Mathematical modeling of vaccination as a control measure of stress to fight COVID-19 infections

The world experienced the life-threatening COVID-19 disease worldwide since its inversion. The whole world experienced difficult moments during the COVID-19 period, whereby most individual lives were affected by the disease socially and economically. The disease caused millions of illnesses and hundreds of thousands of deaths worldwide. To fight and control the COVID-19 disease intensity, mathematical modeling was an essential tool used to determine the potentiality and seriousness of the disease. Due to the effects of the COVID-19 disease, scientists observed that vaccination was the main option to fight against the disease for the betterment of human lives and the world economy. Unvaccinated individuals are more stressed with the disease, hence their body's immune system are affected by the disease. In this study, the SVEIHR deterministic model of COVID-19 with six compartments was proposed and analyzed. Analytically, the next-generation matrix method was used to determine the basic reproduction number (R0). Detailed stability analysis of the no-disease equilibrium (E0) of the proposed model to observe the dynamics of the system was carried out and the results showed that E0 is stable if R0<1 and unstable when R0>1. The Bayesian Markov Chain Monte Carlo (MCMC) method for the parameter identifiability was discussed. Moreover, the sensitivity analysis of R0 showed that vaccination was an essential method to control the disease. With the presence of a vaccine in our SVEIHR model, the results showed that R0=0.208, which means COVID-19 is fading out of the community and hence minimizes the transmission. Moreover, in the absence of a vaccine in our model, R0=1.7214, which means the disease is in the community and spread very fast. The numerical simulations demonstrated the importance of the proposed model because the numerical results agree with the sensitivity results of the system. The numerical simulations also focused on preventing the disease to spread in the community. © 2022 The Authors

Bayesian quantile forecasting via the realized hysteretic GARCH model

This research introduces a new model, a realized hysteretic GARCH, that is similar to a three-regime nonlinear framework combined with daily returns and realized volatility. The setup allows the mean and volatility switching in a regime to be delayed when the hysteresis variable lies in a hysteresis zone. This nonlinear model presents explosive persistence and high volatility in Regime 1 in order to capture extreme cases. We employ the Bayesian Markov chain Monte Carlo (MCMC) procedure to estimate model parameters and to forecast volatility, value at risk (VaR), and expected shortfall (ES). A simulation study highlights the properties of the proposed MCMC methods, as well as their accuracy and satisfactory performance as quantile forecasting tools. We also consider two competing models, the realized GARCH and the realized threshold GARCH, for comparison and carry out Bayesian risk forecasting via predictive distributions on four stock markets. The out-of-sample period covers the recent 4 years by a rolling window approach and includes the COVID-19 pandemic period. Among the realized models, the realized hysteretic GARCH model outperforms at the 1% level in terms of violation rates and backtests.

Comparison of maintenance of intravesical BCG therapy between 1 year and more than 1 year therapies for recurrence rate in high-risk NMIBC: Systematic review and network meta-analysis according to EAU COVID-19 recommendation

Introduction & Objectives: During coronavirus disease 2019 (COVID-19) pandemic, EAU recommended intravesical bacillus Calmette-Guérin (BCG) therapy courses that have been ongoing for longer than 1 year can be safely terminated for high-risk non-muscle-invasive bladder cancer(NMIBC) patients. Thus, we conducted a systematic review and network meta-analysis according to EAU COVID-19 recommendation.Materials & Methods: Systematic review was performed following the PRISMA guideline. PubMed/Medline, EMBASE, and Cochrane Library weresearched up to Sep, 2021. We conducted a network meta-analysis to outcomes including only induction therapy group (No_M), 1-year (M1) andmore than 1 year (MM1) maintenance therapies groups for recurrence rate in patients with NMIBC. Participants, patients with NMIBC;Interventions,NMIBC patients who underwent intravesical BCG therapy;Outcomes, comparison of recurrence rate included for analysis. Quality assessmentswere performed independently by two reviewers using the Scottish Intercollegiate Guidelines Network.Results: Nineteen studies with a total of 3,957 patients were included for network meta-analysis. 19 studies were intermediate risk for detectionbias and there were no major problems. There was just two published studies between M1 and MM1. Five studies between No_M and M1 and 12articles between No_M and MM1 were identified. In node-split forest plot using Bayesian Markov Chain Monte Carlo (MCMC) modeling, there couldbe no difference between M1 and MM1 in recurrence rate (OR 0.95 (0.73-1.2)). However, recurrence rate in No_M group was higher than M1 (OR1.9 (1.5-2.5)) and MM1 (OR 2.0 (1.7-2.4)) groups (Fig. 1A). P-score test using frequentist method to rank treatments in network demonstrate MM1(P-score 0.8701) was superior to M1 (P-score 0.6299) and No_M groups (P-score 0). In the rank-probability test using MCMC modeling, MM1 had the highest rank, followed by M1 and No_M groups (Fig. 1B). (Figure Presented)(Figure Presented)Conclusions: In network meta-analysis, there could be no difference between 1-year and more than 1-year maintenance intravesical BCGtherapies in recurrence rate. In the rank test, more than 1-year therapy could be most effective. During COVID-19 pandemic, 1-year maintenancetherapy can be performed, however, after the COVID-19 pandemic, more than 1-year therapy will be decided

