论文标题
在随机图上传播感染：COVID-19的渗滤类型模型
Spreading of infections on random graphs: A percolation-type model for COVID-19
论文作者
论文摘要
我们使用渗透理论的概念在网络上介绍了一个流行传播模型。该模型是通过讨论标准SIR模型的动机，并扩展了描述锁定在人群中的影响。详细讨论了使用与SIR方案相同的锁定方案实施的晶格模型的基本思想和行为，并通过广泛的模拟进行了详细讨论。两种模型之间的比较均与美国的Covid-19数据案例进行了比较。这两种符合经验数据都非常好，但是两种方法之间出现了一些差异，这些方法表明对广泛的SIR模型采用另一种方法的有用性。
We introduce an epidemic spreading model on a network using concepts from percolation theory. The model is motivated by discussing the standard SIR model, with extensions to describe effects of lockdowns within a population. The underlying ideas and behavior of the lattice model, implemented using the same lockdown scheme as for the SIR scheme, are discussed in detail and illustrated with extensive simulations. A comparison between both models is presented for the case of COVID-19 data from the USA. Both fits to the empirical data are very good, but some differences emerge between the two approaches which indicate the usefulness of having an alternative approach to the widespread SIR model.
