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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]

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.

This paper has explored the impact of the Covid-19 pandemic on the Italian stock market at an industry level, analysing companies listed in the two major stock indexes: MIB 30 and STAR. Using daily firm-level stock prices (from December 2019, until October 31, 2020), we employed an event-study approach to analyse short-term stock market reactions, considering different pandemic windows period. Results showed that stocks reacted negatively to the announcement of the first case in the country, with deep reversal effects when the country was locked down. Monetary policy measures showed potential to ease stock markets: the announcement of Next Generation Agreement highlights the reversed role of Market Capitalization. Firm-specific variables were included in order to make inferences about firm characteristics that emerged as value drivers during the pandemic: in the first lockdown period, a greater company's capitalization ensured a greater resilience to the Covid-19 shock. Reversals at both an industry and a company level are observed. Results allow to understand how an outbreak of contagious disease affects stock returns in various sectors, helping investors to develop trading strategies to protect their wealth from future epidemics and providing inputs into the assessment of economic vulnerability to pandemic crises. [ABSTRACT FROM AUTHOR]

The prediction of the evolution of epidemics plays an important role in limiting the transmissibility and the burdensome consequences of infectious diseases, which leads to the employment of mathematical modeling. In this paper, we propose a stochastic particle filtering extended SEIRS model with repeated vaccination and time-dependent parameters, aiming to efficiently describe the demanding dynamics of time-varying epidemics. The validity of our model is examined using daily records of COVID-19 in Italy for a period of 525 days, revealing a notable capacity to uncover the hidden dynamics of the pandemic. The main findings include the estimation of asymptomatic cases, which is a well-known feature of the current pandemic. Unlike other proposed models that employ extra compartments for asymptomatic cases, which force the estimation of this proportion and significantly increase the model's complexity, our approach leads to the evaluation of the hidden dynamics of COVID-19 without additional computational burden. Other findings that confirm the model's appropriateness and robustness are its parameter evolution and the estimation of more ICU-admitted cases compared to the official records during the most prevalent infection wave of January 2022, attributed to the intensified increase in admissions that may have led to full occupancy in ICUs. As the vast majority of datasets contain time series of total recovered and vaccinated cases, we propose a statistical algorithm to estimate the currently recovered and protected through vaccination cases. This necessity arises from the attenuation of antibodies after vaccination/infection and is necessary for long-time interval predictions. Finally, we not only present a novel stochastic epidemiological model and test its efficiency but also investigate its mathematical properties, such as the existence and stability of epidemic equilibria, giving new insights to the literature. The latter provides additional details concerning the system's long-term behavior, while the conclusions drawn from the R0 index provide perspectives on the severity and future of the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 pandemic has created unprecedented social, financial, and moral disruptions across the globe despite global efforts to reduce the transmission of the novel coronavirus. Currently, vaccinating populations against COVID-19 has emerged as the most sustainable strategy to help countries recover from the socioeconomic effects of COVID-19 while protecting public health. To meet vaccination targets, some countries have adopted policies that rely on varying levels of coercion. This paper analyses the ethical implications of coercive vaccination policies implemented in Singapore and Italy, which impose barriers to accessing healthcare on the unvaccinated. These two cases are compared to the vaccine mandate in New Zealand, which did not restrict access to healthcare for the unvaccinated. This analysis draws on key considerations from Kass' ethical framework for assessing public health intervention. This analysis is relevant to countries considering similar policies to increase vaccination uptake for infectious diseases. Since healthcare is a fundamental good, a critical question is whether imposing barriers to accessing healthcare services is an ethically justifiable consequence of the choice to remain unvaccinated. [ABSTRACT FROM AUTHOR]

COVID-19 is the highly communicable disease which is being announced as pandemic by WHO. The disease has already impacted the economies of big nations like China, Italy, United states etc and now this disease is hitting our developing country i.e. India too. COVID-19 or Corona virus is human to human spread disease and thus social distancing is the only measure which everyone can take to minimize its spread. National lockdown is the measure taken by Government to create distance among people. But what will be the probable impact of this lockdown on different sectors of our country. This paper aims to analyze impact of COVID-19 on the educational sector of India. [ABSTRACT FROM AUTHOR]

