Abstract：
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.
摘要：
事实证明，COVID-19大流行是最近记忆中最具破坏性的突发公共卫生事件之一。在非药物干预措施中，社会距离和封锁措施是世界各国政府用来抗击这种疾病的一些最常见的工具。虽然COVID-19的数学模型无处不在，很少有人以一般的方式利用网络理论来解释社会距离的机制。在本文中,我们建立在现有的异构网络模型上，具有随机链接激活/删除动态的集群网络，以使用分段恒定的激活/删除率提出现实的社交距离机制。我们发现我们的模型能够丰富的定性行为，并以相对较少的干预参数提供有意义的见解。特别是,我们发现，社会距离干预措施的严重程度以及何时开始干预措施的影响要大于干预措施完全生效所需的时间。
