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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]

This paper addresses the problem of describing the spread of COVID-19 by a mathematical model introducing all the possible control actions as prevention (informative campaign, use of masks, social distancing, vaccination) and medication. The model adopted is similar to SEIQR, with the infected patients split into groups of asymptomatic subjects and isolated ones. This distinction is particularly important in the current pandemic, due to the fundamental the role of asymptomatic subjects in the virus diffusion. The influence of the control actions is considered in analysing the model, from the calculus of the equilibrium points to the determination of the reproduction number. This choice is motivated by the fact that the available organised data have been collected since from the end of February 2020, and almost simultaneously containment measures, increasing in typology and effectiveness, have been applied. The characteristics of COVID-19, not fully understood yet, suggest an asymmetric diffusion among countries and among categories of subjects. Referring to the Italian situation, the containment measures, as applied by the population, have been identified, showing their relation with the government's decisions; this allows the study of possible scenarios, comparing the impact of different possible choices. [ABSTRACT FROM AUTHOR]

The World Health Organization (WHO) declared the outbreak of the Coronavirus disease-2019 (COVID-19) infection a pandemic on 11 March 2020. As of the end of October 2020, there were 50 million cases of infection and over one million deaths recorded worldwide, over 45,000 of which occurred in Italy. In Italy, the demand for intensive care over the course of this pandemic crisis has been exceptionally high, resulting in a severe imbalance between the demand for and availability of the necessary resources. This paper focuses on elements of preventive medicine and medical treatments in emergency and non-emergency situations which, based on the international scientific literature, may prove to be useful to physicians on a behavioral level and avert professional liability problems. In order to achieve this objective, we have performed a search on MEDLINE to find published articles related to the risks associated with the pandemic that contain useful suggestions and strategies for mitigating risks and protecting the safety of the population. The results have been collocated in line with these specific study areas. [ABSTRACT FROM AUTHOR]

Given the importance of perceived susceptibility to a disease in adopting preventive behaviors, and the negative impact of optimism bias on prevention, this paper aimed to explore to what extent comparative optimism bias (understood as the tendency to assess a lower probability for oneself to experience negative health events compared to others) is present in the specific context of the Covid-19 pandemic, in two countries with different profiles in terms of the spread of the disease: Italy and Romania. After identifying optimism bias in both countries, we tested whether it depends on respondents' characteristics like gender, age, education, health status and whether or not they have the opportunity to work from home. We surveyed 1126 Romanians and 742 Italians, and found that optimism bias depends on self-reported health status, and that optimism bias increases with age. Inconclusive evidences were found regarding gender and education level, as well as the option to work from home. [ABSTRACT FROM AUTHOR]

This study aimed to explore the impact of the pandemic on medical students' mental health in Italy using a repeated cross-sectional survey with a nested longitudinal subsample (first timepoint: 2018; second: 2020/2021). Three research questions (RQs) were investigated. Study 1 (longitudinal sub-sample) explored whether medical students had higher levels of depressive symptoms and stress during the pandemic compared with a pre-pandemic period (RQ1) and what variables were associated with these conditions during the pandemic adjusting for baseline levels (RQ2). Study 2 (repeated cross-sectional data) aimed to examine whether medical students had higher levels of these conditions during the pandemic compared with their same-year peers during a pre-pandemic period (RQ3). In Study 1, higher levels of depressive symptoms and stress were shown during the pandemic (RQ1). Multivariable models highlighted associations between poor mental health and worsening of the judgment of medical school choice, worsened psychological condition due to the pandemic, economic repercussions due to the pandemic, and baseline levels of symptoms (RQ2). In Study 2, our findings reported higher levels of depressive symptoms and stress during the pandemic, also adjusting for other variables (RQ3). In conclusion, depressive symptoms and stress were greater during the pandemic. The most relevant variables were pandemic-related items and medical school choice judgment. [ABSTRACT FROM AUTHOR]

