Spread In Italy Research Articles

Effect of climate change on West Nile virus transmission in Italy: a systematic review

Abstract Background West Nile Virus (WNV) is a pathogen transmitted by mosquitoes of the Culex genus, affecting both humans and animals such as horses and birds. The prevalence of WNV infection has increased dramatically in recent years in Europe, especially in Italy, and has been related to climate and environmental changes linked to global warming. Our review aims to assess the relation between the spread of WNV in Italy and the different climate change-related factors. Methods We conducted a literature search in the PubMed, Web of Science, Embase and Scopus databases, actualised as of 20 February 2024, using as search terms WNV, its vectors and climate change, and limiting our analysis to studies conducted in to avoid an overly heterogeneous setting. We registered the review in PROSPERO (CRD42023430636). Results Of the 252 unique records retrieved, we eventually included 25 articles after full-text screening. These studies were published between 2011 and 2023 and evaluated the distribution of WNV or its vectors according to various climatic and environmental factors. Despite rather heterogeneous results, we found a consistently positive association with temperature, especially spring temperature, agricultural land use, demography, and soil moisture, and a negative one with evapotranspiration and wind, while the relationship with other environmental variables and water cycle-related factors was weak, null or conflicting. Conclusions Despite some inconsistencies in the results, likely related to differences in study methodologies as well as complex environmental interactions, we found unequivocal evidence that climate change-related factors, such as the lengthening of the warm season, extensive farming, and invasive human activities towards natural environments, favour WNV spread in Italy. Consequently, actions against global warming and preservation of natural environments may counteract the WNV epidemic. Key messages • WNV epidemic seems to be linked to specific climate change-related factors, such as the lengthening of the warm season, extensive farming, and invasive human activities towards natural environments. • Actions against global warming and preservation of natural environments may counteract WNV spread.

Sexually Transmitted Infections in Italian Young and Adult People: A Worrying Positive Trend Hidden by COVID-19 Epidemic.

Recent European data show an increase in sexually transmitted infections (STIs), particularly N. gonorrhoeae, among young heterosexuals, surpassing pre-pandemic numbers. Italy's varied local health restrictions during the COVID-19 pandemic likely affected STI management and reporting. To evaluate COVID-19's impact on STI spread in Italy, we analyzed microbiological data from before and during the pandemic in an area with minimal restrictions on clinical services. This retrospective study (2017-2022) included 5503 subjects: 2586 from STI clinics (STD group) and 3687 diagnosed with primary infertility (ART group). Samples were tested for Mycoplasmas/Ureaplasmas, C. trachomatis, N. gonorrhoeae, and T. vaginalis by a multiplex PCR. During the pandemic, overall STI prevalence increased significantly (p < 0.01). U. parvum was the most frequent microorganism in the STD group (26.1% vs. 23.9%), with a notable increase in women (52.1% vs. 32.7%) (p < 0.001). C. trachomatis and M. hominis positive rates decreased significantly (p < 0.001 and p < 0.01, respectively). N. gonorrhoeae cases rose among young people (19-29), predominantly heterosexual, with high ciprofloxacin resistance. In the ART group, U. parvum was the most common infection, particularly in young infertile women (p = 0.01). This study indicates a notable rise in STIs among young people, including heterosexuals, despite social restrictions. The long-term impact of this trend requires further evaluation.

Molecular Detection and Genetic Characterization of Feline Immunodeficiency Virus (FIV) in Seropositive Cats in Northern Italy.

Feline immunodeficiency virus (FIV) is responsible for immunodeficiency syndrome in cats. Several viral subtypes have been identified, each with a variable geographical distribution. To date, the subtype B is known to be the genotype spread in Italy. In this study, the genetic diversity of FIV in northern Italy was assessed by detecting proviral DNA in the blood samples of 50 cats determined to be positive through an anti-FIV antibodies test. These cats were tested using six different PCR assays, and the identified viruses were sequenced and analyzed. Forty-eight cats were confirmed positive, and several FIV subtypes were characterized. As expected, the subtype B was the most commonly observed, and the subtype A was reported for the first time in Italy. Moreover, a new taxon possibly representing an additional FIV subtype was detected, and one virus belonging to subtype B potentially had a recombinant origin. The genetic variability between the FIV viruses that emerged in this study may lead to the potential diagnostic failure of single molecular tests. Therefore, a new diagnostic strategy, which adopts different molecular tests and sequencing, is recommended to monitor the evolution and spread of FIV.

