Sequential Administration of SARS-CoV-2 Strains-Based Vaccines Effectively Induces Potent Immune Responses against Previously Unexposed Omicron Strain.

In the past few years, the continuous pandemic of COVID-19 caused by SARS-CoV-2 has placed a huge burden on public health. In order to effectively deal with the emergence of new SARS-CoV-2 variants, it becomes meaningful to further enhance the immune responses of individuals who have completed the first-generation vaccination. To understand whether sequential administration using different variant sequence-based inactivated vaccines could induce better immunity against the forthcoming variants, we tried five inactivated vaccine combinations in a mouse model and compared their immune responses. Our results showed that the sequential strategies have a significant advantage over homologous immunization by inducing robust antigen-specific T cell immune responses in the early stages of immunization. Furthermore, the three-dose vaccination strategies in our research elicited better neutralizing antibody responses against the BA.2 Omicron strain. These data provide scientific clues for finding the optimal strategy within the existing vaccine platform in generating cross-immunity against multiple variants including previously unexposed strains.

Efficacy of online training at the International Mental Health Training Center Taiwan (IMHTCT): Pre and during the COVID-19 pandemic.

STUDY OBJECTIVES: The coronavirus disease 2019 (COVID-19) pandemic has resulted in major disruption to regular learning and training for medical staff. The aim of this study was to compare the learning efficacy between on-site training before the COVID-19 pandemic and online training during the pandemic for nurses, psychologists, social workers, and occupational therapists from Southeast Asia. METHOD: The current study derived data from the International Mental Health Training Center Taiwan (IMHTCT) from 2018 to 2020. IMHTCT Trainees Learning Effect Questionnaire (ITLEQ) scores of the medical staff and demographic variables were collected. Reliability and validity of the ITLEQ were estimated. The independent t-test was used to compare differences in ITLEQ scores between the pre-training and post-training stages among the trainees. In addition, generalized estimating equations were used to estimate the predictive effect of online training on changes in ITLEQ scores over time. FINDINGS: A total of 190 trainees were enrolled, including 92 social workers, 16 occupation therapists, 24 psychologists, and 58 nurses. The reliability and validity were satisfactory. The efficacy of the training programs at IMHTCT was significant for all of the healthcare workers. Furthermore, better training efficacy was found in the social workers and occupational therapists who received online training compared to those who received on-site training. The potential efficacy of online training was found in the nurses. CONCLUSION: Our results demonstrate the importance of online training for mental healthcare workers during the COVID-19 pandemic. Online training may be implemented into regular training courses in the future.

Stability analysis of a nonlocal SIHRDP epidemic model with memory effects.

The prediction and control of COVID-19 is critical for ending this pandemic. In this paper, a nonlocal SIHRDP (S-susceptible class, I-infective class (infected but not hospitalized), H-hospitalized class, R-recovered class, D-death class and P-isolated class) epidemic model with long memory is proposed to describe the multi-wave peaks for the spread of COVID-19. Based on the basic reproduction number R 0 , which is completely controlled by fractional order, the stability of the proposed system is studied. Furthermore, the numerical simulation is conducted to gauge the performance of the proposed model. The results on Hunan, China, reveal that R 0 < 1 suggests that the disease-free equilibrium point is globally asymptotically stable. Likewise, the situation of the multi-peak case in China is presented, and it is clear that the nonlocal epidemic system has a superior fitting effect than the classical model. Finally an adaptive impulsive vaccination is introduced based on the proposed system. Then employing the real data of France, India, the USA and Argentina, parameters identification and short-term forecasts are carried out to verify the effectiveness of the proposed model in describing the case of multiple peaks. Moreover, the implementation of vaccine control is expected once the hospitalized population exceeds 20 % of the total population. Numerical results of France, Indian, the USA and Argentina shed light on the varied effect of vaccine control in different countries. According to the vaccine control imposed on France, no obvious effect is observed even consider reducing human contact. As for India, although there will be a temporary increase in hospitalized admissions after execution of vaccination control, COVID-19 will eventually disappear. Results on the USA have seen most significant effect of vaccine control, the number of hospitalized individuals drops off and the disease is eventually eradicated. In contrast to the USA, vaccine control in Argentina has also been very effective, but COVID-19 cannot be completely eradicated.

Identification of T cell epitopes in BALB/c mice immunized with COVID-19 vaccine

Objective: To identify the T cell epitopes of the COVID-19 vaccine carrying SARS-CoV-2 S, N and M genes in BALB/c mice.

Stability analysis of a nonlocal SIHRDP epidemic model with memory effects

The prediction and control of COVID-19 is critical for ending this pandemic. In this paper, a nonlocal SIHRDP (S-susceptible class, I-infective class (infected but not hospitalized), H-hospitalized class, R-recovered class, D-death class and P-isolated class) epidemic model with long memory is proposed to describe the multi-wave peaks for the spread of COVID-19. Based on the basic reproduction number

A fractional-order SEIHDR model for COVID-19 with inter-city networked coupling effects.

In the end of 2019, a new type of coronavirus first appeared in Wuhan. Through the real-data of COVID-19 from January 23 to March 18, 2020, this paper proposes a fractional SEIHDR model based on the coupling effect of inter-city networks. At the same time, the proposed model considers the mortality rates (exposure, infection and hospitalization) and the infectivity of individuals during the incubation period. By applying the least squares method and prediction-correction method, the proposed system is fitted and predicted based on the real-data from January 23 to March 18 - m where m represents predict days. Compared with the integer system, the non-network fractional model has been verified and can better fit the data of Beijing, Shanghai, Wuhan and Huanggang. Compared with the no-network case, results show that the proposed system with inter-city network may not be able to better describe the spread of disease in China due to the lock and isolation measures, but this may have a significant impact on countries that has no closure measures. Meanwhile, the proposed model is more suitable for the data of Japan, the USA from January 22 and February 1 to April 16 and Italy from February 24 to March 31. Then, the proposed fractional model can also predict the peak of diagnosis. Furthermore, the existence, uniqueness and boundedness of a nonnegative solution are considered in the proposed system. Afterward, the disease-free equilibrium point is locally asymptotically stable when the basic reproduction number R 0 ≤ 1 , which provide a theoretical basis for the future control of COVID-19.