Italy was dramatically hit by the COVID-19 pandemic, and the province of Brescia was one of the epicenters of the outbreak. Furthermore, Brescia has one of the highest incidences of people living with HIV (PLWH) and a substantial presence of migrants. We conducted a retrospective cohort study involving all citizens connected to the Brescia Health Protection Agency, assessing the SARS-CoV-2 burden, COVID-19 prevalence, and vaccination coverage. A total of 1,004,210 persons were included, 3817 PLWH and 134,492 foreigners. SARS-CoV-2 infection, hospitalizations and death were more frequent among Italians than foreigners. SARS-CoV-2 infections and deaths were more frequent in HIV-uninfected people than in PLWH. PLWH and foreigners were less likely to have a SARS-CoV-2 diagnosis compared to HIV-negative patients. Migrants were more likely to be hospitalized but had a lower risk of death compared to HIV-negative patients. Regarding vaccination, 89.1% of the population received at least one dose of vaccine, while 70.4% of the Italian citizens and 36.3% of the foreigner subjects received three doses of vaccine. Foreigners showed a lower risk of being diagnosed with SARS-CoV-2 but a higher risk of complications. HIV infection was not associated with a higher risk of SARS-CoV-2 severe manifestations compared to the general population. COVID-19 vaccine hesitancy was not different between PLWH and HIV uninfected people, but foreigners were more hesitant. [ABSTRACT FROM AUTHOR]

Simple Summary: People who use drugs represent a category of patients to be prioritized for antiviral treatment for the purpose of hepatitis C elimination, due to their younger age and the major risk of viral transmission, acting as a virus reservoir. The treatment challenges for hepatitis C in this population are related to an ineffective linkage to care, poor adherence to treatment, and follow-up and the risk of re-infection. The COVID-19 pandemic has further exacerbated these conditions, increasing the concerns among clinicians regarding the effectiveness of their treatment. In our study, we describe a novel "patient-tailored" model-of-care for people who use drugs. The antiviral therapy was adapted to the needs of the patient and monitored remotely by a hepatological specialist, in order to decentralize the point of care within the addiction center. The study was conducted before and during the COVID-19 pandemic, clearly demonstrating the model's high effectiveness in the linkage to care, adherence, and response to antiviral therapy. People who use drugs (PWUDs) are generally considered "hard-to-treat" patients, due to adherence to HCV antiviral therapy or re-infection concerns. Linkage-to-care still remains a significant gap for HCV elimination, worsened by the COVID-19 pandemic. To reduce time-to-treat and improve treatment adherence, we have developed a patient-tailored model-of-care, decentralized within the addiction center and supervised remotely by hepatologists. From January 2017 to December 2020, patients were enrolled in one addiction care center in Southern Italy, where a complete hepatologic assessment, including blood chemistry, ultrasound, and transient elastography examination, was provided. DAAs treatment has been adapted on clinical features, also performing a daily administration during an outpatient visit, and monitored remotely by specialists via telemedicine interactions. Adherence was evaluated on the accomplishment of therapy or on the percentage of attended visits. From a total of 690 PWUDs, 135 had an active HCV infection and were enrolled in the study. All patients started the treatment within 3 weeks after HCV diagnosis. Six drop-outs were recorded, obtaining a sustained virological response at week 12 (SVR12) in 98.5% of PWUDs. There were only two cases of treatment failure, one of which is re-infection. No differences were found between the SVR12 rates before and during the COVID-19 pandemic. We obtained a high SVR12 rate, providing a comprehensive assessment within the addiction care center, tailoring the drug administration with a hepatologic remote stewardship. Our therapeutic model should improve the time-to-treat and treatment adherence in PWUDs. [ABSTRACT FROM AUTHOR]

The novel coronavirus disease, also known as COVID-19, is a disease outbreak that was first identified in Wuhan, a Central Chinese city. In this report, a short analysis focusing on Australia, Italy, and UK is conducted. The analysis includes confirmed and recovered cases and deaths, the growth rate in Australia compared with that in Italy and UK, and the trend of the disease in different Australian regions. Mathematical approaches based on susceptible, infected, and recovered (SIR) cases and susceptible, exposed, infected, quarantined, and recovered (SEIQR) cases models are proposed to predict epidemiology in the above-mentioned countries. Since the performance of the classic forms of SIR and SEIQR depends on parameter settings, some optimization algorithms, namely Broyden–Fletcher–Goldfarb–Shanno (BFGS), conjugate gradients (CG), limited memory bound constrained BFGS (L-BFGS-B), and Nelder–Mead, are proposed to optimize the parameters and the predictive capabilities of the SIR and SEIQR models. The results of the optimized SIR and SEIQR models were compared with those of two well-known machine learning algorithms, i.e., the Prophet algorithm and logistic function. The results demonstrate the different behaviors of these algorithms in different countries as well as the better performance of the improved SIR and SEIQR models. Moreover, the Prophet algorithm was found to provide better prediction performance than the logistic function, as well as better prediction performance for Italy and UK cases than for Australian cases. Therefore, it seems that the Prophet algorithm is suitable for data with an increasing trend in the context of a pandemic. Optimization of SIR and SEIQR model parameters yielded a significant improvement in the prediction accuracy of the models. Despite the availability of several algorithms for trend predictions in this pandemic, there is no single algorithm that would be optimal for all cases. [ABSTRACT FROM AUTHOR]

