Vaccination is a key strategy to prevent the pandemic caused by the coronavirus disease 2019 (COVID-19). This study aims to investigate the willingness of Chinese adults to be vaccinated against COVID-19 and further explore the factors that may affect their willingness. We used a self-design anonymous questionnaire to conduct an online survey via the Sojump. A total of 1009 valid questionnaires were analyzed. The age of the participants ranged from 18 to 74. Among them, 609 (60.4%, 95%CI: 57.4–63.4%) were willing to receive the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine. Logistic regression analysis results showed that the age of 30–49 (OR = 2.042, 95%CI: 1.098–3.799), universities and colleges education (OR = 1.873, 95% CI = 1.016–3.451), master degree or above education (OR = 1.885, 95%CI = 1.367–2.599), previous influenza vaccination history (OR = 2.176, 95%CI: 1.474–3.211), trust in the effectiveness of the vaccine (OR = 6.419, 95%CI: 3.717–11.086), and close attention to the latest news of the vaccine (OR = 1.601, 95%CI: 1.046–2.449) were facilitative factors that affected their willingness to be vaccinated. More than half of the adults in China would be willing to receive a SARS-CoV-2 vaccine. Middle-aged people with higher education, those who had been vaccinated against influenza, and those who believed that COVID-19 vaccine was effective and paid close attention to it were more willing to be vaccinated. Our findings can provide reference for the implementation of vaccination and the prevention of COVID-19 in China. More studies are needed after the vaccine is launched.

Papain-like protease (PLpro) is important for the replication and transcription of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This study aimed to reveal the PLpro mutations associated with the clinical outcomes of patients. Due to the importance of the S protein in the pathogenicity of SARS-CoV-2, the mutation of the S protein was also analyzed in this study. After downloading the data from the Global Initiative on Sharing Avian Influenza Data (GISAID) database, samples were divided into two groups on the basis of patient status, namely, recovered and dead groups. This study performed a univariate analysis and further explored the association of mutations with patient outcomes through multivariate logistic regression analysis. A total of 138,492 samples were used for analysis. The patients had a mean age of 43.66 ± 21.56 years, and 51.3% of them were female. Multivariate logistic regression results showed that, compared with men, women had a lower risk of dying from coronavirus disease 2019 (COVID-19) (OR = 0.687, 95%CI: 0.638–0.740). Compared with patients aged 17 years and younger, patients aged 18–64 years (OR = 2.864, 95%CI: 1.982–4.139) and patients over 65 years old (OR = 19.135, 95%CI: 13.280–27.572) had a higher risk of death after infection. Compared with the wild type, P78L (OR = 5.185, 95%CI: 2.763–9.730) and K233Q (OR = 5.154, 95%CI: 1.442–18.416) in PLpro were associated with an increased risk of death. A synergistic interaction existed between age and mutations A146D and P78L. The results of the multivariate logistic regression analysis of the data on vaccinated patients demonstrated that, compared with the wild type, the P78L (OR = 3.376, 95%CI: 2.040–5.585) mutation was associated with an increased risk of death. In conclusion, compared with the wild-type PLpro protein, the P78L and K233Q mutations may increase the risk of death in infected individuals. In addition, a synergistic effect existed between age and P78L and K233Q that increased the risk of death in older patients.