A cohort study on the biochemical and haematological parameters of Italian blood donors as possible risk factors of COVID-19 infection and severe disease in the pre- and post-Omicron period.

To investigate the association between biochemical and blood parameters collected before the pandemic in a large cohort of Italian blood donors with the risk of infection and severe disease. We also focused on the differences between the pre- and post-Omicron spread in Italy (i.e., pre- and post-January 01, 2022) on the observed associations. We conducted an observational cohort study on 13750 blood donors was conducted using data archived up to 5 years before the pandemic. A t-test or chi-squared test was used to compare differences between groups. Hazard ratios with 95% confidence intervals for SARS-CoV-2 infection and severe disease were estimated using Cox proportional hazards models. Subgroup analyses stratified by sex, age and epidemic phase of first infection (pre- and post-Omicron spread) were examined. We confirmed a protective effect of groups B and O, while groups A and AB had a higher likelihood of infection and severe disease. However, these associations were only significant in the pre-Omicron period. We found an opposite behavior after Omicron spread, with the O phenotype having a higher probability of infection. When stratified by variant, A antigen appeared to protect against Omicron infection, whereas it was associated with an increased risk of infection by earlier variants. We were able to stratify for the SARS CoV-2 dominant variant, which revealed a causal association between blood group and probability of infection, as evidenced by the strong effect modification observed between the pre- and post-Omicron spread. The mechanism by which group A acts on the probability of infection should consider this strong effect modification.

The Most Glorified Day: Memory and Narratives on the Normandy Landings in the Italian Daily Press

The article focuses on the memory of the Normandy landings in post-war Italy, as a case study for investigating the wider subject of the memory of World War II in the country. It is based on two main assumptions: that memories of World War II were by no means limited to the national level (i.e. to the aspects of the conflict that directly involved Italy) and necessarily included a representation of the global features of the war and that memories often have a transnational nature and undergo a continuous process of importation and exportation beyond national boundaries. In order to investigate the issue, a corpus of sources has been collected, made up of articles published on five Italian newspapers of different political allegiance, roughly in the first two decades after the war, from 1945 to 1968. The results of the analysis show how the memory of the landings was paid very different attention, depending on the cultural and political stance of the daily: while the right- and left-wing press seldom focused on it, the moderate and pro-governmental newspapers showed a greater interest. Two other key elements emerge from the analysis: the transnational character of the memories and their strongly celebratory nature. In fact, many articles on the D-Day drew upon foreign sources (of Anglo-American, but also of German origin) in different ways, and almost all of them depicted the landings as the turning point of the war, the moment when Europe was freed and the final triumph of the good against the evil. In conclusion, it is outlined how the memory of the landings played a key role in making the global war known, in importing to Italy the (western) idea of the ‘good war’ and in spreading in Italy the ‘western’ set of values, thus strengthening the bonds of the country with its Cold War allies.

Investigation of airborne spread of COVID-19 using a hybrid agent-based model: a case study of the UK.

Agent-based models have been proven to be quite useful in understanding and predicting the SARS-CoV-2 virus-originated COVID-19 infection. Person-to-person contact was considered as the main mechanism of viral transmission in these models. However, recent understanding has confirmed that airborne transmission is the main route to infection spread of COVID-19. We have developed a computationally efficient agent-based hybrid model to study the aerial propagation of the virus and subsequent spread of infection. We considered virus, a continuous variable, spreads diffusively in air and members of populations as discrete agents possessing one of the eight different states at a particular time. The transition from one state to another is probabilistic and age linked. Recognizing that population movement is a key aspect of infection spread, the model allows unbiased movement of agents. We benchmarked the model to recapture the temporal stochastic infection count data of the UK. The model investigates various key factors such as movement, infection susceptibility, new variants, recovery rate and duration, incubation period and vaccination on the infection propagation over time. Furthermore, the model was applied to capture the infection spread in Italy and France.