Objectives: We evaluated the effect of the pandemic on the disruption of a persuasive educational antimicrobial stewardship program (ASP) conducted in a university hospital in southern Italy. Methods: In March 2020, the ASP, which began in January 2017 and was carried out at different times in 10 wards, was stopped due to the COVID-19 pandemic. We conducted an observational study with interrupted time series analysis to compare the antibiotic consumption and costs, average length of hospital stay and in-hospital mortality between 12 months before and 9 months after the interruption. Results: Four medical, four surgical wards and two ICUs were included in the study, for a total of 35,921 patient days. Among the medical wards we observed after the interruption a significant increase in fluoroquinolone use, with a change in trend (CT) of 0.996, p = 0.027. In the surgical wards, we observed a significant increase in the overall consumption, with a change in level (CL) of 24.4, p = 0.005, and in the use of third and fourth generation cephalosporins (CL 4.7, p = 0.003). In two ICUs, we observed a significant increase in piperacillin/tazobactam and fluoroquinolone consumption (CT 9.28, p = 0.019, and 2.4, p = 0.047). In the wards with a duration of ASP less than 30 months, we observed a significant increase in antibiotic consumption in the use of piperacillin/tazobactam and fluoroquinolones (CT 12.9, p = 0.022: 4.12, p = 0.029; 1.004, p = 0.011). Conclusions: The interruption of ASP during COVID-19 led to an increase in the consumption of broad-spectrum antibiotics, particularly in surgical wards and in those with a duration of ASP less than 30 months. [ABSTRACT FROM AUTHOR]

Cats are susceptible to infection with severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). Whilst a number of studies have been performed worldwide on owned cats, limited data are available on stray, colony or shelter cats. We investigated SARS-CoV-2 infection in a stray cat population before and during human outbreaks of SARS-CoV-2 in cities in the Lombardy region in northern Italy, a high endemic region for SARS-CoV-2, using serological and molecular methods. A cohort of different samples were collected from 241 cats, including frozen archived serum samples from 136 cats collected before the 2019 coronavirus disease (COVID-19) pandemic and serum, pharyngeal and rectal swab samples from 105 cats collected during the SARS-CoV-2 outbreak. All pre-pandemic samples tested seronegative for antibodies against the nucleocapsid of SARS-CoV-2 using indirect enzyme linked immunosorbent assay (ELISA) test, while one serum sample collected during the pandemic was seropositive. No serological cross-reactivity was detected between SARS-CoV-2 antibodies and antibodies against feline enteric (FECV) and infectious peritonitis coronavirus (FIPC), Feline Immunodeficiency Virus (FIV), Feline Calicivirus (FCV), Feline Herpesvirus-1 (FHV-1), Feline Parvovirus (FPV), Leishmania infantum, Anaplasma phagocytophilum, Rickettsia spp., Toxoplasma gondii or Chlamydophila felis. No pharyngeal or rectal swab tested positive for SARS-CoV-2 RNA on real time reverse transcription-polymerase chain reaction (rRT-PCR). Our data show that SARS-CoV-2 did infect stray cats in Lombardy during the COVID-19 pandemic, but with lower prevalence than found in owned cats. This should alleviate public concerns about stray cats acting as SARS-CoV-2 carriers. [ABSTRACT FROM AUTHOR]