The dynamics of human mobility have been known to play a critical role in the spread of infectious diseases like COVID-19. In this paper, we present a simple compact way to model the transmission of infectious disease through transportation networks using widely available aggregate mobility data in the form of a zone-level origin-destination (OD) travel flow matrix. A key feature of our model is that it not only captures the propagation of infection via direct connections between zones (first-order effects) as in most existing studies but also transmission effects that are due to subsequent interactions in the remainder of the system (higher-order effects). We demonstrate the importance of capturing higher-order effects in a simulation study. We then apply our model to study the first wave of COVID-19 infections in (i) Italy, and, (ii) the New York Tri-State area. We use daily data on mobility between Italian provinces (province-level OD data) and between Tri-State Area counties (county-level OD data), and daily reported caseloads at the same geographical levels. Our empirical results indicate substantial predictive power, particularly during the early stages of the outbreak. Our model forecasts at least 85% of the spatial variation in observed weekly COVID-19 cases. Most importantly, our model delivers crucial metrics to identify target areas for intervention. [ABSTRACT FROM AUTHOR]

Coronavirus disease 19 (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus, which is responsible for the ongoing global pandemic. Stringent measures have been adopted to face the pandemic, such as complete lockdown, shutting down businesses and trade, as well as travel restrictions. Nevertheless, such solutions have had a tremendous economic impact. Although the use of recent vaccines seems to reduce the scale of the problem, the pandemic does not appear to finish soon. Therefore, having a forecasting model about the COVID-19 spread is of paramount importance to plan interventions and, then, to limit the economic and social damage. In this paper, we use Genetic Programming to evidence dependences of the SARS-CoV-2 spread from past data in a given Country. Namely, we analyze real data of the Campania Region, in Italy. The resulting models prove their effectiveness in forecasting the number of new positives 10/15 days before, with quite a high accuracy. The developed models have been integrated into the context of SVIMAC-19, an analytical-forecasting system for the containment, contrast, and monitoring of Covid-19 within the Campania Region. [ABSTRACT FROM AUTHOR]

European countries struggled to fight against the second and the third waves of the COVID-19 pandemic, as the Test-Trace-Isolate (TTI) strategy widely adopted over the summer and early fall 2020 failed to contain the spread of the disease effectively. This paper sheds light on the effectiveness of such a strategy in two European countries (Spain and Italy) by analysing data from June to December 2020, collected via a large-scale online citizen survey with 95,251 and 43,393 answers in Spain and Italy, respectively. Our analysis describes several weaknesses in each of the three pillars of the TTI strategy: Test, Trace, and Isolate. We find that 40% of respondents had to wait more than 48 hours to obtain coronavirus tests results, while literature has shown that a delay of more than one day might make tracing all cases inefficient. We also identify limitations in the manual contact tracing capabilities in both countries, as only 29% of respondents in close contact with a confirmed infected individual reported having been contact traced. Moreover, our analysis shows that more than 45% of respondents report being unable to self-isolate if needed. We also analyse the mitigation strategies deployed to contain the second wave of coronavirus. We find that these interventions were particularly effective in Italy, where close contacts were reduced by more than 20% in the general population. Finally, we analyse the participants' perceptions about the coronavirus risk associated with different daily activities. We observe that they are often gender- and age-dependent, and not aligned with the actual risk identified by the literature. This finding emphasises the importance of deploying public-health communication campaigns to debunk misconceptions about SARS-CoV-2. Overall, our work illustrates the value of online citizen surveys to quickly and efficiently collect large-scale population data to support and evaluate policy decisions to combat the spread of infectious diseases, such as coronavirus. [ABSTRACT FROM AUTHOR]

The importance of spatial networks in the spread of an epidemic is an essential aspect in modeling the dynamics of an infectious disease. Additionally, any realistic data-driven model must take into account the large uncertainty in the values reported by official sources such as the amount of infectious individuals. In this paper, we address the above aspects through a hyperbolic compartmental model on networks, in which nodes identify locations of interest such as cities or regions, and arcs represent the ensemble of main mobility paths. The model describes the spatial movement and interactions of a population partitioned, from an epidemiological point of view, on the basis of an extended compartmental structure and divided into commuters, moving on a suburban scale, and non-commuters, acting on an urban scale. Through a diffusive rescaling, the model allows us to recover classical diffusion equations related to commuting dynamics. The numerical solution of the resulting multiscale hyperbolic system with uncertainty is then tackled using a stochastic collocation approach in combination with a finite volume Implicit–Explicit (IMEX) method. The ability of the model to correctly describe the spatial heterogeneity underlying the spread of an epidemic in a realistic city network is confirmed with a study of the outbreak of COVID-19 in Italy and its spread in the Lombardy Region. [ABSTRACT FROM AUTHOR]

