Abstract
ABSTRACTHIV can infect cells via virus-to-cell infection or cell-to-cell viral transmission. These two infection modes may occur in a synergistic way and facilitate viral spread within an infected individual. In this paper, we developed an HIV latent infection model including both modes of transmission and time delays between viral entry and integration or viral production. We analysed the model by defining the basic reproductive number, showing the existence, positivity and boundedness of the solution, and proving the local and global stability of the infection-free and infected steady states. Numerical simulations have been performed to illustrate the theoretical results and evaluate the effects of time delays and fractions of infection leading to latency on the virus dynamics. The estimates of the relative contributions to the HIV latent reservoir and the virus population from the two modes of transmission have also been provided.
Highlights
Human immunodeficiency virus (HIV) is a retrovirus that mainly infects CD4+ T cells
On the basis of the models by Lai and Zou [24, 25] and Alshorman et al [2], we developed an HIV latent infection model incorporating both the cell-free virus infection and cell-to-cell transmission
For a within-host virus dynamic model, the basic reproduction number is the number of virions produced by one virion in its lifespan in a fully susceptible environment
Summary
Human immunodeficiency virus (HIV) is a retrovirus that mainly infects CD4+ T cells. Upon infection, CD4+ T cells can produce new virions, which lead to more cell infection and viral production. Many mathematical models have been developed to study the within-host dynamics of HIV infection [8, 16, 31,32,33,34,35,36, 41, 42, 53,54,55] Most of these models were focused on the virus-to-cell infection. Lai and Zou [24, 25] developed models that incorporate two modes of viral spread with and without the logistic target cell growth. They showed that the basic reproductive number determines the infection dynamics and that the logistic growth of target cells can generate a Hopf bifurcation. We performed numerical simulations to explore the effects of time delays and other parameters on virus dynamics and estimate the relative contributions to the latent reservoir and viral load from the two modes of viral transmission
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