The impact of COVID-19 pandemic on access to medical services and its consequences on emergency surgery.

On March 9, 2020, the Italian Prime Minister announced the lockdown, which was officially closed on May 4. This extraordinary measure was necessary to contain the COVID-19 pandemic spread in Italy. During this phase, a significant decrease in patients' access to Emergency Department (ED) was observed. Delayed access to treatment determined a delay in the diagnosis of acute surgical conditions, as already documented in other clinical areas, with consequences on surgical outcome and survival. Aim of this study is to provide a detailed description of abdominal urgent-emergent conditions surgically treated and surgical outcomes during the lockdown in a tertiary referral Italian hospital, compared with historical data. A retrospective review of urgent-emergent patients surgically treated in our department was conducted in order to compare patients' characteristics and surgical outcomes during the period March 9th-May 4th, 2020 with the same period of the previous year. 152 patients were included in our study, 79 patients in 2020 group and 77 patients in 2019. We found no significant differences between the groups regarding ASA score, age, gender, and disease prevalence. Significant differences were found in symptom duration before ER access and abdominal pain as the main symptom in non-traumatic conditions. We also performed a sub-analysis on peritonitis which showed significant differences in: hospital length of stay, presence of colostomy vs. ileostomy, and fatal events in 2020. No differences were found in the use of laparoscopy. While the overall number of ER accesses has decreased in 2020 group, the number of patients surgically treated in emergency-urgency conditions has not decreased. However, those patients waited significantly more before the hospital access. This diagnostic delay was associated with a more severe clinical condition and a consequent significantly worse prognosis.

Local and Overall Deviance R-Squared Measures for Mixtures of Generalized Linear Models

In generalized linear models (GLMs), measures of lack of fit are typically defined as the deviance between two nested models, and a deviance-based R2 is commonly used to evaluate the fit. In this paper, we extend deviance measures to mixtures of GLMs, whose parameters are estimated by maximum likelihood (ML) via the EM algorithm. Such measures are defined both locally, i.e., at cluster-level, and globally, i.e., with reference to the whole sample. At the cluster-level, we propose a normalized two-term decomposition of the local deviance into explained, and unexplained local deviances. At the sample-level, we introduce an additive normalized decomposition of the total deviance into three terms, where each evaluates a different aspect of the fitted model: (1) the cluster separation on the dependent variable, (2) the proportion of the total deviance explained by the fitted model, and (3) the proportion of the total deviance which remains unexplained. We use both local and global decompositions to define, respectively, local and overall deviance R2 measures for mixtures of GLMs, which we illustrate—for Gaussian, Poisson and binomial responses—by means of a simulation study. The proposed fit measures are then used to assess, and interpret clusters of COVID-19 spread in Italy in two time points.

Correlations between SARS-CoV-2 Infections and the Number of COVID-19 Vaccine Doses Administered in Three Italian Provinces.

The aim of this ecological study is to evaluate correlations between the number of COVID-19 vaccine doses administered in three Italian provinces-one in the south, one in the center and one in the north of the country-and the registered numbers of COVID-19 cases in the same areas. The period of January 2021-September 2022 was considered, with specific analysis for fractions of times corresponding to the spread in Italy of the different SARS-CoV-2 variants. The results confirm the reduction of the effectiveness of the vaccines in preventing new COVID-19 cases in Italy, regardless of latitude, after the appearance of the first omicron variants. The new variants omicron 4 and 5 showed an extremely high spread during the Italian summer months; fortunately, the effects of the vaccinations in preventing new cases was improved compared to the previous omicron variants, showing a negative correlation between the new COVID-19 cases and the number of vaccine doses administered.

Tracking the progressive spread of the SARS-CoV-2 Omicron variant in Italy, December 2021 to January 2022.

