Modeling HIV multiple infection

On the Dynamics of HIV-Tuberculosis Coinfection Model with Temporal Recovery from Tuberculosis: An Analysis

The current study is an attempt to frame a deterministic compartmental model for HIV-TB coinfection, considering temporary recovery from Tuberculosis (TB) after treatment (the possibility of reinfection with TB even after recovery). The proposed HIV-TB coinfection model is a composite of an susceptible-infected (SI) type HIV/AIDS model and a susceptible-exposed-infected-recovered type TB model. In the beginning, the HIV-TB model is constructed, followed by the qualitative investigation of the model. The equilibrium points of the model are obtained and have been examined in detail. Further, the basic reproduction number for the HIV-TB coinfection model has been computed, and the proposed model has been simulated numerically to investigate the effect of treatment on HIV-TB coinfection. Analysis of the model claims the existence of interior equilibrium when both HIV and TB reproduction numbers are more than unity. The results exhibit that TB treatment will be the most efficient in discarding the HIV-TB coinfection disease whenever the basic reproduction of HIV-TB is less than one. In addition, our results suggest that the reinfection of TB after recovery impacts HIV-TB transmission. It has been found that reinfection makes disease eradication more challenging. As, in the presence of reinfection, the total infected cases are always higher than the infected cases in the absence of reinfection.

Modeling SARS-CoV-2 Infection Dynamics: Insights into Viral Clearance and Immune Synergy

Understanding the mechanisms of interaction between SARS-CoV-2 infection and the immune system is crucial for developing effective treatment strategies against COVID-19. In this paper, a mathematical model is formulated to investigate the interactions among SARS-CoV-2 infection, cellular immunity, and humoral immunity. Clinical data from eight asymptomatic or mild COVID-19 patients in Munich are used to fit the model, and the dynamics of natural killer (NK) cells, cytotoxic T lymphocytes (CTLs), B cells, and antibodies are further explored using the average of the best-fitting parameter values. Subsequently, the impact of NK cells, CTLs, B cells, and antibodies on SARS-CoV-2 infection is numerically investigated. The results indicate that (i) the synergy of NK cells, CTLs, and antibodies leads to a rapid decrease in the viral load during SARS-CoV-2 infection; (ii) antibodies play a crucial role compared to other immune mechanisms, and enhancing B cell stimulation may be more effective in clearing the virus from the lungs; (iii) in terms of cytotoxic effects, CTLs are stronger and more sustained than NK cells. Furthermore, the existence and local stability of the model's equilibria are fully classified, and complex dynamics of the model are further investigated using bifurcation theory, revealing multistability phenomena, including multiple attractors and periodic solutions. These findings suggest potential uncertainty and diversity in SARS-CoV-2 infection outcomes.

Modeling dynamics of acute HIV infection incorporating density-dependent cell death and multiplicity of infection

Understanding the dynamics of acute HIV infection can offer valuable insights into the early stages of viral behavior, potentially helping uncover various aspects of HIV pathogenesis. The standard viral dynamics model explains HIV viral dynamics during acute infection reasonably well. However, the model makes simplifying assumptions, neglecting some aspects of HIV infection. For instance, in the standard model, target cells are infected by a single HIV virion. Yet, cellular multiplicity of infection (MOI) may have considerable effects in pathogenesis and viral evolution. Further, when using the standard model, we take constant infected cell death rates, simplifying the dynamic immune responses. Here, we use four models-1) the standard viral dynamics model, 2) an alternate model incorporating cellular MOI, 3) a model assuming density-dependent death rate of infected cells and 4) a model combining (2) and (3)-to investigate acute infection dynamics in 43 people living with HIV very early after HIV exposure. We find that all models qualitatively describe the data, but none of the tested models is by itself the best to capture different kinds of heterogeneity. Instead, different models describe differing features of the dynamics more accurately. For example, while the standard viral dynamics model may be the most parsimonious across study participants by the corrected Akaike Information Criterion (AICc), we find that viral peaks are better explained by a model allowing for cellular MOI, using a linear regression analysis as analyzed by R2. These results suggest that heterogeneity in within-host viral dynamics cannot be captured by a single model. Depending on the specific aspect of interest, a corresponding model should be employed.

A mathematical model for HIV dynamics with multiple infections: implications for immune escape

Multiple infections enable the recombination of different strains, which may contribute to viral diversity. How multiple infections affect the competition dynamics between the two types of strains, the wild and the immune escape mutant, remains poorly understood. This study develops a novel mathematical model that includes the two strains, two modes of viral infection, and multiple infections. For the representative double-infection case, the reproductive numbers are derived and global stabilities of equilibria are obtained via the Lyapunov direct method and theory of limiting systems. Numerical simulations indicate similar viral dynamics regardless of multiplicities of infections though the competition between the two strains would be the fiercest in the case of quadruple infections. Through sensitivity analysis, we evaluate the effect of parameters on the set-point viral loads in the presence and absence of multiple infections. The model with multiple infections predict that there exists a threshold for cytotoxic T lymphocytes (CTLs) to minimize the overall viral load. Weak or strong CTLs immune response can result in high overall viral load. If the strength of CTLs maintains at an intermediate level, the fitness cost of the mutant is likely to have a significant impact on the evolutionary dynamics of mutant viruses. We further investigate how multiple infections alter the viral dynamics during the combination antiretroviral therapy (cART). The results show that viral loads may be underestimated during cART if multiple-infection is not taken into account.

