The awareness and acceptance of anti-COVID 19 vaccination in adolescence.

BACKGROUND: COVID-19 had devastating effects on children's and adolescents' life, including neuropsychological impairment, discontinuation of social life and education. Since June 2021, antiCOVID19 vaccination has become available to adolescents in Italy up to 12 years and since December 2021 to children aged more than 5 years. The pediatric population represents a challenging target for vaccination. Aim of the study is to perform a survey among adolescents to explore factors associated with COVID 19 immunization and their perceptions about COVID-19 vaccines. METHODS: Italian students aged 10-17 years were invited to participate in an anonymous online survey regarding their immunization against COVID-19 and their opinion on the immunization practice through a web link to the questionnaire. The study period was March-June 2022. Statistical analysis was performed with SPSS v 21. RESULTS: In the study period, 895 students entered the survey. A total of 87.3% of respondents were immunized against SARS-CoV2. The most important predictors of being immunized against SARS-CoV2 were having both parents immunized (p < 0, 001) and being aged over 12 years. In the unvaccinated group, the decision was mostly influenced by the family (65.8%). Regardless the immunization status, respondents were willing to receive information about COVID 19 vaccination mostly by their family doctor (51.8%) and at school (28.9%). CONCLUSIONS: Parents' decisions and attitudes strongly affected the immunization status of adolescents. Students' willing to receive COVID 19 vaccine information by family doctors and at school, underline the potential role of paediatricians and school educators in contributing to an increased vaccine coverage among the paediatric age.

Understanding the vaccine stance of Italian tweets and addressing language changes through the COVID-19 pandemic: Development and validation of a machine learning model.

Social media is increasingly being used to express opinions and attitudes toward vaccines. The vaccine stance of social media posts can be classified in almost real-time using machine learning. We describe the use of a Transformer-based machine learning model for analyzing vaccine stance of Italian tweets, and demonstrate the need to address changes over time in vaccine-related language, through periodic model retraining. Vaccine-related tweets were collected through a platform developed for the European Joint Action on Vaccination. Two datasets were collected, the first between November 2019 and June 2020, the second from April to September 2021. The tweets were manually categorized by three independent annotators. After cleaning, the total dataset consisted of 1,736 tweets with 3 categories (promotional, neutral, and discouraging). The manually classified tweets were used to train and test various machine learning models. The model that classified the data most similarly to humans was XLM-Roberta-large, a multilingual version of the Transformer-based model RoBERTa. The model hyper-parameters were tuned and then the model ran five times. The fine-tuned model with the best F-score over the validation dataset was selected. Running the selected fine-tuned model on just the first test dataset resulted in an accuracy of 72.8% (F-score 0.713). Using this model on the second test dataset resulted in a 10% drop in accuracy to 62.1% (F-score 0.617), indicating that the model recognized a difference in language between the datasets. On the combined test datasets the accuracy was 70.1% (F-score 0.689). Retraining the model using data from the first and second datasets increased the accuracy over the second test dataset to 71.3% (F-score 0.713), a 9% improvement from when using just the first dataset for training. The accuracy over the first test dataset remained the same at 72.8% (F-score 0.721). The accuracy over the combined test datasets was then 72.4% (F-score 0.720), a 2% improvement. Through fine-tuning a machine-learning model on task-specific data, the accuracy achieved in categorizing tweets was close to that expected by a single human annotator. Regular training of machine-learning models with recent data is advisable to maximize accuracy.

How the Italian Twitter Conversation on Vaccines Changed During the First Phase of the Pandemic: A Mixed-Method Analysis.

In the context of the European Joint Action on Vaccination, we analyzed, through quantitative and qualitative methods, a random sample of vaccine-related tweets published in Italy between November 2019 and June 2020, with the aim of understanding how the Twitter conversation on vaccines changed during the first phase of the pandemic, compared to the pre-pandemic months. Tweets were analyzed by a multidisciplinary team in terms of kind of vaccine, vaccine stance, tone of voice, population target, mentioned source of information. Multiple correspondence analysis was used to identify variables associated with vaccine stance. We analyzed 2,473 tweets. 58.2% mentioned the COVID-19 vaccine. Most had a discouraging stance (38.1%), followed by promotional (32.5%), neutral (22%) and ambiguous (2.5%). The discouraging stance was the most represented before the pandemic (69.6%). In February and March 2020, discouraging tweets decreased intensely and promotional and neutral tweets dominated the conversation. Between April and June 2020, promotional tweets remained more represented (36.5%), followed by discouraging (30%) and neutral (24.3%). The tweets' tone of voice was mainly polemical/complaining, both for promotional and for discouraging tweets. The multiple correspondence analysis identified a definite profile for discouraging and neutral tweets, compared to promotional and ambiguous tweets. In conclusion, the emergence of SARS-CoV-2 caused a deep change in the vaccination discourse on Twitter in Italy, with an increase of promotional and ambiguous tweets. Systematic monitoring of Twitter and other social media, ideally combined with traditional surveys, would enable us to better understand Italian vaccine hesitancy and plan tailored, data-based communication strategies.