BackgroundThe SARS-CoV-2 variant of concern Omicron was first detected in Italy in November 2021.AimTo comprehensively describe Omicron spread in Italy in the 2 subsequent months and its impact on the overall SARS-CoV-2 circulation at population level.MethodsWe analyse data from four genomic surveys conducted across the country between December 2021 and January 2022. Combining genomic sequencing results with epidemiological records collated by the National Integrated Surveillance System, the Omicron reproductive number and exponential growth rate are estimated, as well as SARS-CoV-2 transmissibility.ResultsOmicron became dominant in Italy less than 1 month after its first detection, representing on 3 January 76.9-80.2% of notified SARS-CoV-2 infections, with a doubling time of 2.7-3.3 days. As of 17 January 2022, Delta variant represented < 6% of cases. During the Omicron expansion in December 2021, the estimated mean net reproduction numbers respectively rose from 1.15 to a maximum of 1.83 for symptomatic cases and from 1.14 to 1.36 for hospitalised cases, while remaining relatively stable, between 0.93 and 1.21, for cases needing intensive care. Despite a reduction in relative proportion, Delta infections increased in absolute terms throughout December contributing to an increase in hospitalisations. A significant reproduction numbers' decline was found after mid-January, with average estimates dropping below 1 between 10 and 16 January 2022.ConclusionEstimates suggest a marked growth advantage of Omicron compared with Delta variant, but lower disease severity at population level possibly due to residual immunity against severe outcomes acquired from vaccination and prior infection.

Predictors for Pulmonary Tuberculosis Outcome and Adverse Events in an Italian Referral Hospital: A Nine-Year Retrospective Study (2013-2021).

Background:The COVID-19 pandemic has undone years of progress in providing essential TB services and controlling the TB burden. Italy, a low TB burden country, has an incidence of 7.1 cases per 100,000 people. To control the TB spreading in Italy is critical to investigate the characteristics of patients with the worst outcomes and the highest risk of adverse events related to antituberculosis therapy. Therefore, we conducted a large retrospective study in TB patients admitted to the Clinic of Infectious Diseases University of Bari, Italy, in order to describe the clinical presentation and the factors associated with adverse events and outcomes.Methods:We retrospectively evaluated the patients admitted to the Clinic of Infectious Diseases from January 2013 to 15 December 2021. We stratified our cohort into two groups: <65 years of age and ≥65 years in order to assess any differences between the two groups. Two logistic regression models were implemented considering the dependent variables as: (I) the adverse events; and (II) the unsuccessful treatments.Results:In total, 206 consecutive patients [60% (n = 124) M, median age 39 years, range 16–92] were diagnosed and admitted with TB at Clinic of Infectious Diseases. Of the whole sample, 151 (74%) were <65 years and 55 (26%) were ≥65. Statistically significant differences between the two groups were detected (p-value < 0.05) for nationality (p-value = 0.01), previous contact with TB patient (p-value = 0.00), type of TB (p-value = 0.00), unsuccessful treatment (p-value = 0.00), length of hospitalization (p-value = 0.02) and diagnostic delay (p-value = 0.01). Adverse events related to TB drug regimen were reported in 24% (n = 49). Age < 65 years (O.R. = 3.91; 95% CI 1.72–4.21), non-Italian nationality (O.R. = 4.45; 95% CI 2.22–4.98.), homeless (O.R. = 3.23; 95% CI 2.58–4.54), presence of respiratory symptoms (O.R. = 1.23; 95% CI 1.10–1.90), diagnostic delay (O.R = 2.55; 95% CI 1.98–3.77) resulted associated with unsuccessful treatment outcome (death, failure or lost to follow up). Finally, age < 65 years (O.R. = 1.73; 95% CI 1.31–2.49), presence of pulmonary TB (O.R. = 1.15; 95% CI 1.02–1.35), length of hospitalization (O.R. = 1.82; 95% CI 1.35–2.57) and TB culture positive (O.R. = 1.35; 95% CI 1.12–1.82) were associated with adverse events in our populations.Conclusions:The pharmacological approach alone seems insufficient to treat and cure a disease whose ethiopathogenesis is not only due to the Mycobacterium tuberculosis, but also to the poverty or the social fragility. Our data suggest that young foreigners, the homeless, and the people with low social and economic status are at higher risk of an unfavorable outcome in low incidence TB countries. Targeted actions to support this highly vulnerable population both in terms of outcome and occurrence of adverse events are needed.

