality studies have focused on the overall excess mortality observed during the pandemic. This paper investigates the cause-specific mortality in Italy from March 2020 to April 2020 and the variation in mortality rates compared with those in 2015-2019 regarding sex, age, and epidemic area. Causes of death were derived from the national cause-of-death register. COVID-19 was the leading cause of death among males and the second leading cause among females. Chronic diseases, such as diabetes and hypertensive, ischemic heart, and cerebrovascular diseases, with decreasing or stable mortality rates in 2015-2019, showed a reversal in the mortality trend. Moreover, mortality due to pneumonia and influenza increased. No increase in neoplasm mortality was observed. Among external causes of death, mortality increased for accidental falls but reduced for transport accidents and suicide. Mortality from causes other than COVID-19 increased similarly in both genders and more at ages 65 years or above. Compared with other areas in Italy, the Lombardy region showed the largest excess in mortality for all leading causes. Underdiagnosis of COVID-19 at the beginning of the pandemic may, to some extent, explain the mortality increase for some causes of death, especially pneumonia and other respiratory diseases.

Background: the Lombardy region in Italy was the first area in Europe to record an outbreak of COVID-19 and one of the most affected worldwide. As this territory is strongly polluted, it was hypothesized that pollution had a role in facilitating the diffusion of the epidemic, but results are uncertain., Aim: the paper explores the effect of air pollutants in the first spread of COVID-19 in Lombardy, with a novel geomatics approach addressing the possible confounding factors, the reliability of data, the measurement of diffusion speed, and the biasing effect of the lockdown measures., Methods and Results: all municipalities were assigned to one of five possible territorial classes (TC) according to land-use and socio-economic status, and they were grouped into districts of 100,000 residents. For each district, the speed of COVID-19 diffusion was estimated from the ambulance dispatches and related to indicators of mean concentration of air pollutants over 1, 6, and 12 months, grouping districts in the same TC. Significant exponential correlations were found for ammonia (NH 3 ) in both prevalently agricultural (R 2 = 0.565) and mildly urbanized (R 2 = 0.688) areas., Conclusions: this is the first study relating COVID-19 estimated speed of diffusion with indicators of exposure to NH 3 . As NH 3 could induce oxidative stress, its role in creating a pre-existing fragility that could have facilitated SARS-CoV-2 replication and worsening of patient conditions could be speculated.

The coronavirus disease 2019 pandemic still represents a global public health emergency, despite the availability of different types of vaccines that reduced the number of severe cases, the hospitalization rate and mortality. The Italian Vaccine Distribution Plan identified healthcare workers (HCWs) as the top-priority category to receive access to a vaccine and different studies on HCWs have been implemented to clarify the duration and kinetics of antibody response. The aim of this paper is to perform a literature review across a total of 44 studies of the serologic response to COVID-19 vaccines in HCWs in Italy and to report the results obtained in a prospective longitudinal study implemented at the Fondazione IRCCS Istituto Nazionale Tumori (INT) of Milan on 1565 HCWs. At INT we found that 99.81% of the HCWs developed an antibody response one month after the second dose. About six months after the first serology evaluation, 100% of the HCWs were still positive to the antibody, although we observed a significant decrease in its levels. Overall, our literature review results highlight a robust antibody response in most of the HCWs after the second vaccination dose. These figures are also confirmed in our institutional setting seven months after the completion of the cycle of second doses of vaccination. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic in March 2020, national and international authorities started to develop and update datasets to provide data to researchers, journalists and health care providers as well as public opinion. These data became one of the most important sources of information, which are updated daily and analysed by scientists in order to investigate and predict the spread of this epidemic. Despite this positive reaction from both national and international authorities in providing aggregated information on the diffusion of COVID-19, different challenges have been underlined in previously published studies. Different papers have discussed strengths and weaknesses of these types of datasets by focusing on different quality perspectives, which include the statistical methods adopted to analyse them; the lack of standards and models in the adoption of data for their management and distribution; and the analysis of different data quality characteristics. These studies have analysed datasets at the general level or by focusing the attention on specific indicators such as the number of cases or deaths. This paper further investigates issues and opportunities in the diffusion of these datasets under two main perspectives. At the general level, it analyses how data are organized and distributed to scientific and non-scientific communities. Moreover, it further explores the indicators adopted to describe the spread of the COVID-19 epidemic while also highlighting the level of detail used to describe them in terms of gender, age ranges and territorial units. The paper focuses on six European countries: Belgium, France, Germany, Italy, Spain and UK.