Highly contagious diseases, such as mumps, are a global concern as new epidemics continue to emerge, even in highly vaccinated populations. The risk of transmission and spread of these viruses is even higher for individuals who are more likely to be exposed, including healthcare workers (HCWs). In healthcare settings, both HCWs and patients are at risk of infection during the care process, potentially leading to nosocomial epidemic outbreaks. Mumps is often underestimated compared with measles and rubella, despite being milder and less likely to spread. In fact, the risk of complications following mumps infection is extremely high, especially if the disease occurs in adulthood. The measles–mumps–rubella (MMR) vaccine has been shown to be an excellent preventive measure. Unfortunately, the mumps component appears to be less effective in inducing immunity than those for measles and rubella (two-dose effectiveness of 85%, 95% and 97%, respectively). The main aim of our study was to investigate the prevalence of detectable mumps antibodies (serum IgG antibodies) in a cohort of Italian and foreign HCWs in relation to personal and occupational factors. We included in the study 468 subjects who underwent health surveillance at the Occupational Medicine Unit of the Tor Vergata Polyclinic in Rome during the period from January 2021 to March 2023. In our study, the proportion of HCWs found to be unprotected against mumps was very high (8.3%), and those found to be immune are below the WHO threshold for herd immunity (95%). From our data, it seems essential that all occupational health services carry out an accurate screening with a dose of anti-mumps antibodies to assess serological protection before starting a job, regardless of an individual's vaccination history. This approach is proving to be beneficial, accurate, as it allows all serologically non-immune individuals to be vaccinated in the workplace, including those who would be protected by their vaccination history but have lost the antibody response. [ABSTRACT FROM AUTHOR]

Introduction: This paper aims to measure the performance of early detection methods, which are usually used for infectious diseases. Methodology: By using real data of confirmed Coronavirus cases from the Kingdom of Saudi Arabia and Italy, the moving epidemic method (MEM) and the moving average cumulative sums (Mov. Avg Cusum) methods are used in our simulation study. Results: Our results suggested that the CUSUM method outperforms the MEM in detecting the start of the Coronavirus outbreak. [ABSTRACT FROM AUTHOR]

Background: Tdap and flu immunization in pregnancy has been proven to be both effective and safe. Despite this, the vaccination rate in pregnant women is low in Italy. The COVID-19 pandemic has focused the attention of public opinion on communicable diseases, underlining the importance of primary prevention measures such as vaccination. We conducted a survey to investigate the behavior of pregnant women during the COVID-19 pandemic regarding maternal immunization to identify the reasons for vaccine hesitancy in order to overcome them. The new challenge is COVID-19 vaccination in pregnancy, and preliminary data show hesitancy towards it. Our analysis may be useful to improve immunization in the pregnant population, including through the COVID-19 vaccine. Methods: A targeted survey was performed in Italy including 520 women who experienced in the first trimester of pregnancy, prior to the novel coronavirus spread, the 2019–2020 influenza vaccination campaign and the Tdap vaccine recommendation in the third trimester during the COVID pandemic. They represent a unique model to investigate if the new coronavirus outbreak might have changed attitudes towards vaccination in pregnancy in the same patients. Data were collected from a self-completed paper questionnaire. Descriptive statistics were calculated and percentages were compared using the chi-2 test or Fisher's exact test. Results: We obtained data from 195 of the 520 women who gave birth during the inclusion period; 325 cases declined to participate in the survey. A total of 8.7% (17 cases) performed flu vaccination in the first trimester of pregnancy (pre-COVID era), 50.8% (99 cases) accepted Tdap immunization during their third trimester of gestation (COVID-19 pandemic) and 6.7% (13 cases) received both vaccines during pregnancy. For both the flu and Tdap shots, pregnant patients were more likely to accept the vaccines if they were recommended by a healthcare provider, whereas the main reason not to be vaccinated was the lack of such a recommendation. Conclusions: Our survey shows that the COVID-19 experience, which has raised awareness as to the role of vaccines in preventable diseases, may positively change attitudes toward immunization in pregnancy. Vaccination must be recommended to all pregnant women and organized during routine prenatal care as an important element for the prevention of communicable diseases. Vaccination hesitancy can be minimized through consistent recommendation to all pregnant women offered by obstetric staff during routine prenatal care. This approach is likely to be effective in terms of building trust in flu and Tdpa immunization among pregnant women, as well as to avoid unjustified hesitancy towards the more recent COVID-19 vaccines. [ABSTRACT FROM AUTHOR]

