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arXiv 1902.08902q-bio.PEcond-mat.stat-mech
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持续性病毒感染的时间分布

Time Distribution for Persistent Viral Infection

Carmel Sagi, Michael Assaf

摘要:

我们研究了病毒感染的早期阶段,以及获得持续感染的时间分布。病毒群体通过进入靶细胞内并在靶细胞内繁殖而增殖,直到足够数量的新病毒颗粒通过爆发释放,具有给定的爆发尺寸分布,这导致受感染细胞的死亡。从描述病毒和感染细胞群体的联合动力学的2D模型开始,我们使用概率生成函数形式主义分析相应的主方程。利用病毒和感染细胞动力学之间的时间尺度分离,2D模型可以转换为有效的1D模型。为此,我们解决了1D模型解析为一个特定的选择的突发大小分布。在一般情况下,我们通过进行广泛的蒙特卡罗模拟来数值求解模型,并通过测量相应分布之间的Kullback-Leibler散度来证明2D和1D模型之间的等效性。重要的是,我们发现,感染时间的分布是高度偏斜的“胖”指数右尾。这表明,有不可忽略的一部分人的感染时间明显长于平均值,这可能对何时进行艾滋病毒检测产生影响。

英文摘要:

We study the early stages of viral infection, and the distribution of times to obtain a persistent infection. The virus population proliferates by entering and reproducing inside a target cell until a sufficient number of new virus particles are released via a burst, with a given burst size distribution, which results in the death of the infected cell. Starting with a 2D model describing the joint dynamics of the virus and infected cell populations, we analyze the corresponding master equation using the probability generating function formalism. Exploiting time-scale separation between the virus and infected cell dynamics, the 2D model can be cast into an effective 1D model. To this end, we solve the 1D model analytically for a particular choice of burst size distribution. In the general case, we solve the model numerically by performing extensive Monte-Carlo simulations, and demonstrate the equivalence between the 2D and 1D models by measuring the Kullback-Leibler divergence between the corresponding distributions. Importantly, we find that the distribution of infection times is highly skewed with a "fat" exponential right tail. This indicates that there is non-negligible portion of individuals with an infection time, significantly longer than the mean, which may have implications on when HIV tests should be performed.

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