论文标题
网络元人口流行病的分布式链接删除策略及其在COVID-19大流行的控制中的应用
Distributed Link Removal Strategy for Networked Meta-Population Epidemics and its Application to the Control of the COVID-19 Pandemic
论文作者
论文摘要
在本文中，我们研究了用于网络元群体流行病的分布式链接删除策略。特别是，考虑了确定性网络易感感染的（SIR）模型来描述流行病的进化过程。为了遏制流行病的传播，我们提出了基于光谱的优化问题，涉及由网络拓扑和过渡速率构建的基质的perron-frobenius特征值。制定了修改的分布式链接去除策略，以便可以将其应用于加权挖掘的异质过渡速率的SIR模型。实施了提出的方法，以使用所报道的每个德国的感染和回收数据来控制Covid-19-19的大流行。数值实验表明，通过使用分布式链接删除策略可以显着降低感染百分比。
In this paper, we investigate the distributed link removal strategy for networked meta-population epidemics. In particular, a deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. In order to curb the spread of epidemics, we present the spectrum-based optimization problem involving the Perron-Frobenius eigenvalue of the matrix constructed by the network topology and transition rates. A modified distributed link removal strategy is developed such that it can be applied to the SIR model with heterogeneous transition rates on weighted digraphs. The proposed approach is implemented to control the COVID-19 pandemic by using the reported infected and recovered data in each state of Germany. The numerical experiment shows that the infected percentage can be significantly reduced by using the distributed link removal strategy.