We employ a new macroeconomic–epidemiological agent-based model to evaluate the "lives vs livelihoods" trade-off brought to the fore by coronavirus disease (Covid-19). An infectious disease spreads across the network of agents' social and economic contacts and feeds back on the economic dimension of the model through various channels such as employment and consumption demand. Under a lockdown (LD) scenario, the model is able to closely reproduce the epidemiological dynamics of the first wave of the Covid-19 epidemic in the Lombardy region of Italy. We find that, in the presence of non-pharmaceutical interventions, there is no trade-off between lives and macroeconomic losses as a stricter LD eventually leads to superior outcomes along both dimensions. We also evaluate the efficacy of various macroeconomic stabilization policies designed to counteract the downturn generated by the epidemic and LD measures. In an agent-based setting we gain additional insights on the way in which such policies impact not only on gross domestic product but also, for instance, on firms' defaults and relative prices. Liquidity support for firms, a short-time working scheme with compensation for workers, and direct transfer to households stand out as the most effective policy tools. [ABSTRACT FROM AUTHOR]

The present research aimed at understanding individuals' vaccination intentions and protective behaviours against COVID-19 through two different studies. In Study 1 (N = 213, 73% women; mean age = 24.03) the Protection Motivation Theory model was tested considering the fear of COVID-19 as a possible mediator between threat appraisal (in terms of both health and social life) and intentions to get vaccinated when the vaccination was not yet available. Study 2 (N = 1111, 68.9% women; mean age = 38.33) was conducted when the vaccine became available for the entire population. Through this study, by adopting the 5C model of vaccine hesitancy as a theoretical framework, we aimed to understand how people who got vaccinated and the ones who did not differed, considering fear of vaccination, personality and vaccination hesitancy. In Study 1, social limitations, perceived severity and COVID-19 perceived vulnerability were significantly and positively related to fear of COVID-19. Contrary to what we expected, fear of COVID-19 was not a significant predictor of vaccination intention, which was predicted by both response efficacy and self-efficacy. In Study 2, in line with previous studies, vaccine hesitancy was negatively related to vaccination. More specifically, the social-oriented dimension of collective responsibility was the strongest predictor of effective behaviour. Our findings provide insights into the complexity of vaccine acceptance and emphasise the need for targeted interventions to promote vaccination and mitigate the spread of infectious diseases. [ABSTRACT FROM AUTHOR]

We discuss the probable presence of tuberculosis in non-adults of a medieval rural community in northern Italy with a biocultural perspective. Before birth, mother and child have a closely interconnected relationship, as suggested by the role of microchimerism on maternal health. To better understand maternal–child health and related stress factors, paleopathology has investigated the potential of this relationship in recent years. Diseases with environmental development factors such as infections depend on and are strongly affected by maternal control variables such as, for example, breastfeeding and the growth environment. This article presents ISZ2, the second possible case of childhood tuberculosis identified in northeastern Italy through recent paleopathological criteria. The subject is of primary importance as it not only represents a direct testimony of this infectious disease otherwise known only from historical sources, but also increases our knowledge on the state of health of infants in this area, which to date are scarcely analyzed. [ABSTRACT FROM AUTHOR]

Evidences on the absence of risk of sexual transmission of HIV by persons living with HIV/AIDS (PLWHA) with undetectable plasma HIV-RNA (HIV-RNA <200 copies/ml) led to the worldwide campaign "U = U" (undetectable = untransmittable). The purpose of this study was to evaluate the perceived accuracy of this message among PLWHA, HIV-negative people having unprotected sex (PHUS) and infectious diseases' (ID) physicians in Italy. A nationwide survey has been conducted using three different anonymous questionnaires (for ID physicians, PLWHA and PHUS). A total of 1121 participants filled the questionnaires: 397 PLWHA; 90 physicians; 634 PHUS. Awareness of U = U message has been reported in 74%, 92% and 47% of PLWHA, ID physicians and PHUS, respectively. The perception of accuracy of the U = U message among those aware was reported as high in 80.4%, 79.5% and 67.3% of PLWHA, ID physicians and PHUS, respectively. Physicians perceived that 11% of PLWHA have a high rate of perception of U = U, whereas among PLWHA, only 34% reported definitive positive messages from physicians. Discrepancies between awareness and perception of accuracy of the message U = U in PLWHA and physicians have been found, suggesting still low confidence in the community regarding the message itself. [ABSTRACT FROM AUTHOR]

