Manda Edna Chilenje, Chirove Faraimunashe
University of KwaZulu-Natal, Pietermaritzburg, South Africa.
J Math Biol. 2018 Apr;76(5):1123-1158. doi: 10.1007/s00285-017-1170-1. Epub 2017 Jul 31.
Most existing models have considered the immunological processes occurring within the host and the epidemiological processes occurring at population level as decoupled systems. We present a new model using continuous systems of non linear ordinary differential equations by directly linking the within host dynamics capturing the interactions between Langerhans cells, CD4[Formula: see text] T-cells, R5 HIV and X4 HIV and the without host dynamics of a basic compartmental HIV/AIDS model. The model captures the biological theories of the cells that take part in HIV transmission. The study incorporates in its analysis the differences in time scales of the fast within host dynamics and the slow without host dynamics. In the mathematical analysis, important thresholds, the reproduction numbers, were computed which are useful in predicting the progression of the infection both within the host and without the host. The study results showed that the model exhibits four within host equilibrium points inclusive of three endemic equilibria whose effects translate into different scenarios at the population level. All the endemic equilibria were shown to be globally stable using Lyapunov functions and this is an important result in linking the within host dynamics to the population dynamics, because the disease free equilibrium point ceases to exist. The effects of linking were observed on the endemic equilibrium points of both the within host and population dynamics. Linking the two dynamics was shown to increase in the viral load within the host and increase in the epidemic levels in the population dynamics.
大多数现有模型将宿主内部发生的免疫过程和人群水平上发生的流行病学过程视为解耦系统。我们提出了一种新模型,该模型使用非线性常微分方程的连续系统,通过直接将捕获朗格汉斯细胞、CD4[公式:见正文]T细胞、R5型HIV和X4型HIV之间相互作用的宿主内部动态与基本房室HIV/AIDS模型的宿主外部动态联系起来。该模型体现了参与HIV传播的细胞的生物学理论。该研究在其分析中纳入了宿主内部快速动态和宿主外部缓慢动态的时间尺度差异。在数学分析中,计算了重要的阈值,即繁殖数,这对于预测宿主内部和宿主外部感染的进展都很有用。研究结果表明,该模型呈现出四个宿主内部平衡点,包括三个地方病平衡点,其影响在人群水平上转化为不同的情况。使用李雅普诺夫函数表明所有地方病平衡点都是全局稳定的,这是将宿主内部动态与人群动态联系起来的一个重要结果,因为无病平衡点不再存在。观察到这种联系对宿主内部和人群动态的地方病平衡点都有影响。将这两种动态联系起来显示会增加宿主内的病毒载量,并增加人群动态中的流行水平。