Intention of Parents to Immunize Children against SARS-CoV-2 in Italy.

Several countries have targeted adolescents for immunization against SARS-CoV-2 to mitigate COVID-19 spread. In Italy, immunization for children ≥ 12 years has been available starting from June 2021. We conducted a cross-sectional study to investigate the knowledge, attitude and intention to vaccinate children < 18 years in Italian families. We used a multinomial logistic regression model to investigate factors associated with intention to vaccinate. We collected a total of 1696 responses. Among the 491 families of children ≥ 12 years, 41.2% would not vaccinate their children and 21.2% were uncertain, while among the 1205 families of children < 12 years, 36.1% would not vaccinate and 33.8% were uncertain. Determinants of intention to vaccinate both age groups were perceived safety and efficacy of vaccines and perceived risk of transmitting infection to adults. For children < 12 years, additional determinants were perceived risk of being infected and being hospitalized because of COVID-19. In view of the expanding strategy to vaccinate adolescents and the availability of immunization for children < 12 years, our results call for a communication strategy targeted at families of children focused on the safety and efficacy of COVID-19 vaccine in children and on the dynamics of infection spread across different age groups. As perceptions in families are volatile and may change rapidly over time, repeated surveys for measuring attitudes to vaccinate would be advisable.

Infodemics: A new challenge for public health.

The COVID-19 information epidemic, or "infodemic," demonstrates how unlimited access to information may confuse and influence behaviors during a health emergency. However, the study of infodemics is relatively new, and little is known about their relationship with epidemics management. Here, we discuss unresolved issues and propose research directions to enhance preparedness for future health crises.

Digital Surveillance Through an Online Decision Support Tool for COVID-19 Over One Year of the Pandemic in Italy: Observational Study.

BACKGROUND: Italy has experienced severe consequences (ie, hospitalizations and deaths) during the COVID-19 pandemic. Online decision support systems (DSS) and self-triage applications have been used in several settings to supplement health authority recommendations to prevent and manage COVID-19. A digital Italian health tech startup, Paginemediche, developed a noncommercial, online DSS with a chat user interface to assist individuals in Italy manage their potential exposure to COVID-19 and interpret their symptoms since early in the pandemic. OBJECTIVE: This study aimed to compare the trend in online DSS sessions with that of COVID-19 cases reported by the national health surveillance system in Italy, from February 2020 to March 2021. METHODS: We compared the number of sessions by users with a COVID-19-positive contact and users with COVID-19-compatible symptoms with the number of cases reported by the national surveillance system. To calculate the distance between the time series, we used the dynamic time warping algorithm. We applied Symbolic Aggregate approXimation (SAX) encoding to the time series in 1-week periods. We calculated the Hamming distance between the SAX strings. We shifted time series of online DSS sessions 1 week ahead. We measured the improvement in Hamming distance to verify the hypothesis that online DSS sessions anticipate the trends in cases reported to the official surveillance system. RESULTS: We analyzed 75,557 sessions in the online DSS; 65,207 were sessions by symptomatic users, while 19,062 were by contacts of individuals with COVID-19. The highest number of online DSS sessions was recorded early in the pandemic. Second and third peaks were observed in October 2020 and March 2021, respectively, preceding the surge in notified COVID-19 cases by approximately 1 week. The distance between sessions by users with COVID-19 contacts and reported cases calculated by dynamic time warping was 61.23; the distance between sessions by symptomatic users was 93.72. The time series of users with a COVID-19 contact was more consistent with the trend in confirmed cases. With the 1-week shift, the Hamming distance between the time series of sessions by users with a COVID-19 contact and reported cases improved from 0.49 to 0.46. We repeated the analysis, restricting the time window to between July 2020 and December 2020. The corresponding Hamming distance was 0.16 before and improved to 0.08 after the time shift. CONCLUSIONS: Temporal trends in the number of online COVID-19 DSS sessions may precede the trend in reported COVID-19 cases through traditional surveillance. The trends in sessions by users with a contact with COVID-19 may better predict reported cases of COVID-19 than sessions by symptomatic users. Data from online DSS may represent a useful supplement to traditional surveillance and support the identification of early warning signals in the COVID-19 pandemic.