Within the confinements of critical infrastructures, the COVID-19 pandemic is posing a series of challenges to Health Management. In the spotlight of highly contagious and quick spreading diseases within such enclosed facilities, whether it be a detention facility or otherwise, the health and safety of those living within its internment is paramount. This paper aims to highlight the specific challenges and the possible solutions to counteract this problem, starting from the lessons learnt from the Italian prison system case study. Following the general description of the available resources within the Italian prisons, the study aimed at specifically describing the first counteracting measures deployed by the Italian prison authorities during the first phase of the COVID-19 outbreak (February-July 2020). The aim was to propose an integrated plan capable of responding to a biological threat within the prisons. In particular, the study describes the actions and technical features that, in accordance with national and international legal frameworks and the relevant organisational bodies that run the Italian Prison Service, had been adopted in managing, right from the start, the COVID-19 pandemic until Summer 2020. Available information and data showed the ability of the prison administration to comply almost completely with WHO's technical and human rights recommendations and also, in successfully handling prison emergencies both in terms of the sick and the deceased in line with the epidemiological framework of the general population. In addition, the paper proposes a draft of guidelines that should involve the National Health Service and the Prison Service that are aimed at supporting the local prison facilities with drawing up their own biological incident contingency plans. An approved, legal, standardised plan could increase the awareness of prison managers. It could even increase their self-confidence, in particular, with regard to cases of dispute and their ability to respond to them. In fact, it is valuable and forward-thinking to be able to demonstrate that every endeavour has been taken and that 'certified' best practices have been put in place in accordance with the national standards.

The COVID-19 global pandemic still represents a major threat with detrimental health consequences. Analyzing the psychological outcomes, COVID-19 could be interpreted as a collective traumatic event that can generate symptoms related to post-traumatic stress disorder (PTSD). Considering this, the purpose of this paper is twofold: first, to investigate the relationship between intrusive thoughts and fear related to the COVID-19 pandemic and between intrusive thoughts and mental health; second, to test the mediating role of hyperarousal and avoidance in these two relationships. In order to reach these aims, the present study investigated these relationships and tested a mediation model in two cross-sectional studies in Italy. Altogether, 627 individuals and 495 workers completed an online survey for study 1 and study 2, respectively. Mediation analyses were performed via the SPSS macro PROCESS; the significance of total, direct, and indirect effect was tested via bootstrapping. The results showed that within the PTSD framework, hyperarousal compared with avoidance mediated the relationship between intrusion and the analyzed outcomes. In conclusion, the present study provided empirical evidence for the influence of hyperarousal on individual consequences such as fear of COVID-19 and mental health. Research, as well as theoretical and practical implications, are discussed.

The first wave of COVID-19 spread worldwide from March to May 2020. Italy was one of the countries in the world where the lockdown period was most prolonged and restrictive. To date, the impact of prolonged lockdown on pediatric traumas has not fully investigated. This paper aimed to analyze, and compare to 2019, the incidence and the fracture pattern in patients admitted to our pediatric hospital during the total lockdown period. A single-center retrospective study was performed. The data were gathered from the Pediatric Emergency Department (PED) of the Bambino Gesù Children Hospital of Rome (Palidoro). This PED is the pediatric referral center for Rome and the hub for pediatric traumas of the region. Any admission diagnosis for fracture, trauma, sprains and dislocation during the lockdown period (10 March-4 May) were included. The demographic data, diagnosis, type of treatment, fracture segment, bone involvement and time interval between trauma and presentation to the PED were recorded. In 2020, a reduction of total traumas and fractures, compared to 2019 ( p < 0.001), occurred (81%). Superior limb and inferior limb fractures decreased in 2020 compared to 2019 ( p < 0.05). The identification of pediatric traumas and fractures trend could be useful to reorganize the PED. Epidemiological data from the previous lockdown could be helpful to prepare the healthcare system for new pandemic waves. Moreover, sharing national statistics and correlating those to other countries' protocols, could be helpful to solve problems in case of worldwide emergency situations.

In this paper, we develop a forecasting model for the spread of COVID-19 infection at a provincial (i.e., EU NUTS-3) level in Italy by using official data from the Italian Ministry of Health integrated with data extracted from daily official press conferences of regional authorities and local newspaper websites. This data integration is needed as COVID-19 death data are not available at the NUTS-3 level from official open data channels. An adjusted time-dependent SIRD model is used to predict the behavior of the epidemic; specifically, the number of susceptible, infected, deceased, recovered people and epidemiological parameters. Predictive model performance is evaluated using comparison with real data.

