Distributed Algorithm for Suppressing Epidemic Spread in Networks
Distributed Algorithm for Suppressing Epidemic Spread in Networks
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DOI：
10.1109/lcsys.2018.2844118
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发表时间：
2018-07-01
影响因子：
3
通讯作者：
Mills, Kevin
中科院分区：
文献类型：
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作者：
Mai, Van Sy;Battou, Abdella;Mills, Kevin
这封信考虑了在有限的治疗资源下抑制流行病在网络上传播的相关问题。传播动态被一个易感-感染-易感模型捕捉到。流行阈值和恢复速度由接触网络结构和异质性感染和治愈率决定。我们开发了一个分布式算法，可用于分配治疗资源，以满足三个潜在目标：1)在预防流行病的同时最小化总治疗成本；2)在给定足够的治疗资源的情况下最大化恢复速度；或3)在治疗资源不足的情况下，限制流行状态的大小。该分布式算法是Jacobi型的，几何收敛。我们给出了收敛速度的一个上界，它依赖于底层网络的结构和感染率。数值模拟表明了该分布式算法的有效性和可扩展性。
This letter considers problems related to suppressing epidemic spread over networks given limited curing resources. The spreading dynamic is captured by a susceptible-infected-susceptible model. The epidemic threshold and recovery speed are determined by the contact network structure and the heterogeneous infection and curing rates. We develop a distributed algorithm that can be used for allocating curing resources to meet three potential objectives: 1) minimize total curing cost while preventing an epidemic; 2) maximize recovery speed given sufficient curing resources; or 3) given insufficient curing resources, limit the size of an endemic state. The distributed algorithm is of the Jacobi type, and converges geometrically. We provide an upper bound on the convergence rate that depends on the structure and infection rates of the underlying network. Numerical simulations illustrate the efficiency and scalability of our distributed algorithm.