论文标题
边缘删除算法，以最大程度地减少SIR流行模型的传播
Edge Deletion Algorithms for Minimizing Spread in SIR Epidemic Models
论文作者
论文摘要
本文研究了算法策略，以有效减少易感感染的（SIR）流行模型中感染的数量。我们在确定性的SIR（D-SIR）模型和独立的Cascade Sir（IC-SIR）模型中考虑了Markov Chain Sir Model及其两个实例。我们研究了通过限制在现实限制下的接触来最大程度地减少感染数量的问题。在对繁殖数量的中等假设下，我们证明感染数在D-SIR模型中由超模型函数和大型随机网络的IC-SIR模型界定。我们提出了有效的算法，并保证了最大程度地减少感染。理论结果通过数值模拟说明。
This paper studies algorithmic strategies to effectively reduce the number of infections in susceptible-infected-recovered (SIR) epidemic models. We consider a Markov chain SIR model and its two instantiations in the deterministic SIR (D-SIR) model and the independent cascade SIR (IC-SIR) model. We investigate the problem of minimizing the number of infections by restricting contacts under realistic constraints. Under moderate assumptions on the reproduction number, we prove that the infection numbers are bounded by supermodular functions in the D-SIR model and the IC-SIR model for large classes of random networks. We propose efficient algorithms with approximation guarantees to minimize infections. The theoretical results are illustrated by numerical simulations.
