Traffic-Driven Epidemic Spreading in Networks: Considering the Transition of Infection From Being Mild to Severe
提出一个考虑感染从轻症到重症转变的交通驱动流行病传播模型，用平均场近似和连续时间马尔可夫链推导动态方程，发现感染阈值由通信频率矩阵的最大实特征值决定，并比较了不同资源分配分布对抑制传播的效果。
Realistic epidemic spreading is usually driven by traffic flow in networks, which is not captured in classic diffusion models. Moreover, the progress of a node's infection from mild to severe phase has not been particularly addressed in previous epidemic modeling. To address these issues, we propose a novel traffic-driven epidemic spreading model by introducing a new epidemic state, that is, the severe state, which characterizes the serious infection of a node different from the initial mild infection. We derive the dynamic equations of our model with the tools of individual-based mean-field approximation and continuous-time Markov chain. We find that, besides infection and recovery rates, the epidemic threshold of our model is determined by the largest real eigenvalue of a communication frequency matrix we construct. Finally, we study how the epidemic spreading is influenced by representative distributions of infection control resources. In particular, we observe that the uniform and Weibull distributions of control resources, which have very close performance, are much better than the Pareto distribution in suppressing the epidemic spreading.