The recent COVID-19 pandemic has imposed a general lockdown in Italy, one of the most affected countries at the beginning of the outbreak, between 9 March and 3 May 2020. As a consequence, Italian citizens were confined at home for almost two months, an unprecedented situation, which could have negative effects on both psychological and physical health. The aim of this study was to review the published papers concerning the effects of the lockdown on physical activity and the consequences on general health. As expected, most studies highlighted a significant reduction in the amount of performed physical activity compared to before lockdown, in both the general population and in individuals with chronic conditions. This fact had negative consequences on both general health, in terms of increased body mass, and on specific chronic conditions, especially obesity and neurological diseases.

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

The new coronavirus emergency spread to Italy when little was known about the infection's impact on mothers and newborns. This study aims to describe the extent to which clinical practice has protected childbirth physiology and preserved the mother-child bond during the first wave of the pandemic in Italy. A national population-based prospective cohort study was performed enrolling women with confirmed SARS-CoV-2 infection admitted for childbirth to any Italian hospital from 25 February to 31 July 2020. All cases were prospectively notified, and information on peripartum care (mother-newborn separation, skin-to-skin contact, breastfeeding, and rooming-in) and maternal and perinatal outcomes were collected in a structured form and entered in a web-based secure system. The paper describes a cohort of 525 SARS-CoV-2 positive women who gave birth. At hospital admission, 44.8% of the cohort was asymptomatic. At delivery, 51.9% of the mothers had a birth support person in the delivery room; the average caesarean section rate of 33.7% remained stable compared to the national figure. On average, 39.0% of mothers were separated from their newborns at birth, 26.6% practised skin-to-skin, 72.1% roomed in with their babies, and 79.6% of the infants received their mother's milk. The infants separated and not separated from their SARS-CoV-2 positive mothers both had good outcomes. At the beginning of the pandemic, childbirth raised awareness and concern due to limited available evidence and led to "better safe than sorry" care choices. An improvement of the peripartum care indicators was observed over time.

This paper outlines the role of Lombardy's regional influenza reference laboratory (Northern Italy) in the surveillance of influenza-like illnesses (ILIs) in monitoring SARS-CoV-2 circulation by analyzing 631 consecutive nasopharyngeal swabs (NPSs) collected from ILI outpatients by sentinel physicians during the 2019-2020 season. The samples were tested by specific real-time RT-PCRs targeting SARS-CoV-2, influenza viruses, and RSVs. Results: Of these NPSs, 31% tested positive for influenza viruses, 10% for SARS-CoV-2, and 7% for RSV. No coinfections were detected. Influenza viruses and RSVs circulated throughout the surveillance period until the end of February (week 9-2020), when they suddenly ceased to circulate seven weeks earlier than during the previous five influenza seasons. After the first detection of SARS-CoV-2 in our ILI outpatients at the beginning of March (week 10-2020), SARS-CoV-2 remained the only virus identified throughout the surveillance period. Patients ≥ 65 years had a 3.2-fold greater risk of being infected with SARS-CoV-2, while school-age children (5-14 years) and children < 5 years proved to be the age groups most at risk of contracting influenza viruses and RSV, respectively. Our experience demonstrates that laboratory-based ILI surveillance networks are essential for identifying SARS-CoV-2 cases that would otherwise remain undetected, in order to stop their spread within our communities.

This paper describes the in situ monitoring of indoor air quality (IAQ) in two dwellings, using low-cost IAQ sensors to provide high-density temporal and spatial data. IAQ measurements were conducted over 2-week periods in the kitchen and bedroom of each home during the winter, spring, and summer seasons, characterized by different outside parameters, that were simultaneously measured. The mean indoor PM 2.5 concentrations were about 15 μg m -3 in winter, they dropped to values close to 10 μg m -3 in spring and increased to levels of about 13 μg m -3 in summer. During the winter campaign, indoor PM 2.5 was found mainly associated with particle penetration inside the rooms from outdoors, because of the high outdoor PM 2.5 levels in the season. Such pollution winter episodes occur frequently in the study region, due to the combined contributions of strong anthropogenic emissions and stable atmospheric conditions. The concentrations of indoor volatile organic compounds (VOCs) and CO 2 increased with the number of occupants (humans and pets), as likely associated with consequent higher emissions through breathing and metabolic processes. They also varied with occupants' daily activities, like cooking and cleaning. Critic CO 2 levels above the limit of 1000 ppm were observed in spring campaign, in the weeks close to the end of the COVID-19 quarantine, likely associated with the increased time that the occupants spent at home.

