Center for Functional Nanomaterials, Brookhaven National Laboratory, Upton, United States.
Department of Bioengineering, University of Illinois Urbana-Champaign, Urbana, United States.
Elife. 2021 Nov 8;10:e68341. doi: 10.7554/eLife.68341.
It is well recognized that population heterogeneity plays an important role in the spread of epidemics. While individual variations in social activity are often assumed to be persistent, that is, constant in time, here we discuss the consequences of dynamic heterogeneity. By integrating the stochastic dynamics of social activity into traditional epidemiological models, we demonstrate the emergence of a new long timescale governing the epidemic, in broad agreement with empirical data. Our stochastic social activity model captures multiple features of real-life epidemics such as COVID-19, including prolonged plateaus and multiple waves, which are transiently suppressed due to the dynamic nature of social activity. The existence of a long timescale due to the interplay between epidemic and social dynamics provides a unifying picture of how a fast-paced epidemic typically will transition to an endemic state.
人们普遍认识到,人群异质性在传染病的传播中起着重要作用。虽然社会活动中的个体差异通常被认为是持续的,即随时间保持不变,但在这里我们讨论了动态异质性的后果。通过将社会活动的随机动力学纳入传统的流行病学模型,我们展示了一个新的主导传染病的长时标出现,这与经验数据基本一致。我们的随机社会活动模型捕捉到了现实生活中的多种传染病,如 COVID-19 的特征,包括由于社会活动的动态性质而暂时受到抑制的长时间平台和多个波。由于传染病和社会动力学之间的相互作用而存在长时标,为了解一个快速传播的传染病如何通常过渡到地方性状态提供了一个统一的图景。