社会距离干预的自适应网络建模
Adaptive Network Modeling of Social Distancing Interventions
摘要：
2019冠状病毒病大流行已被证明是近期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，社交距离和封锁措施是世界各国政府为抗击疾病而采用的一些最常见的工具。虽然COVID-19的数学模型无处不在，但很少有人以一般的方式利用网络理论来解释社交距离的机制。在本文中，我们建立在现有的网络模型的异构，集群网络的随机链接激活/删除动态提出现实的社会距离机制，使用分段恒定的激活/删除率。我们发现，我们的模型能够丰富的定性行为，并提供有意义的见解与相对较少的干预参数。特别是，我们发现，社会距离干预措施的严重程度和何时开始比干预措施充分发挥作用所需的时间更有影响。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