Coronavirus disease 2019 (COVID-19) is an acute infectious disease caused by the novel coronavirus (SARS-CoV-2) identified in 2019. The COVID-19 outbreak continues to have devastating consequences for human lives and the global economy. The B-LiFe mobile laboratory in Piedmont, Italy, was deployed for the surveillance of COVID-19 cases by large-scale testing of first responders. The objective was to assess the seroconversion among the regional civil protection (CP), police, health care professionals, and volunteers. The secondary objective was to detect asymptomatic individuals within this cohort in the light of age, sex, and residence. In this paper, we report the results of serological testing performed by the B-LiFe mobile laboratory deployed from 10 June to 23 July 2020. The tests included whole blood finger-prick and serum sampling for detection of SARS-CoV-2 spike receptor-binding domain (S-RBD) antibodies. The prevalence of SARS-CoV-2 antibodies was approximately 5% (294/6013). The results of the finger-prick tests and serum sample analyses showed moderate agreement (kappa = 0.77). Furthermore, the detection rates of serum antibodies to the SARS-CoV-2 nucleocapsid protein (NP) and S-RBD among the seroconverted individuals were positively correlated (kappa = 0.60), at least at the IgG level. Seroprevalence studies based on serological testing for the S-RBD protein or SARS-CoV-2 NP antibodies are not sufficient for diagnosis but might help in screening the population to be vaccinated and in determining the duration of seroconversion.

In Italy, COVID-19 lockdown was imposed from 8 March until 3 May 2020 with negative consequences on the lifestyles and health of people. Within this context, the paper aims: (i) to analyse the impact of COVID-19 lockdown on perceived weight changes; (ii) to evaluate factors associated with the perception of weight changes (Body Mass Index (BMI), sleep quality, time spent in sedentary activities), in an Italian academic community of students and workers. A total of 3666 participants took part in this cross-sectional study (2838 students and 828 workers, of whom 73.0% were female). T -test, Chi-square test and the two-way ANOVA were used. Results showed that 43.3% of participants perceived a weight gain. Workers experienced a more substantial increase in body weight (0.7 kg) compared to students (0.3 kg; p = 0.013). A significant difference between preobese/obese workers (0.9 kg) and students (-0.3 kg; p < 0.001) was found. Overall, 57.0% of the sample was characterized by high levels of sedentary activities. Sedentary people noticed a higher weight gain (0.4 kg) compared to less sedentary people (0.3 kg; p = 0.048). More than 45% of participants reported a worsening of sleep quality and showed a perceived increase in body weight (0.5 kg) in comparison to those who improved their sleep quality (no weight change; p = 0.001). Designing tailored interventions to promote health-related behaviours during lockdown periods is essential.

Background: In the context of the COVID-19 pandemic, there is interest in assessing if per- and polyfluoroalkyl substances (PFAS) exposures are associated with any increased risk of COVID-19 or its severity, given the evidence of immunosuppression by some PFAS. The objective of this paper is to evaluate at the ecological level if a large area (Red Zone) of the Veneto Region, where residents were exposed for decades to drinking water contaminated by PFAS, showed higher mortality for COVID-19 than the rest of the region., Methods: We fitted a Bayesian ecological regression model with spatially and not spatially structured random components on COVID-19 mortality at the municipality level (period between 21 February and 15 April 2020). The model included education score, background all-cause mortality (for the years 2015-2019), and an indicator for the Red Zone. The two random components are intended to adjust for potential hidden confounders., Results: The COVID-19 crude mortality rate ratio for the Red Zone was 1.55 (90% Confidence Interval 1.25; 1.92). From the Bayesian ecological regression model adjusted for education level and baseline all-cause mortality, the rate ratio for the Red Zone was 1.60 (90% Credibility Interval 0.94; 2.51)., Conclusion: In conclusion, we observed a higher mortality risk for COVID-19 in a population heavily exposed to PFAS, which was possibly explained by PFAS immunosuppression, bioaccumulation in lung tissue, or pre-existing disease being related to PFAS.