Despite its effectiveness in preventing invasive meningococcal disease (IMD), pediatric uptake of recombinant meningococcal vaccination for serogroup B meningitis (MenB) is low in Italy. This study aimed to investigate knowledge, attitudes, and practice (KAP) about IMD and the vaccine uptake for MenB from July to December 2019, in a sample collected from a series of local Facebook discussion groups from the provinces of Parma and Reggio Emilia (North-Eastern Italy; 337,104 registered users). A self-administered anonymous web-based questionnaire was used to collect demographics, knowledge status, perceived risk for contracting meningitis, attitude towards the utility of meningococcal vaccine, and willingness to receive/perform MenB vaccine in their offspring. In total, 541 parents returned a fully completed questionnaire (response rate of 1.6% of potential recipients), with a mean age of 39.2 years ± 6.3 (78.1% females). Meningococcal infection was identified as severe or highly severe by most participants (88.9%), while it was recognized as being frequent/highly frequent in the general population by 18.6% of respondents. The overall knowledge status was unsatisfactory (57.6% ± 33.6 of correct answers to the knowledge test). Even though 63.4% of participants were somewhat favorable to MenB/MenC vaccines, offspring's vaccination towards MenB was reported by only 38.7% of participants. In a binary logistic regression model, the male gender of respondents (adjusted odds ratio [aOR] 3.184, 95% confidence interval [95%CI] 1.772 to 5.721), living in a municipality >15,000 inhabitants (aOR 1.675, 95%CI 1.051 to 2.668), reporting a favorable attitude on meningococcus B vaccine (aOR 12.472, 95%CI 3.030 to 51.338), having been vaccinated against serogroup B (aOR 5.624, 95%CI 1.936 to 16.337) and/or serogroup C (aOR 2.652, 95%CI 1.442 to 4.872), and having previously vaccinated their offspring against serogroup C meningococcus (aOR 6.585, 95%CI 3.648 to 11.888) were characterized as positive effectors of offspring's vaccination. On the contrary, having a higher risk perception on vaccines was identified as the only negative effector (aOR 0.429, 95%CI 0.241 to 0.765). Our results hint towards extensive knowledge gaps on IMD and preventive interventions in the general population, suggesting that a positive attitude towards vaccines and vaccinations could be identified as the main effector also for MenB acceptance. Interventions in the general population aimed at improving confidence, compliance, and acknowledgment of the collective responsibility, as well as preventing actual constraints and the sharing of false beliefs on infectious diseases and their preventive measures, could therefore increase vaccination acceptance in both targeted individuals and their offspring. [ABSTRACT FROM AUTHOR]

Background: This cross-sectional survey aimed to determine the knowledge, attitudes, and behaviors regarding vaccinations among schoolteachers in Italy. Methods: Data were collected through an online questionnaire from September 2020 to May 2021 from a sample of schoolteachers in the Campania region of southern Italy. Results: Only 27% of the participants had good knowledge about the vaccinations recommended for schoolteachers. Females who had children, who were unsatisfied by their health status, and not needing additional information about vaccinations were more likely to have good knowledge. Moreover, 61.5% perceived that vaccinations are useful in preventing infectious diseases, and 44.5% agreed or strongly agreed that vaccinations should be mandatory for schoolteachers. The results of multivariate logistic regression showed that schoolteachers who perceived that vaccinations are useful in preventing infectious diseases, who considered the recommended vaccinations to be useful to protecting their own and their students' health, who believed that information received about vaccination was useful, and who needed additional information about vaccinations recommended for schoolteachers, were significantly more likely to agree or strongly agree that vaccinations should be mandatory for schoolteachers. Conclusions: These findings suggest the need for education strategies to ensure that schoolteachers are better informed about vaccinations recommended for their profession. [ABSTRACT FROM AUTHOR]

