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Achieving high levels of vaccination coverage against COVID-19 may be hindered by vaccine hesitancy. We quantified over time the prevalence of COVID-19 vaccine hesitancy among university students, investigated its determinants, and analyzed student attitudes, risk perceptions and compliance with preventive measures. The survey was administered online from 1 March to 30 June 2021. A multivariable logistic regression model was built to identify predictors of hesitancy. Overall, we collected 5369 questionnaires that were grouped into three survey periods (March, April–May, and May–June). The response rate ranged from 81.2% to 76.4%, whereas vaccine hesitancy ranged from 22% to 29%. Multivariable analysis showed that April–May participants had higher odds of hesitancy than March respondents. Other positive predictors were being male, not being a healthcare student, having a lower academic level, and not disclosing a political position. Conversely, higher levels of perceived COVID-19 severity, concern for the emergency, confidence in vaccine safety and effectiveness, and self-reported adherence to mask wearing indoors and outdoors were negatively associated with hesitancy. We found that vaccine hesitancy changed over time and in relation to several factors. Strategies aimed at increasing the students’ awareness and engagement, restoring confidence in health authorities, and limiting disinformation around the vaccines should be devised.

Introduction A precise estimate of the frequency and severity of SARS‐CoV‐2 reinfections would be critical to optimize restriction and vaccination policies for the hundreds of millions previously infected subjects. We performed a meta‐analysis to evaluate the risk of reinfection and COVID‐19 following primary infection. Methods We searched MedLine, Scopus and preprint repositories for cohort studies evaluating the onset of new infections among baseline SARS‐CoV‐2‐positive subjects. Random‐effect meta‐analyses of proportions were stratified by gender, exposure risk, vaccination status, viral strain, time between episodes, and reinfection definition. Results Ninety‐one studies, enrolling 15,034,624 subjects, were included. Overall, 158,478 reinfections were recorded, corresponding to a pooled rate of 0.97% (95% CI: 0.71%–1.27%), with no substantial differences by definition criteria, exposure risk or gender. Reinfection rates were still 0.66% after ≥12 months from first infection, and the risk was substantially lower among vaccinated subjects (0.32% vs. 0.74% for unvaccinated individuals). During the first 3 months of Omicron wave, the reinfection rates reached 3.31%. Overall rates of severe/lethal COVID‐19 were very low (2–7 per 10,000 subjects according to definition criteria) and were not affected by strain predominance. Conclusions A strong natural immunity follows the primary infection and may last for more than one year, suggesting that the risk and health care needs of recovered subjects might be limited. Although the reinfection rates considerably increased during the Omicron wave, the risk of a secondary severe or lethal disease remained very low. The risk–benefit profile of multiple vaccine doses for this subset of population needs to be carefully evaluated.

In this paper we study the semi-active control of structural systems by means of variable stiffness devices, and the resulting non-linear dynamics of the controlled system. In particular, the study is applied to base-excited single-degree-of-freedom systems, controlled by variable ON–OFF elastic devices and subjected to simple motion conditions: free vibrations following initial conditions and stationary response to harmonic input. A performance index, which, in the case of base-excited structures, assumes an original dual formulation following the relative or absolute approaches, has been proposed for instantaneous optimal control. The study includes both the optimal control design and the observation of the resulting non-linear dynamics of the controlled system. The dynamic behavior of the controlled non-linear system is studied with respect to the parameters which characterize the algorithms, the control device, and the input. In this way, also by means of significant (and original) analytical solutions of the equations of motion, the optimal formulation of the algorithms, some particular behaviors of the controlled systems, and the performances of the control approach are shown and explained for the different control strategies.