Chen Yingqing, Dale Renee, He Hongyu, Le Quoc-Anh T
Fred Hutchinson Cancer Research Center, Seattle, WA, USA.
Department of Biological Sciences, Louisiana State University, Baton Rouge, LA 70803, USA.
Comput Math Methods Med. 2017;2017:1093045. doi: 10.1155/2017/1093045. Epub 2017 Nov 7.
In this paper, we construct a linear differential system in both continuous time and discrete time to model HIV transmission on the population level. The main question is the determination of parameters based on the posterior information obtained from statistical analysis of the HIV population. We call these parameters dynamic constants in the sense that these constants determine the behavior of the system in various models. There is a long history of using linear or nonlinear dynamic systems to study the HIV population dynamics or other infectious diseases. Nevertheless, the question of determining the dynamic constants in the system has not received much attention. In this paper, we take some initial steps to bridge such a gap. We study the dynamic constants that appear in the linear differential system model in both continuous and discrete time. Our computations are mostly carried out in Matlab.
在本文中，我们构建了一个连续时间和离散时间的线性微分系统，以在人群层面上对艾滋病毒传播进行建模。主要问题是根据从艾滋病毒人群统计分析中获得的后验信息来确定参数。我们将这些参数称为动态常数，因为这些常数决定了系统在各种模型中的行为。使用线性或非线性动态系统来研究艾滋病毒人群动态或其他传染病有着悠久的历史。然而，确定系统中动态常数的问题并未受到太多关注。在本文中，我们采取了一些初步措施来弥合这一差距。我们研究了连续时间和离散时间的线性微分系统模型中出现的动态常数。我们的计算大多在Matlab中进行。
