State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, People's Republic of China.
Key Laboratory of Universal Wireless Communications, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Chaos. 2022 Aug;32(8):083124. doi: 10.1063/5.0098328.
The spread of disease on complex networks has attracted wide attention in physics, mathematics, and epidemiology. Recent works have demonstrated that individuals always exhibit different criteria for disease infection in a network that significantly influences the epidemic dynamics. In this paper, considering the heterogeneity of node susceptibility, we proposed an infection threshold model with neighbor resource support. The infection threshold of an individual is associated with the degree, and a parameter follows the normal distribution. Based on improved heterogeneous mean-field theory and extensive numerical simulations, we find that the mean and standard deviation of the infection threshold model can affect the phase transition and epidemic outbreak size. As the mean of the normal distribution parameter increases from a small value to a large value, the system shows a change from a continuous phase transition to a discontinuous phase transition, and the disease even stops spreading. The disease spreads from a discontinuous phase transition to continuous for the sizeable mean value as the standard deviation increases. Furthermore, the standard deviation also varies in the outbreak size.
疾病在复杂网络上的传播在物理学、数学和流行病学领域引起了广泛关注。最近的研究表明，个体在网络中对疾病感染的标准总是不同的，这对传染病动力学有重大影响。在本文中，我们考虑到节点易感性的异质性，提出了一个具有邻居资源支持的感染阈值模型。个体的感染阈值与度数相关，并且一个参数遵循正态分布。基于改进的异质平均场理论和广泛的数值模拟，我们发现感染阈值模型的均值和标准差会影响相变和传染病爆发规模。随着正态分布参数均值从小值增加到大值，系统表现出从连续相变到不连续相变的变化，疾病甚至停止传播。当标准差增大时，疾病从不连续相变转变为连续相变，且疾病传播。此外，标准差也会在爆发规模上发生变化。