Pharmacists in the community and the essential requirement to safeguard their own health have become fundamental since the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The aims of this paper were (I) to analyze the directives provided to pharmacists in 2020 regarding preventative safety measures to be adopted; (II) to determine the number of pharmacists who came into contact with SARS-CoV-2 in North-West Italy and relate this to the adopted preventative measures. The first aim was pursued by conducting a bibliographic research, consulting the principal regulatory sources. The second one was achieved with an observational study by administering a questionnaire and performing a serological test. The various protection measures imposed by national and regional legislation were analyzed. Two hundred and eighty-six pharmacists (about 8% of the invited ones) responded to the survey. Ten pharmacists reported a positive result to the serological test. Of the subjects who presented a positive result, three declared that they had not used a hand sanitizer, while two stated that they had not scheduled the cleaning and decontamination of surfaces. Two interviewees had not set up a system of quota restrictions on admissions. In four cases, a certified cleaning company had decontaminated the premises. The results of our study show that during the coronavirus disease 2019 (COVID-19) pandemic, the most pressing challenge for community pharmacists has been the protection of staff and clients inside the pharmacy; the challenge to be faced in the near future will probably be the management of new responsibilities.

The first wave of the COVID-19 pandemic brought about a broader use of masks by both professionals and the general population. This resulted in a severe worldwide shortage of devices and the need to increase import and activate production of safe and effective surgical masks at the national level. In order to support the demand for testing surgical masks in the Italian context, Universities provided their contribution by setting up laboratories for testing mask performance before releasing products into the national market. This paper reports the effort of seven Italian university laboratories who set up facilities for testing face masks during the emergency period of the COVID-19 pandemic. Measurement set-ups were built, adapting the methods specified in the EN 14683:2019+AC. Data on differential pressure (DP) and bacterial filtration efficiency (BFE) of 120 masks, including different materials and designs, were collected over three months. More than 60% of the masks satisfied requirements for DP and BFE set by the standard. Masks made of nonwoven polypropylene with at least three layers (spunbonded-meltblown-spunbonded) showed the best results, ensuring both good breathability and high filtration efficiency. The majority of the masks created with alternative materials and designs did not comply with both standard requirements, resulting in suitability only as community masks. The effective partnering between universities and industries to meet a public need in an emergency context represented a fruitful example of the so-called university "third-mission".

As the Italian health system is regionally based, COVID-19 emergency actions are based on a general lockdown imposed by national authority and then management at local level by 21 regional authorities. Therefore, the pandemic response plan developed by each region led to different approaches. The aim of this paper is to analyze whether differences in patient management may have influenced the local course of the epidemic. The analysis on the 21 Italian regions considers the strategies adopted in terms of hospitalization, treatment in the ICU and at home. Moreover, an in-depth analysis was carried out on: Lombardia, which adopted a hospitalization approach; Veneto, which tended to confine patients at home; and Emilia Romagna, which adopted a mixed hospitalization-home based approach. The majority of regions implemented a home-based approach, while the hospital approach was followed in three regions (Lombardia, Piemonte, and Lazio), mainly limited to the first period of the outbreak. All regions in the later phases tended to reduce hospitalization, preferring to confine patients at home. This comparison, highlighting the different phases of the pandemic, outlined that the adoption of home-based practices contributed to limiting infection rates among patients and health professionals as well as decreasing the number of deaths.

Early known cases of COVID-19 emerged in late 2019 in the city of Wuhan (China) and in a relatively short time, it has reached more than 200 countries up to July 2020. In Italy, from 21 February 2020, (first official Italian positive case of COVID-19) until 27 July 2020, 246,286 confirmed cases were observed of which over 68,150 (28%) needed hospitalization and 35,112 died. In recent scientific research, it has been shown that the severity of symptoms and mortality rates were different not only among the various countries of the world but also in different regions of the same country. This research investigates whether and by how much air environmental conditions (such as exposure to fine particulate matter-PM 2.5 , sea air masses and altitude) influences the risk of hospitalization due to COVID-19 in Italy, once the spreading of the virus and the percentage of the elderly in the population have been accounted for. A log-linear multiple regression model was estimated where the log of the ratio of hospitalized patients per inhabitant, since the beginning of the epidemic up to July 27, has been considered as a dependent variable. Among the independent variables, the ones that have been taken into account are the spreading of the virus, the rate of people over 50 years of age, the concentration of PM 2.5 , the rate of population living by the sea, the rate of green public space for each resident and the ratio of population living at a high altitude. The results showed an increase in the hospitalization rate in terms of the percentage of people over 50 and the average concentration of PM 2.5 . If average limits of PM 2.5 concentration allowed by the current European regulations (25 µg/m 3 ) were respected in all Italian provinces, that would have led to 7339 less hospitalizations for COVID-19 (-11%). On the contrary, near the coast there were lower hospitalized cases in the referred period. In the hypothetical case that no Italians lived near the sea, about 1363 (+2%) more hospitalizations would have been recorded in the analysis period in addition to the effect of a lower PM concentration. This paper wanted to investigate which are the areas with a higher risk of hospitalization in Italy, so as to help the Italian Government to strengthen Health System measures, predicting the most suffering areas and health care systems. According to the results, this is directly related to the severity of symptoms which decreased with the long-time exposure to the sea.