Mobile-Health Technologies for a Child Neuropsychiatry Service: Development and Usability of the Assioma Digital Platform.

We developed an m-Health platform to support clinical pathways in a child and adolescent neuropsychiatry unit. The Assioma platform was created for tablets, smartphones and PCs, to support data collection and clinical workflow, to promote constant communication between patients, caregivers and clinicians, and to promote active family involvement in day hospital (DH) procedures. Through the Assioma application for tablets, caregivers filled out an anamnestic questionnaire and explored contents on the DH procedures and neurodevelopmental conditions. The application for smartphones included an agenda function for the DH pathways. Through the application for desktops, clinicians could export anamnestic information in text and Excel formats, send real-time notifications, and push relative contents to families' account. We tested the usability and satisfaction of the Assioma platform in a group of children, caregivers (N = 24) and clinicians (N = 6). Both families and clinicians gave high scores to almost all usability items. The overall satisfaction reached the highest levels at 50% satisfied for families and at 33% for clinicians. Our results indicate that the Assioma platform has the potential to optimize clinical pathways, increasing compliance and clinical efficiency, and to reduce in-person contacts supporting social distancing for clinical pathways, a crucial need during the COVID-19 pandemic.

Restructuring maternal services during the covid-19 pandemic: Early results of a scoping review for non-infected women.

INTRODUCTION AND OBJECTIVE: The novel coronavirus outbreak has caused substantial changes in societal norms as well as adjustments in health systems worldwide. To date the impact of these pandemic-related variations has yet to be fully understood also in the field of maternal health for which continuity of care is a proven life-saving preventive measure. DESIGN: Following the PRISMA guidelines for reviews, a literature search was carried out to assess different approaches that combine quality of maternal care with the imposed social-distancing rules. Nine studies were included in the scoping review. FINDINGS: Reduction of in-person visits is the preferred overall solution. Yet, fewer consultations can still guarantee essential services and appropriate care through integration with telemedicine. Referral to epidemic-free community centres is an alternative option and new paths need to include the interdisciplinary contribution of medical consultants and IT experts, among others. In this context, delaying access for symptomatic expectant mothers is still debated since it carries the potential risk of untimely detection of pregnancy complications. KEY CONCLUSIONS: Preliminary experiences provide an overview of the different attempts put in place to reshape health services to contain the pandemic hazards. IMPLICATIONS FOR PRACTICE: These early prototypes may inspire future innovative health solutions compatible with local resources and specific population preferences and needs.

Potential use of noncoding RNAs and innovative therapeutic strategies to target the 5'UTR of SARS-CoV-2.

After the increasing number of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections all over the world, researchers and clinicians are struggling to find a vaccine or innovative therapeutic strategies to treat this viral infection. The severe acute respiratory syndrome coronavirus infection that occurred in 2002, Middle East respiratory syndrome (MERS) and other more common infectious diseases such as hepatitis C virus, led to the discovery of many RNA-based drugs. Among them, siRNAs and antisense locked nucleic acids have been demonstrated to have effective antiviral effects both in animal models and humans. Owing to the high genomic homology of SARS-CoV-2 and severe acute respiratory syndrome coronavirus (80-82%) the use of these molecules could be employed successfully also to target this emerging coronavirus. Trying to translate this approach to treat COVID-19, we analyzed the common structural features of viral 5'UTR regions that can be targeted by noncoding RNAs and we also identified miRNAs binding sites suitable for designing RNA-based drugs to be employed successfully against SARS-CoV-2.

The G-Quadruplex/Helicase World as a Potential Antiviral Approach Against COVID-19.

G-Quadruplexes (G4s) are non-canonical secondary structures formed within guanine-rich regions of DNA or RNA. G4 sequences/structures have been detected in human and in viral genomes, including Coronaviruses Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) and SARS-CoV-2. Here, we outline the existing evidence indicating that G4 ligands and inhibitors of SARS-CoV-2 helicase may exert some antiviral activity reducing viral replication and can represent a potential therapeutic approach to tackle the COVID-19 pandemic due to SARS-CoV-2 infection. We also discuss how repositioning of FDA-approved drugs against helicase activity of other viruses, could represent a rapid strategy to limit deaths associated with COVID-19 pandemic.