Discontinuation of antimicrobial stewardship programs (ASPs) and increased antibiotic use were described during SARS-CoV-2 pandemic. In order to measure COVID-19 impact on ASPs in a setting of high multidrug resistance organisms (MDRO) prevalence, a qualitative survey was designed. In July 2021, eighteen ID Units were asked to answer a questionnaire about their hospital characteristics, ASPs implementation status before the pandemic and impact of SARS-CoV-2 pandemic on ASPs after the 1st and 2nd pandemic waves in Italy. Nine ID centres (50%) reported a reduction of ASPs and in 7 cases (38.9%) these were suspended. After the early pandemic waves, the proportion of centres that restarted their ASPs was higher among the ID centres where antimicrobial stewardship was formally identified as a priority objective (9/11, 82%, vs 2/7, 28%). SARS-CoV-2 pandemic had a severe impact in ASPs in a region highly affected by COVID-19 and antimicrobial resistance but weaknesses related to the pre-existent ASPs might have played a role. [ABSTRACT FROM AUTHOR]

Tick-borne encephalitis (TBE), a human viral infectious disease caused by the tick-borne encephalitis virus (TBEV), is emerging in Italy, especially in the north-eastern area. No human cases of autochthonous TBE have been reported in Italy's central regions (such as Emilia-Romagna, Italy). However, here we describe the first human case of TBEV infection in this region, pointing to endemic transmission of TBEV, supporting the concept of circulation of TBEV and of the presence of a possible hot spot in the Serramazzoni region in the Emilian Apennines. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) has harshly impacted Italy since its arrival in February 2020. In particular, provinces in Italy's Central and Northern macroregions have dealt with disproportionately greater case prevalence and mortality rates than those in the South. In this paper, we compare the morbidity and mortality dynamics of 16th and 17th century Plague outbreaks with those of the ongoing COVID-19 pandemic across Italian regions. We also include data on infectious respiratory diseases which are presently endemic to Italy in order to analyze the regional differences between epidemic and endemic disease. A Growth Curve Analysis allowed for the estimation of time-related intercepts and slopes across the 16th and 17th centuries. Those statistical parameters were later incorporated as criterion variables in multiple General Linear Models. These statistical examinations determined that the Northern macroregion had a higher intercept than the Southern macroregion. This indicated that provinces located in Northern Italy had historically experienced higher plague mortalities than Southern polities. The analyses also revealed that this geographical differential in morbidity and mortality persists to this day, as the Northern macroregion has experienced a substantially higher COVID-19 mortality than the Southern macroregion. These results are consistent with previously published analyses. The only other stable and significant predictor of epidemic disease mortality was foreign urban potential, a measure of the degree of interconnectedness between 16th and 17th century Italian cities. Foreign urban potential was negatively associated with plague slope and positively associated with plague intercept, COVID-19 mortality, GDP per capita, and immigration per capita. Its substantial contribution in predicting both past and present outcomes provides a temporal continuity not seen in any other measure tested here. Overall, this study provides compelling evidence that temporally stable geographical factors, impacting both historical and current foreign pathogen spread above and beyond other hypothesized predictors, underlie the disproportionate impact COVID-19 has had throughout Central and Northern Italian provinces. • Historical plague and COVID-19 afflicted many of the same present-day Italian regions. • Geography underpins the associations of many predictors of Italian epidemic mortality. • Central and Northern Italian regions are disproportionately afflicted by epidemics. • Social biogeography gives insights into mortality of historical plague and COVID-19. [ABSTRACT FROM AUTHOR]

Background. The COVID-19 pandemic has drastically impacted many aspects of society and has indirectly produced various psychological consequences. This systematic review aimed to estimate the worldwide prevalence of posttraumatic stress disorder (PTSD) in children due to the COVID-19 pandemic, as well as to identify protective or risk factors contributing to child PTSD. Methods. We conducted a systematic literature search in the PubMed, ProQuest, PsycINFO, Embase, Web of Science, WanFang, CNKI, and VIP databases. We searched for studies published between January 1, 2020 and May 26, 2021, that reported the prevalence of child PTSD due to the COVID-19 pandemic, as well as factors contributing to child PTSD. Eighteen studies were included in our systematic review, of which 10 studies were included in the meta-analysis. Results. The estimated prevalence of child PTSD after the COVID-19 outbreak was 28.15% (95% CI: 19.46–36.84%, I 2 = 99.7%). In subgroup analyses for specific regions the estimated prevalence of post-pandemic child PTSD was 19.61% (95% CI: 11.23–27.98%) in China, 50.8% (95% CI: 34.12–67.49%) in the USA, and 50.08% in Italy (95% CI: 47.32–52.84%). Conclusions. Factors contributing to child PTSD were categorized into four aspects: personal factors, family factors, social factors and infectious diseases related factors. Based on this, we presented a new framework summarizing the occurrence and influence of the COVID-19 related child PTSD, which may contribute to a better understanding, prevention and development of interventions for child PTSD in forthcoming pandemics. [ABSTRACT FROM AUTHOR]

