ArticleIET systems biology2015
Global stability of infection-free state and endemic infection state of a modified human immunodeficiency virus infection model.
Article in IET systems biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Fractional view analysis of sexual transmitted human papilloma virus infection for public health.Scientific reports · 2024Article
- A simple epidemic model for semi-closed community reveals the hidden outbreak risk in nursing homes, prisons, and residential universities.International journal of dynamics and control · 2022Article
- Mathematical analysis and simulation of a stochastic COVID-19 Lévy jump model with isolation strategy.Results in physics · 2021Article
- Modeling the dynamics of viral infections in presence of latently infected cells.Chaos, solitons, and fractals · 2020Article
- Control strategy design for the anti-HBV mathematical model.IET systems biology · 2019Article
- Mathematical Analysis and Clinical Implications of an HIV Model with Adaptive Immunity.Computational and mathematical methods in medicine · 2019Article
- Prediction of the containment of HIV infection by antiretroviral therapy - a variable structure control approach.IET systems biology · 2017Article
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Authors and funding
3 authors.
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Abstract
This study proposes a modified human immunodeficiency virus (HIV) infection differential equation model with a saturated infection rate. This model has an infection-free equilibrium point and an endemic infection equilibrium point. Using Lyapunov functions and LaSalle's invariance principle shows that if the model's basic reproductive number R0 < 1, the infection-free equilibrium point is globally asymptotically stable, otherwise the endemic infection equilibrium point is globally asymptotically stable. It is shown that a forward bifurcation will occur when R0 = 1. The basic reproductive number R0 of the modified model is independent of plasma total CD4⁺ T cell counts and thus the modified model is more reasonable than the original model proposed by Buonomo and Vargas-De-León. Based on the clinical data from HIV drug resistance database of Stanford University, using the proposed model simulates the dynamics of two group patients' anti-HIV infection treatments. The simulation results have shown that the first 4 weeks' treatments made the two group patients' R'0 < 1, respectively. After the period, drug resistance made the two group patients' R'0 > 1. The results explain why the two group patients' mean CD4⁺ T cell counts raised and mean HIV RNA levels declined in the first period, but contrary in the following weeks.
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