论文标题
Covid-19的Seair流行传播模型
SEAIR epidemic spreading model of COVID-19
论文作者
论文摘要
我们研究了COVID-19的易感暴露于疾病的疾病疫苗感染（SEAIR）流行传播模型。它捕获了Covid-19的感染性的两个重要特征：延迟开始及其在症状发作之前，甚至完全没有症状。理论上在随机常规图和复杂网络上以连续时隔室版本和离散时间版本进行理论上分析该模型。我们在分析上表明，流行阈值与所有三个版本中流行均衡处的易感人群的方程之间存在关系，当流行病很弱时。我们提供了理论论点，即节点的特征向量中心性大致决定了其被感染的风险。
We study Susceptible-Exposed-Asymptomatic-Infectious-Recovered (SEAIR) epidemic spreading model of COVID-19. It captures two important characteristics of the infectiousness of COVID-19: delayed start and its appearance before onset of symptoms, or even with total absence of them. The model is theoretically analyzed in continuous-time compartmental version and discrete-time version on random regular graphs and complex networks. We show analytically that there are relationships between the epidemic thresholds and the equations for the susceptible populations at the endemic equilibrium in all three versions, which hold when the epidemic is weak. We provide theoretical arguments that eigenvector centrality of a node approximately determines its risk to become infected.
