The effect of self-limiting on the prevention and control of the diffuse COVID-19 epidemic with delayed and temporal-spatial heterogeneous

Ursodeoxycholic acid and COVID-19 outcomes: a cohort study and data synthesis of state-of-art evidence

BACKGROUND

The potential of ursodeoxycholic acid (UDCA) in inhibiting angiotensin-converting enzyme 2 was demonstrated. However, conflicting evidence emerged regarding the association between UDCA and COVID-19 outcomes, prompting the need for a comprehensive investigation.

RESEARCH DESIGN AND METHODS

Patients diagnosed with COVID-19 infection were retrospectively analyzed and divided into two groups: the UDCA-treated group and the control group. Kaplan-Meier recovery analysis and Cox proportional hazards models were used to evaluate the recovery time and hazard ratios. Additionally, study-level pooled analyses for multiple clinical outcomes were performed.

RESULTS

In the 115-patient cohort, UDCA treatment was significantly associated with a reduced recovery time. The subgroup analysis suggests that the 300 mg subgroup had a significant (adjusted hazard ratio: 1.63 [95% CI, 1.01 to 2.60]) benefit with a shorter duration of fever. The results of pooled analyses also show that UDCA treatment can significantly reduce the incidence of severe/critical diseases in COVID-19 (adjusted odds ratio: 0.68 [95% CI, 0.50 to 0.94]).

CONCLUSIONS

UDCA treatment notably improves the recovery time following an Omicron strain infection without observed safety concerns. These promising results advocate for UDCA as a viable treatment for COVID-19, paving the way for further large-scale and prospective research to explore the full potential of UDCA.

Spatial and temporal dynamics of SARS-CoV-2: Modeling, analysis and simulation

A reaction-diffusion viral infection model is formulated to characterize the infection process of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a heterogeneous environment. In the model, the viral production, infection and death rates of compartments are given by the general functions. We consider the well-posedness of the solution, derive the basic reproduction number R 0 , discuss the global stability of uninfected steady state and explore the uniform persistence for the model. We further propose a spatial diffusion SARS-CoV-2 infection model with humoral immunity and spatial independent coefficients, and analyze the global attractivity of uninfected, humoral inactivated and humoral activated equilibria which are determined by two dynamical thresholds. Numerical simulations are performed to illustrate our theoretical results which reveal that diffusion, spatial heterogeneity and incidence types have evident impact on the SARS-CoV-2 infection process which should not be neglected for experiments and clinical treatments.

A Non-Standard Finite Difference Scheme for a Diffusive HIV-1 Infection Model with Immune Response and Intracellular Delay

In this paper, we propose and study a diffusive HIV infection model with infected cells delay, virus mature delay, abstract function incidence rate and a virus diffusion term. By introducing the reproductive numbers for viral infection R0 and for CTL immune response number R1, we show that R0 and R1 act as threshold parameter for the existence and stability of equilibria. If R0≤1, the infection-free equilibrium E0 is globally asymptotically stable, and the viruses are cleared; if R1≤1<R0, the CTL-inactivated equilibrium E1 is globally asymptotically stable, and the infection becomes chronic but without persistent CTL response; if R1>1, the CTL-activated equilibrium E2 is globally asymptotically stable, and the infection is chronic with persistent CTL response. Next, we study the dynamic of the discreted system of our model by using non-standard finite difference scheme. We find that the global stability of the equilibria of the continuous model and the discrete model is not always consistent. That is, if R0≤1, or R1≤1<R0, the global stability of the two kinds model is consistent. However, if R1>1, the global stability of the two kinds model is not consistent. Finally, numerical simulations are carried out to illustrate the theoretical results and show the effects of diffusion factors on the time-delay virus model.