The role of tunneling nanotubes during early stages of HIV infection and reactivation: implications in HIV cure

Tunneling nanotubes (TNTs), also called cytonemes or tumor microtubes, correspond to cellular processes that enable long-range communication. TNTs are plasma membrane extensions that form tubular processes that connect the cytoplasm of two or more cells. TNTs are mostly expressed during the early stages of development and poorly expressed in adulthood. However, in disease conditions such as stroke, cancer, and viral infections such as HIV, TNTs proliferate, but their role is poorly understood. TNTs function has been associated with signaling coordination, organelle sharing, and the transfer of infectious agents such as HIV. Here, we describe the critical role and function of TNTs during HIV infection and reactivation, as well as the use of TNTs for cure strategies.

The role of cell-to-cell transmission in HIV infection: insights from a mathematical modeling approach

HIV infection remains a serious global public health problem. Although current drug treatment is effective and can reduce plasma viral loads below the level of detection, it cannot eradicate the virus. The reasons for the low virus persistence despite long-term therapy have not been fully elucidated. In addition, multiple HIV infection, i.e., infection of a cell by multiple viruses, is common and can facilitate viral recombination and mutations, evading the immune system and conferring resistance to drug treatment. The mechanisms for multiple HIV infection formation and their respective contributions remain unclear. To answer these questions, we developed a mathematical modeling framework that encompasses cell-free viral infection and cell-to-cell spread. We fit sub-models that only have one transmission route and the full model containing both to the multi-infection data from HIV-infected patients, and show that the multi-infection data can only be reproduced if these two transmission routes are both considered. Computer simulations with the best-fitting parameter values indicate that cell-to-cell spread leads to the majority of multiple infection and also accounts for the majority of overall infection. Sensitivity analysis shows that cell-to-cell spread has reduced susceptibility to treatment and may explain low HIV persistence. Taken together, this work indicates that cell-to-cell spread plays a crucial role in the development of HIV multi-infection and low HIV persistence despite long-term therapy, and therefore has important implications for understanding HIV pathogenesis and developing more effective treatment strategies to control or even eliminate the disease.

PrEP Intervention in the Mitigation of HIV/AIDS Epidemics in China via a Data-Validated Age-Structured Model

Antiretroviral-based pre-exposure prophylaxis (PrEP) treatment offers a new opportunity for protecting humans against HIV and disrupting current HIV prevention systems. However, implementing this preventive measure has been difficult due to its high cost. In this paper, we propose an age-structured model that incorporates infection ages, HAART (highly active antiretroviral therapy), and PrEP intervention. We investigate the qualitative behavior of the model and find a threshold parameter (the basic reproduction number) that determines the asymptotic stability of equilibria. We validate the model and estimate the parameters by confronting the actual HIV/AIDS data from 2004 to 2018 in China using MCMC (Markov Chain Monte Carlo) method. Furthermore, we investigate the PrEP intervention strategy by using incremental cost-effectiveness and average cost-effectiveness. Our work suggests that PrEP intervention based on the infection characteristics of different age groups can be an effective strategy to eradicate HIV/AIDS epidemics in China.

Modeling viral dynamics in SARS-CoV-2 infection based on differential equations and numerical analysis

Computational modeling and simulation of viral dynamics would explain the pathogenesis for any virus. Such computational attempts have been successfully made to predict and control HIV-1 or hepatitis B virus. However, the dynamics for SARS-CoV-2 has not been adequately investigated. The purpose of this research is to propose different SARS-CoV-2 dynamics models based on differential equations and numerical analysis towards distilling the models to explain the mechanism of SARS-CoV-2 pathogenesis. The proposed four models formalize the dynamical system of SARS-CoV-2 infection, which consists of host cells and viral particles. These models undergo numerical analysis, including sensitivity analysis and stability analysis. Based on the sensitivity indices of the four models' parameters, the four models are simplified into two models. In advance of the following calibration experiments, the eigenvalues of the Jacobian matrices of these two models are calculated, thereby guaranteeing that any solutions are stable. Then, the calibration experiments fit the simulated data sequences of the two models to two observed data sequences, SARS-CoV-2 viral load in mild cases and that in severe cases. Comparing the estimated parameters in mild cases and severe cases indicates that cell-to-cell transmission would significantly correlate to the COVID-19 severity. These experiments for modeling and simulation provide plausible computational models for the SARS-CoV-2 dynamics, leading to further investigation for identifying the essential factors in severe cases.