Department of Mathematics, University of California, Davis, Davis, CA, USA.
Department of Mathematics, Oklahoma State University, Stillwater, OK, USA.
J Theor Biol. 2022 Aug 7;546:111151. doi: 10.1016/j.jtbi.2022.111151. Epub 2022 May 12.
The COVID-19 pandemic has proved to be one of the most disruptive public health emergencies in recent memory. Among non-pharmaceutical interventions, social distancing and lockdown measures are some of the most common tools employed by governments around the world to combat the disease. While mathematical models of COVID-19 are ubiquitous, few have leveraged network theory in a general way to explain the mechanics of social distancing. In this paper, we build on existing network models for heterogeneous, clustered networks with random link activation/deletion dynamics to put forth realistic mechanisms of social distancing using piecewise constant activation/deletion rates. We find our models are capable of rich qualitative behavior, and offer meaningful insight with relatively few intervention parameters. In particular, we find that the severity of social distancing interventions and when they begin have more impact than how long it takes for the interventions to take full effect.
新冠疫情是近年来最具破坏性的公共卫生突发事件之一。在非药物干预措施中，社交距离和封锁措施是世界各国政府用来对抗该疾病的最常见工具之一。尽管 COVID-19 的数学模型无处不在，但很少有模型以通用的方式利用网络理论来解释社交距离的力学原理。在本文中，我们基于具有随机链接激活/删除动态的异构、聚类网络的现有网络模型，提出了使用分段常数激活/删除率的现实社交距离机制。我们发现我们的模型具有丰富的定性行为，并提供了有意义的见解，而干预参数相对较少。特别是，我们发现社交距离干预的严重程度和开始时间比干预完全生效所需的时间更具影响力。