A weighted approach for spatio-temporal clustering of COVID-19 spread in Italy

The SARS-Cov-2 has spread differently over space and time worldwide. By monitoring the contagion’s time evolution, the November 3 2020 the Italian government introduced differentiated regime of restrictions among its regions. This experiment demonstrated that public health policies can be effectively designed by means of clustering. This paper proposes a fuzzy clustering model where spatial and temporal dimensions of the disease spread are optimally weighted. The resulting model is applied with the aim of identifying groups of Italian regions with similar contagion spread. We found that two groups of regions sharing similar patterns of COVID-19 spread over both space and time exist. Appropriate public health policies can be designed on the basis of this evidence.

Modelling the COVID-19 epidemic and the vaccination campaign in Italy by the SUIHTER model.

Several epidemiological models have been proposed to study the evolution of COVID-19 pandemic. In this paper, we propose an extension of the SUIHTER model, to analyse the COVID-19 spreading in Italy, which accounts for the vaccination campaign and the presence of new variants when they become dominant. In particular, the specific features of the variants (e.g. their increased transmission rate) and vaccines (e.g. their efficacy to prevent transmission, hospitalization and death) are modeled, based on clinical evidence. The new model is validated comparing its near-future forecast capabilities with other epidemiological models and exploring different scenario analyses.

Does social distancing affect the processing of brand logos?

Social distancing and isolation have been imposed to contrast the spread of COVID‐19. The present study investigates whether social distancing affects our cognitive system, in particular the processing of different types of brand logos in different moments of the pandemic spread in Italy. In a size discrimination task, six different logos belonging to three categories (letters, symbols, and social images) were presented in their original format and spaced. Two samples of participants were tested: one just after the pandemic spread in Italy, the other one after 6 months. Results showed an overall distancing effect (i.e., spaced stimuli are processed slower than original ones) that interacted with the sample, revealing a significant effect only for participants belonging to the second sample. However, both groups showed a distancing effect modulated by the type of logo as it only emerged for social images. Results suggest that social distancing behaviors have been integrated in our cognitive system as they appear to affect our perception of distance when social images are involved.

A D-vine copula-based quantile regression model with spatial dependence for COVID-19 infection rate in Italy

The main determinants of COVID-19 spread in Italy are investigated, in this work, by means of a D-vine copula based quantile regression. The outcome is the COVID-19 cumulative infection rate registered on October 30th 2020, with reference to the 107 Italian provinces, and it is regressed on some covariates of interest accounting for medical, environmental and demographic factors. To deal with the issue of spatial autocorrelation, the D-vine copula based quantile regression also embeds a spatial autoregressive component that controls for the extent of spatial dependence. The use of vine copula enhances model flexibility accounting for non-linear relationships and tail dependencies. Moreover, the model selection procedure leads to parsimonious models providing a rank of covariates based on their explanatory power with respect to the outcome.

Contratto di rete e codatorialità: il lavoro come fattore di aggregazione e crescita delle medie, piccole e microimprese

The essay examines the spread in Italy of the phenomenon of aggregation between small and medium-sized enterprises and the importance that the co-employment relationships can have as a stimulating factor. From the perspective indicated, the data relating to the use of the network contract are discussed in the first part. In the second part, the technical methods with which co-employment relationships can be established are explored. The conclusions discuss the function that can be performed by the certification of employment contracts.

Using location based social media data to explain COVID-19 spread in Italy

During crisis, social network data is valuable information for authorities and researchers. Since the COVID-19 outbreak, a significant amount of data, including mobility-related data, has been released from various agencies to support studies. This paper aims to check the suitability of mobility-related datasets in describing and predicting new cases, about the spatial dimension during the initial phase of the outbreak. We focus on rich anonymized datasets through Facebook – Data for Good program: colocation matrices, movement matrices and stay-at-home data. However, we also compare their usability with a traditional Origin-Destination matrix. Our test case is Italy, the second country hit after China, where the infection spread irregularly from a few northern provinces to rest of Italy and abroad. Our regression models explain the number of actual new cases at the provincial level (corresponding to NUTS-3) by three groups of variables: active cases proxying local infections, interprovincial mobility proxying the arrival of cases from outside, and the degree of people staying at home proxying infections from cohabitants. The variants among the models consist of different measures of interprovincial mobility, thus allowing us to confront them. The result is the inclusion of time-dependent mobility data improving the significance of model and is effective in explaining irregular rise of cases in different parts of the country. Moreover, colocation results as the best measure. From a policy perspective, results show that mobility restrictions help reduce the geographical spread of infection at the very beginning, but once the outbreak, the interprovincial mobility becomes less relevant.

