Data_Sheet_1_On the Coevolution Between Social Network Structure and Diffusion of the Coronavirus (COVID-19) in Spatial Compartmental Epidemic Models.pdf

In this article, the author studies epidemic diffusion in a spatial compartmental model, where individuals are initially connected in a social or geographical network. As the virus spreads in the network, the structure of interactions between people may endogenously change over time, due to quarantining measures and/or spatial-distancing (SD) policies. The author explores via simulations the dynamic properties of the coevolutionary process linking disease diffusion and network properties. Results suggest that, in order to predict how epidemic phenomena evolve in networked populations, it is not enough to focus on the properties of initial interaction structures. Indeed, the coevolution of network structures and compartment shares strongly shape the process of epidemic diffusion, especially in terms of its speed. Furthermore, the author shows that the timing and features of SD policies may dramatically influence their effectiveness.

本文作者对空间隔室模型中的疫情扩散进行了研究，在该模型中，个体最初连接于社交或地理网络。随着病毒在网络中的传播，由于隔离措施和/或空间隔离（SD）政策的影响，人与人之间的互动结构可能会随时间内生性地发生变化。作者通过模拟探讨了疾病扩散与网络属性之间的协同进化过程的动态特性。研究结果表明，为了预测网络人群中疫情现象的演变，仅仅关注初始互动结构的特性是远远不够的。实际上，网络结构与隔室份额的协同进化强烈地塑造了疫情扩散的过程，特别是在其速度方面。此外，作者还表明，空间隔离政策的时机和特征可能会对其有效性产生显著影响。