Département de physique, de génie physique et d'optique, Université Laval, Québec (Québec) G1V 0A6, Canada.
Centre interdisciplinaire en modélisation mathématique, Université Laval, Québec (Québec) G1V 0A6, Canada.
Phys Rev Lett. 2021 Oct 8;127(15):158301. doi: 10.1103/PhysRevLett.127.158301.
The collocation of individuals in different environments is an important prerequisite for exposure to infectious diseases on a social network. Standard epidemic models fail to capture the potential complexity of this scenario by (1) neglecting the higher-order structure of contacts that typically occur through environments like workplaces, restaurants, and households, and (2) assuming a linear relationship between the exposure to infected contacts and the risk of infection. Here, we leverage a hypergraph model to embrace the heterogeneity of environments and the heterogeneity of individual participation in these environments. We find that combining heterogeneous exposure with the concept of minimal infective dose induces a universal nonlinear relationship between infected contacts and infection risk. Under nonlinear infection kernels, conventional epidemic wisdom breaks down with the emergence of discontinuous transitions, superexponential spread, and hysteresis.
个体在不同环境中的组合是在社交网络中接触传染病的一个重要前提。标准的传染病模型未能捕捉到这种情况的潜在复杂性,因为(1)忽略了通常通过工作场所、餐馆和家庭等环境发生的更高阶的接触结构,以及(2)假设接触感染接触者与感染风险之间存在线性关系。在这里,我们利用超图模型来接受环境的异质性和个体在这些环境中的参与异质性。我们发现,将异质暴露与最小感染剂量的概念相结合,会在感染接触者和感染风险之间产生一种普遍的非线性关系。在非线性感染核下,传统的传染病智慧随着不连续跃迁、超指数传播和滞后的出现而瓦解。