Timeline of SARS-CoV-2 Spread in Italy: Results from an Independent Serological Retesting.

The massive emergence of COVID-19 cases in the first phase of pandemic within an extremely short period of time suggest that an undetected earlier circulation of SARS-CoV-2 might have occurred. Given the importance of this evidence, an independent evaluation was recommended by the World Health Organization (WHO) to test a subset of samples selected on the level of positivity in ELISA assays (positive, low positive, negative) detected in our previous study of prepandemic samples collected in Italy. SARS-CoV-2 antibodies were blindly retested by two independent centers in 29 blood samples collected in the prepandemic period in Italy, 29 samples collected one year before and 11 COVID-19 control samples. The methodologies used included IgG-RBD/IgM-RBD ELISA assays, a qualitative micro-neutralization CPE-based assay, a multiplex IgG protein array, an ELISA IgM kit (Wantai), and a plaque-reduction neutralization test. The results suggest the presence of SARS-CoV-2 antibodies in some samples collected in the prepandemic period, with the oldest samples found to be positive for IgM by both laboratories collected on 10 October 2019 (Lombardy), 11 November 2019 (Lombardy) and 5 February 2020 (Lazio), the latter with neutralizing antibodies. The detection of IgM and/or IgG binding and neutralizing antibodies was strongly dependent on the different serological assays and thresholds employed, and they were not detected in control samples collected one year before. These findings, although gathered in a small and selected set of samples, highlight the importance of harmonizing serological assays for testing the spread of the SARS-CoV-2 virus and may contribute to a better understanding of future virus dynamics.

The impact of COVID-19 on global stock markets: early linear and non-linear evidence for Italy

The scientific community still struggles to understand the magnitude of the worldwide infections and deaths induced by COVID-19, partly ignoring the financial consequences. In this paper, using the autoregressive fractionally integrated moving average (ARFIMA)—general autoregressive conditional heteroskedasticity (GARCH) model, we quantify and show the impact of the COVID-19 spread in Italy, utilizing data for the stock market. Using information criteria and forecasting accuracy measures, we show that the COVID-19 confirmed cases contribute with statistically significant information to the modeling of volatility, and also increase the forecasting ability of the volatility of the Italian stock market index, leading to a decrease in the mean stock index.

The Geography of Covid-19 Spread in Italy Using Social Media and Geospatial Data Analytics

ABSTRACT Since December 2019, Coronavirus has been spreading rapidly across the world. From its early stages, while the rest of the world was still new to COVID-19, Italy was one of the first and most severely hit countries. The chronic nature of the COVID-19 outbreak and untimely treatments were translated into an unstable social and emotional environment. This study explores the societal response to the first wave of the pandemic outbreak in Italy, looking at geographic variations, social and policy perspectives, and sentiments across northern, central, and southern regions. For this study, we collected nearly three million tweets associated with the coronavirus outbreak in the Italian regions of Lombardy, Lazio, and Sicily, during the months of March-May, 2020. A sentiment analysis was developed to (i) identify the spatio-temporal distribution of the polarity patterns of anger, fear, and joy; (ii) test correlation between these sentiments and COVID-19 cases; and (iii) determine the statistical significance between the sentiments across geographies. Findings provide insights on the relationship between sentiment variations and spatio-temporal case distribution associated with regional differences. These findings align with those of the “Track IT. COVID-19 International Survey,” that investigated the societal response of Italians to the new measures enforced by local governments. Overall, the findings of this study can be weaved together into important implications for emotional wellbeing and economic perspective.