Miller Joel C
School of Mathematical Sciences, School of Biological Sciences, and Monash Academy for Cross & Interdisciplinary Mathematics, Monash University, Melbourne, Victoria, Australia.
PLoS One. 2014 Jul 8;9(7):e101421. doi: 10.1371/journal.pone.0101421. eCollection 2014.
In this paper we extend previous work deriving dynamic equations governing infectious disease spread on networks. The previous work has implicitly assumed that the disease is initialized by an infinitesimally small proportion of the population. Our modifications allow us to account for an arbitrarily large initial proportion infected. This helps resolve an apparent paradox in earlier work whereby the number of susceptible individuals could increase if too many individuals were initially infected. It also helps explain an apparent small deviation that has been observed between simulation and theory. An advantage of this modification is that it allows us to account for changes in the structure or behavior of the population during the epidemic.
在本文中,我们扩展了之前的工作,推导了控制传染病在网络上传播的动态方程。之前的工作隐含地假设疾病是由极小比例的人群引发的。我们的改进使我们能够考虑任意大的初始感染比例。这有助于解决早期工作中一个明显的悖论,即如果最初感染的个体过多,易感个体的数量可能会增加。它也有助于解释在模拟和理论之间观察到的一个明显的小偏差。这种改进的一个优点是它使我们能够考虑疫情期间人群结构或行为的变化。