COVID-19 Vaccination Coverage in Italy: How Many Hospitalisations and Related Costs Could Have Been Saved If We Were All Vaccinated?

COVID-19 recognition from chest X-ray images by combining deep learning with transfer learning

Objective

Based on the current research status, this paper proposes a deep learning model named Covid-DenseNet for COVID-19 detection from CXR (computed tomography) images, aiming to build a model with smaller computational complexity, stronger generalization ability, and excellent performance on benchmark datasets and other datasets with different sample distribution features and sample sizes.

Methods

The proposed model first extracts and obtains features of multiple scales from the input image through transfer learning, followed by assigning internal weights to the extracted features through the attention mechanism to enhance important features and suppress irrelevant features; finally, the model fuses these features of different scales through the multi-scale fusion architecture we designed to obtain richer semantic information and improve modeling efficiency.

Results

We evaluated our model and compared it with advanced models on three publicly available chest radiology datasets of different types, one of which is the baseline dataset, on which we constructed the model Covid-DenseNet, and the recognition accuracy on this test set was 96.89%, respectively. With recognition accuracy of 98.02% and 96.21% on the other two publicly available datasets, our model performs better than other advanced models. In addition, the performance of the model was further evaluated on external test sets, trained on data sets with balanced sample distribution (experiment 1) and unbalanced sample distribution (experiment 2), identified on the same external test set, and compared with DenseNet121. The recognition accuracy of the model in experiment 1 and experiment 2 is 80% and 77.5% respectively, which is 3.33% and 4.17% higher than that of DenseNet121 on external test set. On this basis, we also changed the number of samples in experiment 1 and experiment 2, and compared the impact of the change in the number of training set samples on the recognition accuracy of the model on the external test set. The results showed that when the number of samples increased and the sample features became more abundant, the trained Covid-DenseNet performed better on the external test set and the model became more robust.

Conclusion

Compared with other advanced models, our model has achieved better results on multiple datasets, and the recognition effect on external test sets is also quite good, with good generalization performance and robustness, and with the enrichment of sample features, the robustness of the model is further improved, and it has better clinical practice ability.

Strategic vaccination responses to Chikungunya outbreaks in Rome: Insights from a dynamic transmission model

BACKGROUND

Chikungunya virus (CHIKV) outbreaks, driven by the expanding habitat of the Aedes albopictus mosquito and global climate change, pose a significant threat to public health. Our study evaluates the effectiveness of emergency vaccination using a dynamic disease transmission model for a potential large-scale outbreak in Rome, Italy.

METHODS

The model incorporates a susceptible-exposed-infected-recovered (SEIR) framework for human and mosquito populations, taking into account temperature-dependent mosquito lifecycle dynamics, human-mosquito interactions, and various vaccination scenarios.

FINDINGS

Results indicate that emergency vaccination could significantly mitigate the impact of a CHIKV outbreak. Without vaccination, an outbreak is estimated to infect up to 6.21% of Rome's population, equating to approximately 170,762 individuals. Implementing rapid vaccination after detecting the virus in ten individuals and achieving 40% coverage could reduce infection rates by 82%, preventing 139,805 cases. Scenario and sensitivity analyses confirm that even with lower vaccination coverage rates, significant benefits are observed: at 10% coverage, the number of infections drops to 115,231, and at 20% coverage, it further reduces to 76,031. These scenarios indicate prevention of approximately 33% and 55% of infections, respectively.

CONCLUSIONS

The findings highlight the critical role of timely vaccination interventions in outbreak settings, demonstrating that even modest coverage levels can markedly decrease the spread of CHIKV. This study underscores the importance of preparedness, early detection and adaptive response capabilities to manage emerging infectious diseases in urban centres, advocating for strategic vaccine stockpiling and rapid deployment mechanisms to enhance public health outcomes.

