Background Coronavirus SARS-CoV-2, the causative agent of COVID-19, has caused a still evolving global pandemic. Given the worldwide vaccination campaign, the understanding of the vaccine-induced versus COVID-19-induced immunity will contribute to adjusting vaccine dosing strategies and speeding-up vaccination efforts. Methods Anti-spike-RBD IgGs and neutralizing antibodies (NAbs) titers were measured in BNT162b2 mRNA vaccinated participants (n = 250); we also investigated humoral and cellular immune responses in vaccinated individuals (n = 21) of this cohort 5 months post-vaccination and assayed NAbs levels in COVID-19 hospitalized patients (n = 60) with moderate or severe disease, as well as in COVID-19 recovered patients (n = 34). Results We found that one (boosting) dose of the BNT162b2 vaccine triggers robust immune (i.e., anti-spike-RBD IgGs and NAbs) responses in COVID-19 convalescent healthy recipients, while naïve recipients require both priming and boosting shots to acquire high antibody titers. Severe COVID-19 triggers an earlier and more intense (versus moderate disease) immune response in hospitalized patients; in all cases, however, antibody titers remain at high levels in COVID-19 recovered patients. Although virus infection promotes an earlier and more intense, versus priming vaccination, immune response, boosting vaccination induces antibody titers significantly higher and likely more durable versus COVID-19. In support, high anti-spike-RBD IgGs/NAbs titers along with spike (vaccine encoded antigen) specific T cell clones were found in the serum and peripheral blood mononuclear cells, respectively, of vaccinated individuals 5 months post-vaccination. Conclusions These findings support vaccination efficacy, also suggesting that vaccination likely offers more protection than natural infection. Graphical abstract

Objectives Coronavirus disease‐19 (COVID‐19) is associated with various clinical manifestations, ranging from asymptomatic infection to critical illness. The aim of this study is to evaluate the clinical and laboratory characteristics of hospitalised COVID‐19 patients and construct a predictive model for the discrimination of patients at risk of disease progression. Methods A single‐centre cohort study was conducted including consecutively patients with COVID‐19. Demographic, clinical and laboratory findings were prospectively collected at admission. The primary outcome of interest was the intensive care unit admission. A risk model was constructed by applying a Cox's proportional hazard's model with elastic net penalty. Its diagnostic performance was assessed by receiver operating characteristic analysis and was compared with conventional pneumonia severity scores. Results From a total of 67 patients 15 progressed to critical illness. The risk score included patients’ gender, presence of hypertension and diabetes mellitus, fever, shortness of breath, serum glucose, aspartate aminotransferase, lactate dehydrogenase, C‐reactive protein and fibrinogen. Its predictive accuracy was estimated to be high (area under the curve: 97.1%), performing better than CURB‐65, CRB‐65 and PSI/PORT scores. Its sensitivity and specificity were estimated to be 92.3% and 93.3%, respectively, at the optimal threshold of 1.6. Conclusions A10‐variable risk score was constructed based on clinical and laboratory characteristics in order to predict critical illness amongst hospitalised COVID‐19 patients, achieving better discrimination compared with traditional pneumonia severity scores. The proposed risk model should be externally validated in independent cohorts in order to ensure its prognostic efficacy.