This study aims to explore the relationship between the doses of inactivated COVID-19 vaccines received and SARS-CoV-2 Omicron infection in the real-world setting, so as to preliminarily evaluate the protective effect induced by COVID-19 vaccination. We conducted a test-negative case-control study and recruited the test-positive cases and test-negative controls in the outbreak caused by Omicron BA.2 in April 2022 in Guangzhou, China. All the participants were 3 years and older. The vaccination status between the case group and the control group was compared in the vaccinated and all participants, respectively, to estimate the immune protection of inactivated COVID-19 vaccines. After adjusting for sex and age, compared with a mere single dose, full vaccination of inactivated COVID-19 vaccines (OR = 0.191, 95% CI: 0.050 to 0.727) and booster vaccination (OR = 0.091, 95% CI: 0.011 to 0.727) had a more superior protective effect. Compared with one dose, the second dose was more effective in males (OR = 0.090), as well as two doses (OR = 0.089) and three doses (OR = 0.090) among individuals aged 18–59. Whereas, when compared with the unvaccinated, one dose (OR = 7.715, 95% CI: 1.904 to 31.254) and three doses (OR = 2.055, 95% CI: 1.162 to 3.635) could contribute to the increased risk of Omicron infection after adjusting for sex and age. Meanwhile, by contrast with unvaccinated individuals, the result of increased risk was also manifested in the first dose in males (OR = 12.400) and one dose (OR = 21.500), two doses (OR = 1.890), and a booster dose (OR = 1.945) in people aged 18–59. In conclusion, the protective effect of full and booster vaccination with inactivated COVID-19 vaccines exceeded the incomplete vaccination, of which three doses were more effective. Nevertheless, vaccination may increase the risk of Omicron infection compared with unvaccinated people. This may result from the transmission traits of BA.2, the particularity and stronger protection awareness of the unvaccinated population, as well as the ADE effect induced by the decrease of antibody titers after a long time of vaccination. It is crucial to explore this issue in depth for the formulation of future COVID-19 vaccination strategies.

Background: During the COVID-19 pandemic, reducing the case fatality rate (CFR) becomes an urgent goal. Objective: This study explored the effect of vaccination and variants on COVID-19 fatality and provide a basis for the adjustment of control measures. Methods: This study collected epidemiological information on COVID-19 from January to October 2021. By setting different lag times, we calculated the adjusted CFR. The Spearman correlation coefficient and beta regression were used to explore factors that may affect COVID-19 fatality. Results: Every 1% increase in the percentage of full vaccinations may reduce the 3 weeks lagging CFR by 0.66%. Increasing the restrictions on internal movement from level 0 to 1, restrictions on international travel controls from level 2 to 3, and stay-at-home restrictions from level 0 to 2 were associated with an average reduction in 3 weeks lagging CFR of 0.20%, 0.39%, and 0.36%, respectively. Increasing strictness in canceling public events from level 0 to 1 and 2 may reduce the 3 weeks lagging CFR by 0.49% and 0.37, respectively. Increasing the severity of school and workplace closures from level 1 or level 0 to 3 may increase the 3 weeks lagging CFR of 0.39% and 0.83, respectively. Every 1-point increase in the Global Health Security (GHS) index score may increase the 3 weeks lagging CFR by 0.12%. Conclusion: A higher percentage of full vaccinations, higher levels of internal movement restrictions, international travel control restrictions, cancelations of public events, and stay-at-home restrictions are factors that may reduce the adjusted CFR.

In April 2022, a COVID-19 outbreak caused by the Omicron variant emerged in Guangzhou. A case–control study was conducted to explore the relationship between vaccination intervals and SARS-CoV-2 infection in the real world. According to the vaccination dose and age information of the cases, a 1:4 matched case–control sample was established, finally including n = 242 for the case group and n = 968 for the control group. The results indicated that among the participants who received three vaccine doses, those with an interval of more than 300 days between the receipt of the first vaccine dose and infection (or the first contact with a confirmed case) were less likely to be infected with SARS-CoV-2 than those with an interval of less than 300 days (OR = 0.67, 95% CI = 0.46–0.99). After age-stratified analysis, among participants aged 18–40 years who received two doses of vaccine, those who received the second dose more than 30 days after the first dose were less likely to be infected with SARS-CoV-2 (OR = 0.53, 95% CI = 0.30–0.96). Our findings suggest that we need to extend the interval between the first dose and the second dose and further explore the optimal interval between the first and second and between the second and third doses in order to improve vaccine efficacy.