Optimal curing policy for epidemic spreading over a community network with heterogeneous population
Optimal curing policy for epidemic spreading over a community network with heterogeneous population
复制标题
异质人群社区网络流行病传播的最优治疗策略
DOI：
10.1093/comnet/cnx060
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
P. Mieghem
中科院分区：
文献类型：
--
作者：
Stefania Ottaviano;F. Pellegrini;S. Bonaccorsi;P. Mieghem
要设计一项能够以负担得起的代价遏制流行病进程的有效防治政策，就必须考虑到支持传染进程的人口接触网络的结构。因此，我们以尽可能低的成本解决在人口中分配恢复资源的问题，以防止该流行病在网络中无限期地持续下去。具体地说，我们用一阶平均场近似的方法分析了在加权图上传播的易感-感染-易感流行病过程。首先，我们描述了接触网络对异质人群中流行病动态的影响，这些人群可能被划分为社区。对于社区网络的情况，我们的调查依赖于图论的公平划分概念；我们表明，流行病阈值是网络对流行病传播鲁棒性的关键度量，可以使用低维动态系统来确定。利用流行病阈值的计算，我们通过求解一个凸最小化问题来确定成本最优的治疗策略，该问题在社区网络的情况下具有降维性。最后，我们考虑了一个两级最优养护问题，针对该问题，我们设计了一个在网络规模下具有多项式时间复杂度的算法。
The design of an efficient curing policy, able to stem an epidemic process at an affordable cost, has to account for the structure of the population contact network supporting the contagious process. Thus, we tackle the problem of allocating recovery resources among the population, at the lowest cost possible to prevent the epidemic from persisting indefinitely in the network. Specifically, we analyze a susceptible-infected-susceptible epidemic process spreading over a weighted graph, by means of a first-order mean-field approximation. First, we describe the influence of the contact network on the dynamics of the epidemics among a heterogeneous population, that is possibly divided into communities. For the case of a community network, our investigation relies on the graph-theoretical notion of equitable partition; we show that the epidemic threshold, a key measure of the network robustness against epidemic spreading, can be determined using a lower-dimensional dynamical system. Exploiting the computation of the epidemic threshold, we determine a cost-optimal curing policy by solving a convex minimization problem, which possesses a reduced dimension in the case of a community network. Lastly, we consider a two-level optimal curing problem, for which an algorithm is designed with a polynomial time complexity in the network size.
影响因子：
4.3
作者：
Ball, Frank;Sirl, David;Trapman, Pieter
通讯作者：
Trapman, Pieter
影响因子：
3.8
作者：
Ball, Frank;Britton, Tom;Tomba, Gianpaolo Scalia
通讯作者：
Tomba, Gianpaolo Scalia
影响因子：
4.3
作者：
Ball, Frank;Neal, Peter
通讯作者：
Neal, Peter