Sensitivity of SARS-CoV-2 Life Cycle to IFN Effects and ACE2 Binding Unveiled with a Stochastic Model.

Mathematical modelling of infection processes in cells is of fundamental interest. It helps to understand the SARS-CoV-2 dynamics in detail and can be useful to define the vulnerability steps targeted by antiviral treatments. We previously developed a deterministic mathematical model of the SARS-CoV-2 life cycle in a single cell. Despite answering many questions, it certainly cannot accurately account for the stochastic nature of an infection process caused by natural fluctuation in reaction kinetics and the small abundance of participating components in a single cell. In the present work, this deterministic model is transformed into a stochastic one based on a Markov Chain Monte Carlo (MCMC) method. This model is employed to compute statistical characteristics of the SARS-CoV-2 life cycle including the probability for a non-degenerate infection process. Varying parameters of the model enables us to unveil the inhibitory effects of IFN and the effects of the ACE2 binding affinity. The simulation results show that the type I IFN response has a very strong effect on inhibition of the total viral progeny whereas the effect of a 10-fold variation of the binding rate to ACE2 turns out to be negligible for the probability of infection and viral production.

Clinical Effectiveness of REGN-COV2 in Patients with COVID-19 in Japan: A Retrospective Cohort Study with a Bayesian Inference.

BACKGROUND: Neutralizing antibody cocktail therapy, REGN-COV2, is promising in preventing a severe form of coronavirus disease 2019 (COVID-19), but its effectiveness in Japan has not been fully investigated. MATERIALS AND METHODS: To evaluate the effectiveness of REGN-COV2, clinical data of 20 patients with COVID-19 who received REGN-COV2 was compared with the control by matching age and sex. The primary outcome was the time from the onset to defervescence, the duration of hospitalization, and oxygen requirement. A sensitivity analysis using Bayesian analysis was also conducted. RESULTS: The time to defervescence was significantly shorter in the treatment group (5.25 vs. 7.95 days, P = 0.02), and so was the duration of hospitalization (7.115 vs. 11.45, P = 0.0009). However, the oxygen therapy requirement did not differ between the two groups (15% vs. 35%, P = 0.27). For Bayesian analysis, the median posterior probability of the time to defervescence since the symptom onset on the REGN-COV2 group was 5.28 days [95% credible interval (CrI): 4.28 - 6.31 days], compared with the control of 7.99 days (95% CrI: 6.81 - 9.24 days). The posterior probability of the duration of the hospitalization on the REGN-COV2 group was 7.17 days (95% CrI: 5.99 - 8.24 days), compared with the control of 11.54 days (95% CrI: 10.28 - 13.14 days). The posterior probability of the oxygen requirement on the REGN-COV2 group was 18% (95% CrI: 3 - 33%), compared with the control of 36% (95% CrI: 16 - 54%). CONCLUSION: REGN-COV2 may be effective in early defervescence and shorter hospitalization. Its effectiveness for preventing a severe form of infection needs to be evaluated by further studies.