COVID-19 is one of the most important problems for public health, according to the number of deaths associated to this pathology reported so far. However, from the epidemiological point of view, the dimension of the problem is still unknown, since the number of actual cases of SARS-CoV-2 infected people is underestimated, due to limited testing. This paper aims at estimating the actual Infection Fatality Ratio (number of deaths with respect to the number of infected people) and the actual current prevalence (number of infected people with respect to the entire population), both in a specific population and all over the world. With this aim, this paper proposes a method to estimate Infection Fatality Ratio of a still ongoing infection, based on a daily estimation, and on the relationship between this estimation and the number of tests performed per death. The method has been applied using data about COVID-19 from Italy. Results show a fatality ratio of about 0.9%, which is lower than previous findings. The number of actual infected people in Italy is also estimated, and results show that (i) infection started at the end of January 2020; (ii) a maximum number of about 100,000 new cases in one day was reached at the beginning of March 2020; (iii) the estimated cumulative number of infections at the beginning of October 2020 is about 4.2 million cases in Italy (more than 120 million worldwide, if a generalization is conjectured as reasonable). Therefore, the prevalence at the beginning of October 2020 is estimated at about 6.9% in Italy (1.6% worldwide, if a generalization is conjectured).

Temporomandibular disorders are multi-factorial conditions that are caused by both physical and psychological factors. It has been well established that stress triggers or worsens TMDs. This paper looks to present early research, still unfolding, on the relationship between COVID-19 as a major life stressor and TMDs. The main aims of this study were to: investigate the presence of symptoms related to TMDs and the time of onset and the worsening of painful symptoms in relation to the changes in social life imposed by the coronavirus pandemic; and to evaluate the perception of COVID-19 as a major stressful event in subjects who report worsening of painful TMD symptoms. One hundred and eighty-two subjects answered questionnaires-Axis II of the RDC/TMD, the PSS, and specific items about coronavirus as a stressful event-during the lockdown period for COVID-19 in Italy to evaluate the presence of reported symptoms of TMD and the level of depression, somatization, and stress perceived. The results showed that 40.7% of subjects complained about TMD symptoms in the past month. Regarding the time of onset, 60.8% of them reported that facial pain started in the last three months, while 51.4% of these subjects reported that their symptoms worsened in the last month and were related to the aggravation of pain due to the coronavirus lockdown as a major life event and to the stress experienced. The results of this study seem to support the hypothesis that stress during the pandemic lockdown influenced the onset of temporomandibular joint disorders and facial pain, albeit with individual responses.

COVID-19 is likely to be the 2020s' deadliest pandemic according to the World Health Organization (WHO). There have been more than 3.7 million confirmed deaths after 15 months spread. Besides the loss of human lives, COVID-19 has other unfavorable impacts on society, education, and the economy. Due to successive lockdowns and the continuous quarantines, the demands on power resources have reduced. Therefore, there is a need to investigate the impacts of the COVID-19 pandemic on electricity prices (EP). In this paper, a set of six economic factors that are affected by COVID-19 and affect EP are considered. These factors were fed into a functional link artificial neural network (FLANN) to model the relationships between them and the EP. An empirical equation was formulated to help decision makers and strategic developers in the electricity markets come up with more appropriate plans. Italy, the Czech Republic, and China were used as case studies in this research. [ABSTRACT FROM AUTHOR]

