Xiao Yanni, Zhou Yicang, Tang Sanyi
Department of Applied Mathematics, Xi'an Jiaotong University Xi'an, 710049, PR China.
Math Med Biol. 2011 Sep;28(3):227-44. doi: 10.1093/imammb/dqq007. Epub 2010 May 3.
A network model at both the population and individual levels, which simulates both between-patch and within-patch dynamics, is proposed. We investigated the effects of dispersal networks and distribution of local dynamics on the outcome of an epidemic at the population level. Numerical studies show that disease control on random networks may be easier than on small-world networks, depending on the initial distribution of the local dynamics. Spatially separating instead of gathering patches where disease locally persists is beneficial to global disease control if dispersal networks are a type of small-world networks. Dispersal networks with higher degree lead to a higher mean value of R0. Furthermore, irregularity of network and randomization are beneficial to disease stabilization and greatly affect the resulting global dynamics.
提出了一种在种群和个体层面的网络模型，该模型模拟了斑块间和斑块内的动态变化。我们研究了扩散网络和局部动态分布对种群层面疫情结果的影响。数值研究表明，根据局部动态的初始分布，在随机网络上进行疾病控制可能比在小世界网络上更容易。如果扩散网络是小世界网络类型，将疾病局部持续存在的斑块空间分离而不是聚集起来，有利于全球疾病控制。具有更高度的扩散网络会导致更高的R0平均值。此外，网络的不规则性和随机化有利于疾病稳定，并极大地影响最终的全球动态。
