School of Mathematical and Physical Sciences, Department of Mathematics, University of Sussex, Falmer, Brighton, BN1 9QH, UK.
Math Biosci. 2011 Jun;231(2):197-209. doi: 10.1016/j.mbs.2011.03.006. Epub 2011 Mar 21.
In a recent paper, we proposed and analyzed a compartmental ODE-based model describing the dynamics of an infectious disease where the presence of the pathogen also triggers the diffusion of information about the disease. In this paper, we extend this previous work by presenting results based on pairwise and simulation models that are better suited for capturing the population contact structure at a local level. We use the pairwise model to examine the potential of different information generating mechanisms and routes of information transmission to stop disease spread or to minimize the impact of an epidemic. The individual-based simulation is used to better differentiate between the networks of disease and information transmission and to investigate the impact of different basic network topologies and network overlap on epidemic dynamics. The paper concludes with an individual-based semi-analytic calculation of R(0) at the non-trivial disease free equilibrium.
在最近的一篇论文中，我们提出并分析了一个基于房室微分方程的模型，该模型描述了一种传染病的动态，其中病原体的存在也会引发关于该疾病的信息扩散。在本文中，我们通过提出基于成对模型和模拟模型的结果来扩展之前的工作，这些结果更适合捕捉局部水平的人口接触结构。我们使用成对模型来研究不同的信息生成机制和信息传播途径的潜力，以阻止疾病传播或最小化传染病的影响。基于个体的模拟用于更好地区分疾病和信息传播网络，并研究不同基本网络拓扑结构和网络重叠对传染病动态的影响。本文最后对非平凡无病平衡点处的 R(0)进行了基于个体的半解析计算。