This study provides a descriptive analysis of the Lègami/Legàmi service, a free psychological support service in maternal and childcare, offered through the internet and by telephone that was initiated by the Italian Society of Pediatric Psychology (S.I.P.Ped.) during the COVID-19 medical emergency as an act of solidarity, first independently, and then in collaboration with the Italian Ministry of Health. This paper presents findings related to the "universe" of people who called the toll-free service, from the sociocultural characteristics of the users to the information collected by the professionals during the psychological pathways until human satisfaction was achieved. We provide a retrospective description of an experience that took place between April and June 2020, and which involved users of the maternal-infant area calling from the whole Italy. (1) Methods: The aims of this study were to investigate the configuration of the indicators identified and to detect the possible correlations between them in the sample. There were 193 users who took advantage of the Service, 160 of whom continued beyond the reception service; it is this group that we report the findings from here. The tool used was a form reporting access to care and interventions, and the resulting data underwent a content analysis and the indicators were subject to non-parametric statistical analysis to analyze differences and relationships. (2) Results: There were many correlations among the indicators that revealed a high prevalence of calls due to personal motivations and requests for support, which later allowed users to gain a greater understanding of the underlying problems they were facing. The professionals running the service noticed a prevalence of weaknesses attributable to the negative emotions of its users, alongside a presence of cognitive and relational resources. The professionals' interventions, which can be characterized by a prevalence of social support, psychological rehabilitation, and psychoeducation, achieved outcomes of redefining users' relationships with themselves and others. All of the service's users have expressed a high level of satisfaction with it. (3) Discussion: Our results revealed the protective and transformative effects of the service for its users and the underlying importance of having an easily accessible psychological support system in place during emergencies, like the recent pandemic. In conditions like these, the great value of a remote support service should be noted, and despite its limitations, assures its own efficacy when a medical emergency precludes closer in-person forms of psychological assistance. [ABSTRACT FROM AUTHOR]

Prediction models of coronavirus disease utilizing machine learning algorithms range from forecasting future suspect cases, predicting mortality rates, to building a pattern for country-specific pandemic end date. To predict the future suspect infection and death cases, we categorized the approaches found in the literature into: first, a purely data-driven approach, whose goal is to build a mathematical model that relates the data variables including outputs with inputs to detect general patterns. The discovered patterns can then be used to predict the future infected cases without any expert input. The second approach is partially data-driven; it uses historical data, but allows expert input such as the SIR epidemic algorithm. This approach assumes that the epidemic will end according to medical reasoning. In this paper, we compare the purely data-driven and partially-data driven approaches by applying them to data from three countries having different past pattern behavior. The countries are the US, Jordan, and Italy. It is found that those two prediction approaches yield significantly different results. Purely data-driven approach depends totally on the past behavior and does not show any decline in the number of the infected cases if the country did not experience any decline in the number of cases. On the other hand, a partially data-driven approach guarantees a timely decline of the infected curve to reach zero. Using the two approaches highlights the importance of human intervention in pandemic prediction to guide the learning process as opposed to the purely data-driven approach that predicts future cases based on the pattern detected in the data. [ABSTRACT FROM AUTHOR]

Background: Coronavirus Disease 2019 (COVID-19) is the main discussed topic worldwide in 2020 and at the beginning of the Italian epidemic, scientists tried to understand the virus diffusion and the epidemic curve of positive cases with controversial findings and numbers. Objectives: In this paper, a data analytics study on the diffusion of COVID-19 in Lombardy Region and Campania Region is developed in order to identify the driver that sparked the second wave in Italy. Methods: Starting from all the available official data collected about the diffusion of COVID-19, we analyzed Google mobility data, school data and infection data for two big regions in Italy: Lombardy Region and Campania Region, which adopted two different approaches in opening and closing schools. To reinforce our findings, we also extended the analysis to the Emilia Romagna Region. Results: The paper shows how different policies adopted in school opening/closing may have had an impact on the COVID-19 spread, while other factors related to citizen mobility did not affect the second Italian wave. Conclusions: The paper shows that a clear correlation exists between the school contagion and the subsequent temporal overall contagion in a geographical area. Moreover, it is clear that highly populated provinces have the greatest spread of the virus. [ABSTRACT FROM AUTHOR]

Efficiency in stock markets is essential for economic stability and growth. This study investigates the efficiency and herding behavior of the stock markets from the top economies of the world (known as G20 countries). We classify stock market indices using MSCI classification for the developed and emerging markets to provide a comparative examination using the latest data and by employing the robust multifractal detrended fluctuation (MFDFA) method. In addition to the full sample, the analysis uses sub-sample periods to reveal the hidden features and efficiencies of the G20 markets during the Russia–Ukraine War and COVID-19 for the first time. The findings show the availability of varied multifractality among all G20 stock markets during the overall and crisis periods, exhibit long-range correlations, and may support the fractal market hypothesis. In addition, Italy remains the least efficient, while Germany remains the most efficient stock market. The sub-sample results further reveal unevenness in the local fluctuations and resultant higher inefficiency considering the sheer magnitude and impact of crises on the G20 stock markets. However, the efficiency of developed stock markets performed better as compared to emerging markets. The study of G20 stock markets is useful and provides several implications for a wider audience. [ABSTRACT FROM AUTHOR]