This work uses cognitive network science to reconstruct how experts, influential news outlets and social media perceived and reported the news "COVID-19 is a pandemic". In an exploratory corpus of 1 public speech, 10 influential news media articles on the same news and 37,500 trending tweets, the same pandemic declaration elicited a wide spectrum of perceptions retrieved by automatic language processing. While the WHO adopted a narrative strategy of mitigating the pandemic by raising public concern, some news media promoted fear for economic repercussions, while others channelled trust in contagion containment through semantic associations with science. In Italy, the first country to adopt a nationwide lockdown, social discourse perceived the pandemic with anger and fear, emotions of grief elaboration, but also with trust, a useful mechanism for coping with threats. Whereas news mostly elicited individual emotions, social media promoted much richer perceptions, where negative and positive emotional states coexisted, and where trust mainly originated from politics-related jargon rather than from science. This indicates that social media linked the pandemics to institutions and their intervention policies. Since both trust and fear strongly influence people's risk-averse behaviour and mental/physical wellbeing, identifying evidence for these emotions is key under a global health crisis. Cognitive network science opens the way to unveiling the emotional framings of massively read news in automatic ways, with relevance for better understanding how information was framed and perceived by large audiences. [ABSTRACT FROM AUTHOR]

The ongoing pandemic scenario, due to the coronavirus disease 2019 (COVID-19), has had a considerable impact on public health all over the world. Italy was one of the most affected countries, as the first European full-blown outbreak occurred there. The exposure of the Italian health care workers to COVID-19 may be an important risk factor for psychological distress. The aim of this cross-sectional study was to describe worries and risk perception of being infected among Italian Health Care Workers (HCWs) during the first wave of the pandemic. In total, 2078 HCWs participated in a web survey (78.8% were females). The highest percentage of respondents were physicians (40.75%) and nurses (32.15%), followed by medical (18.00%), health care support (4.50%) and administrative (4.60%) staff. In a score range between 0 (not worried) and 4 (very worried), our results showed that participants declared that they were worried about the Coronavirus infection with a median score of 3 (IQR 2-3) and for 59.19% the risk perception of being infected was very high. In addition, HCWs reported they suffered from sleep disturbances (63.43%). From the analysis of the psychological aspect, a possible divergence emerged between the perceived need for psychological support (83.85%) and the relative lack of this service among health care providers emerged (9.38%). Our findings highlight the importance of psychological and psychiatric support services not only during the COVID-19 pandemic, but also in other emerging infectious diseases (EIDs) scenarios. These services may be useful for health authorities and policymakers to ensure the psychological well-being of health care professionals and to promote precautionary behaviors among them. [ABSTRACT FROM AUTHOR]

COVID-19 (Corona-Virus Disease 2019) in Italy and the measures that were adopted to contain its diffusion had a strong impact on people's quality of life and mental health. The objective of the study was to quantify the psychological impact of the lockdown period on the general Italian population during the two weeks when the COVID-19 emergency in Italy was at its peak. The study (1556 adults) was conducted from April 6th to April 12th, 2020. A survey was developed through Google Forms in order to assess different psychological measures (Self Efficacy, Locus of Control, Social Connectedness, Sense of Virtual Community, Flourishing, Positive and Negative Affect, Life Satisfaction, and Risk Propensity). The results were then compared to reference data. Thelockdown period increased arousal mainly for negative emotions, but also for positive emotions, and quality of life seemed to be reduced. From a psychosocial point of view, while social connectedness has decreased during lockdown, probably because of isolation and social distancing, the virtual social community seemed to increase in the same period. Interestingly, we revealed how self efficacy increased during the lockdown period, and, at the same time, the Locus of control appeared as externalized, and the risk propensity as reduced. The results are discussed considering previous literature, and a coherent theoretical framework is proposed in order to refine the forecasting model for the psychological impact of the lockdown. [ABSTRACT FROM AUTHOR]

The pandemic respiratory disease COVID-19, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), emerged in Wuhan in December 2019 and then spread throughout the world; Italy was the most affected European country. Despite close pet–human contact, little is known about the predisposition of pets to SARS-CoV-2. Among these, felines are the most susceptible. In this study, a domestic cat with clear clinical signs of pneumonia, confirmed by Rx imaging, was found to be infected by SARS-CoV-2 using quantitative RT–qPCR from a nasal swab. This is the first Italian study responding to the request of the scientific community to focus attention on the possible role of pets as a viral reservoir. An important question remains unanswered: did the cat actually die due to SARS-CoV-2 infection? [ABSTRACT FROM AUTHOR]