Psoriatic patients with latent tuberculosis infection and properly treated active tuberculosis need careful management when prescribing modern biological drugs. Although data and guidelines regarding tumour necrosis factor-α inhibitors advise caution and initiation of prophylactic therapy in patients with latent tuberculosis infection, the same indications do not seem to find equal force for interleukin (IL)-23 and IL-17 inhibitors. In order to evaluate the risk of reactivation in patients with latent tuberculosis infection or properly treated active tuberculosis, an observational retrospective study was conducted on the population referred to our centre at Dermatologic Clinic of University of Turin, Italy. In the last 10 years at the clinic 19 psoriatic patients were found to be at risk of tuberculosis reactivation: 10 patients were QuantiFERON-TB-positive at baseline, 2 became positive during treatment, 6 reported prior tuberculous infection, and 1 was QuantiFERON-TB-negative at baseline and developed disseminated tuberculosis during treatment with anti-tumour necrosis factor-α. Overall, 10.5% of this group of patients developed active tuberculosis; however, stratifying by biologic therapy, zero cases were observed among patients treated with anti-IL-17, -23, or -12/23 over a relatively long followup (48.1 months) A review of the available literature following our experience confirms the increased risk of tuberculosis reactivation with tumour necrosis factor-α inhibitors. Concerning anti-IL-23 and IL-17 drugs, available data showed high safety in patients at risk of tuberculosis reactivation. Screening of patients who should be taking IL-17 and IL-23 inhibitors is recommended for public health purposes. In case of a positive result with these therapies, consulting with an infectious diseases specialist is suggested in order to weigh up the risks and benefits of prophylactic treatment. [ABSTRACT FROM AUTHOR]

In this article, we describe the advances in the field of pediatrics that have been published in the Italian Journal of Pediatrics in 2020. We report progresses in understanding allergy, autoinflammatory disorders, critical care, endocrinology, genetics, infectious diseases, microbiota, neonatology, neurology, nutrition, orthopedics, respiratory tract illnesses, rheumatology in childhood. [ABSTRACT FROM AUTHOR]

A discrete-time deterministic epidemic model is proposed to better understand the contagious dynamics and the behaviour observed in the incidence of real infectious diseases. For this purpose, we analyse a SIRS model both in a random-mixing approach and in a small-world network formulation. The models include the basic parameters that characterize an epidemic: infection and recovery times, as well as mechanisms of contagion. Depending on the parameters, the random-mixing model has different types of behaviour of an epidemic: pathogen extinction; endemic infection; sustained oscillations and dynamic extinction. Spatial effects are included in our network-based approach, where each individual of a population is represented by a node of a small-world network. Our network-based approach includes rewiring connections to account for time-varying network structure, a consequence of the natural response to the emergence of an epidemic (e.g. avoiding contacts with infected individuals). Random and adaptive rewiring conditions are analysed and numerical simulation are made. A comparison of model predictions with the actual effects of COVID-19 infection on population that occurred in Italy and France is produced. Results of the time series of infected people show that our adaptive evolving networks represent effective strategies able to decrease the epidemic spreading. [ABSTRACT FROM AUTHOR]

For epidemics such as COVID-19, with a significant population having asymptomatic, untested infection, model predictions are often not compatible with data reported only for the cases confirmed by laboratory tests. Additionally, most compartmental models have instantaneous recovery from infection, contrary to observation. Tuning such models with observed data to obtain the unknown infection rate is an ill-posed problem. Here, we derive from the first principle an epidemiological model with delay between the newly infected (N) and recovered (R) populations. To overcome the challenge of incompatibility between model and case data, we solve for the ratios of the observed quantities and show that log(N(t)/R(t)) should follow a straight line. This simple prediction tool is accurate in hindcasts verified using data for China and Italy. In traditional epidemiology, an epidemic wanes when much of the population is infected so that 'herd immunity' is achieved. For a highly contagious and deadly disease, herd immunity is not a feasible goal without human intervention or vaccines. Even before the availability of vaccines, the epidemic was suppressed with social measures in China and South Korea with much less than 5% of the population infected. Effects of social behaviour should be and are incorporated in our model. [ABSTRACT FROM AUTHOR]