Background: Coronavirus disease 2019 (COVID-19) can lead to persistent and debilitating symptoms referred to as Post-Acute sequelae of SARS-CoV-2 infection (PASC) This broad symptomatology lasts for months after the acute infection and impacts physical and mental health and everyday functioning. In the present study, we aimed to evaluate the prevalence and predictors of long-term impairment of working ability in non-elderly people hospitalised for COVID-19. Methods: This cross-sectional study involved 322 subjects hospitalised for COVID-19 from 1 March 2020 to 31 December 2022 in the University Hospital of Bari, Apulia, Italy, enrolled at the time of their hospital discharge and followed-up at a median of 731 days since hospitalization (IQR 466–884). Subjects reporting comparable working ability and those reporting impaired working ability were compared using the Mann-Whitney test (continuous data) and Fisher's test or Chi-Square test (categorical data). Multivariable analysis of impaired working ability was performed using a logistic regression model. Results: Among the 322 subjects who were interviewed, 184 reported comparable working ability (57.1%) and 134 reported impaired working ability (41.6%) compared to the pre-COVID-19 period. Multivariable analysis identified age at hospital admission (OR 1.02, 95% CI 0.99 to 1.04), female sex (OR 1.90, 95% CI 1.18 to 3.08), diabetes (OR 3.73, 95% CI 1.57 to 9.65), receiving oxygen during hospital stay (OR 1.76, 95% CI 1.01 to 3.06), and severe disease (OR 0.51, 95% CI 0.26 to 1.01) as independent predictors of long-term impaired working ability after being hospitalised for COVID-19. Conclusions: Our findings suggest that PASC promotes conditions that could result in decreased working ability and unemployment. These results highlight the significant impact of this syndrome on public health and the global economy, and the need to develop clinical pathways and guidelines for long-term care with specific focus on working impairment. [ABSTRACT FROM AUTHOR]

Thromboprophylaxis/anticoagulation treatment is often required in hospitalized COVID-19 patients. We aimed to estimate the prevalence of major bleeding events in hospitalized COVID-19 patients. This was a retrospective observational study including all COVID-19 hospitalized patients ≥18 years of age at one reference center in northern Italy. The crude prevalence (between February 2020–2022) of major bleeding events was estimated as the number of major bleeding episodes divided by patients at risk. Uni- and multivariable Cox models were built to assess factors potentially associated with major bleeding events. Twenty-nine (0.98%) out of 2,945 COVID-19 patients experienced a major bleeding event [prevalence of 0.55% (95%CI 0.37–0.79)], of which five were fatal. Patients who experienced a major bleeding event were older [78 years (72–84 IQR) vs. 67 years (55–78 IQR), p-value < 0.001] and more frequently exposed to anti-aggregating therapy (44.8% vs. 20.0%, p-value 0.002) when compared to those who did not. In the multivariable Cox model, age [per 1 year more AHR 1.05 (CI95% 1.02–1.09)] was independently associated with an increased risk of major bleeding events. A strict monitoring of older hospitalized COVID-19 patients is warranted due to the risk of major bleeding events. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), still remains a severe threat. At the time of writing this paper, the second infectious wave has caused more than 280,000 deaths all over the world. Italy was one of the first countries involved, with more than 200,000 people reported as infected and 30,000 deaths. There are no specific treatments for COVID-19 and the vaccine still remains somehow inconclusive. The world health community is trying to define and share therapeutic protocols in early and advanced clinical stages. However, numbers remain critical with a serious disease rate of 14%, ending with sepsis, acute respiratory distress syndrome (ARDS), multiple organ failure (MOF) and vascular and thromboembolic findings. The mortality rate was estimated within 2–3%, and more than double that for individuals over 65 years old; almost one patient in three dies in the Intensive Care Unit (ICU). Efforts for effective solutions are underway with multiple lines of investigations, and health authorities have reported success treating infected patients with donated plasma from survivors of the illness, the proposed benefit being protective antibodies formed by the survivors. Plasma transfusion, blood and stem cells, either autologous or allograft transplantation, are not novel therapies, and in this short paper, we propose therapeutic autologous plasma and peripheral blood stem cells as a possible treatment for fulminant COVID-19 infection. [ABSTRACT FROM AUTHOR]