A Serological Analysis of the Humoral Immune Responses of Anti-RBD IgG, Anti-S1 IgG, and Anti-S2 IgG Levels Correlated to Anti-N IgG Positivity and Negativity in Sicilian Healthcare Workers (HCWs) with Third Doses of the mRNA-Based SARS-CoV-2 Vaccine: A Retrospective Cohort Study

BACKGROUND

With SARS-CoV-2 antibody tests on the market, healthcare providers must be confident that they can use the results to provide actionable information to understand the characteristics and dynamics of the humoral response and antibodies (abs) in SARS-CoV-2-vaccinated patients. In this way, the study of the antibody responses of healthcare workers (HCWs), a population that is immunocompetent, adherent to vaccination, and continuously exposed to different virus variants, can help us understand immune protection and determine vaccine design goals.

METHODS

We retrospectively evaluated antibody responses via multiplex assays in a sample of 538 asymptomatic HCWs with a documented complete vaccination cycle of 3 doses of mRNA vaccination and no previous history of infection. Our sample was composed of 49.44% males and 50.56% females, with an age ranging from 21 to 71 years, and a mean age of 46.73 years. All of the HCWs' sera were collected from April to July 2022 at the Sant'Elia Hospital of Caltanissetta to investigate the immunologic responses against anti-RBD, anti-S1, anti-S2, and anti-N IgG abs.

RESULTS

A significant difference in age between HCWs who were positive and negative for anti-N IgG was observed. For anti-S2 IgG, a significant difference between HCWs who were negative and positive compared to anti-N IgG was observed only for positive HCWs, with values including 10 (U/mL)-100 (U/mL); meanwhile, for anti-RBD IgG and anti-S1 IgG levels, there was only a significant difference observed for positive HCWs with diluted titers. For the negative values of anti-N IgG, among the titer dilution levels of anti-RBD, anti-S1, and anti-S2 IgG, the anti-S2 IgG levels were significantly lower than the anti-RBD and anti-S1 levels; in addition, the anti-S1 IgG levels were significantly lower than the anti-RBD IgG levels. For the anti-N IgG positive levels, only the anti-S2 IgG levels were significantly lower than the anti-RBD IgG and anti-S1 IgG levels. Finally, a logistic regression analysis showed that age and anti-S2 IgG were negative and positive predictors of anti-N IgG levels, respectively. The analysis between the vaccine type and mixed mRNA combination showed higher levels of antibodies in mixed vaccinated HCWs. This finding disappeared in the anti-N positive group.

CONCLUSIONS

Most anti-N positive HCWs showed antibodies against the S2 domain and were young subjects. Therefore, the authors suggest that including the anti-SARS-CoV-2-S2 in antibody profiles can serve as a complementary testing approach to qRT-PCR for the early identification of asymptomatic infections in order to reduce the impact of potential new SARS-CoV-2 variants. Our serological investigation on the type of mRNA vaccine and mixed mRNA vaccines shows that future investigations on the serological responses in vaccinated asymptomatic patients exposed to previous infection or reinfection are warranted for updated vaccine boosters.

Integration of Cellular and Humoral Immune Responses as an Immunomonitoring Tool for SARS-CoV-2 Vaccination in Healthy and Fragile Subjects

Cellular and humoral immunity are both required for SARS-CoV-2 infection recovery and vaccine efficacy. The factors affecting mRNA vaccination-induced immune responses, in healthy and fragile subjects, are still under investigation. Thus, we monitored the vaccine-induced cellular and humoral immunity in healthy subjects and cancer patients after vaccination to define whether a different antibody titer reflected similar rates of cellular immune responses and if cancer has an impact on vaccination efficacy. We found that higher titers of antibodies were associated with a higher probability of positive cellular immunity and that this greater immune response was correlated with an increased number of vaccination side effects. Moreover, active T-cell immunity after vaccination was associated with reduced antibody decay. The vaccine-induced cellular immunity appeared more likely in healthy subjects rather than in cancer patients. Lastly, after boosting, we observed a cellular immune conversion in 20% of subjects, and a strong correlation between pre- and post-boosting IFN-γ levels, while antibody levels did not display a similar association. Finally, our data suggested that integrating humoral and cellular immune responses could allow the identification of SARS-CoV-2 vaccine responders and that T-cell responses seem more stable over time compared to antibodies, especially in cancer patients.