Until less than two decades ago, all known human coronaviruses (CoV) caused diseases so mild that they did not stimulate further advanced CoV research. In 2002 and following years, the scenario changed dramatically with the advent of the new more pathogenic CoVs, including Severe Acute Respiratory Syndome (SARS-CoV-1), Middle Eastern respiratory syndrome (MERS)-CoV, and the new zoonotic SARS-CoV-2, likely originated from bat species and responsible for the present coronavirus disease (COVID-19), which to date has caused 15,581,007 confirmed cases and 635,173 deaths in 208 countries, including Italy. SARS-CoV-2 transmission is mainly airborne via droplets generated by symptomatic patients, and possibly asymptomatic individuals during incubation of the disease, although for the latter, there are no certain data yet. However, research on asymptomatic viral infection is currently ongoing worldwide to elucidate the real prevalence and mortality of the disease. From a clinical point of view, COVID-19 would be defined as "COVID Planet " because it presents as a multifaceted disease, due to the large number of organs and tissues infected by the virus. Overall, based on the available published data, 80.9% of patients infected by SARS-CoV-2 develop a mild disease/infection, 13.8% severe pneumonia, 4.7% respiratory failure, septic shock, or multi-organ failure, and 3% of these cases are fatal, but mortality parameter is highly variable in different countries. Clinically, SARS-CoV-2 causes severe primary interstitial viral pneumonia and a "cytokine storm syndrome", characterized by a severe and fatal uncontrolled systemic inflammatory response triggered by the activation of interleukin 6 (IL-6) with development of endothelitis and generalized thrombosis that can lead to organ failure and death. Risk factors include advanced age and comorbidities including hypertension, diabetes, and cardiovascular disease. Virus entry occurs via binding the angiotensin-converting enzyme 2 (ACE2) receptor present in almost all tissues and organs through the Spike (S) protein. Currently, SARS-CoV-2 infection is prevented by the use of masks, social distancing, and improved hand hygiene measures. This review summarizes the current knowledge on the main biological and clinical features of the SARS-CoV-2 pandemic, also focusing on the principal measures taken in some Italian regions to face the emergency and on the most important treatments used to manage the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

At the moment of writing, in Italy, there is an ongoing pandemic of coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Its outbreak is leading to severe global socioeconomic disruptions impacting on all economic sectors from tourism, industry and the tertiary sector, up to the operational and opening of public offices, the closure of schools and the organization of families. Measures adopted by the Italian government to deal with the COVID-19 emergency have had direct effects both on people's daily lives and on the activity of most industrial and commercial production companies. These changes have been unequivocally reflected also on the Italian electricity system, which has shown unprecedented behavior in terms of both energy consumption and volume—and subsequently, in the observed share of renewable and conventional production technologies. The goal of this study is to show the impact on the power industry of all the restrictions and lockdown of the activities in Italy and to discuss the effects of COVID-19 outbreak on the bulk power system and the entire electricity sector. In particular, the consequences on load profiles, electricity consumption and market prices in Italy, including the environmental aspects, are examined. [ABSTRACT FROM AUTHOR]

We statistically investigate the Coronavirus Disease 19 (COVID-19) pandemic, which became particularly invasive in Italy in March 2020. We show that the high apparent lethality or case fatality ratio (CFR) observed in Italy, as compared with other countries, is likely biased by a strong underestimation of the number of infection cases. To give a more realistic estimate of the lethality of COVID-19, we use the actual (March 2020) estimates of the infection fatality ratio (IFR) of the pandemic based on the minimum observed CFR and analyze data obtained from the Diamond Princess cruise ship, a good representation of a "laboratory" case-study from an isolated system in which all the people have been tested. From such analyses, we derive more realistic estimates of the real extent of the infection as well as more accurate indicators of how fast the infection propagates. We then isolate the dominant factors causing the abnormal severity of the disease in Italy. Finally, we use the death count—the only data estimated to be reliable enough—to predict the total number of people infected and the interval of time when the infection in Italy could end. [ABSTRACT FROM AUTHOR]