Group-Based General Epidemic Modeling for Spreading Processes on Networks: GroupGEM

被引:6
|
作者
Moon, Sifat Afroj [1 ]
Sahneh, Faryad Darabi [2 ]
Scoglio, Caterina [1 ]
机构
[1] Kansas State Univ, Dept Elect & Comp Engn, Kansas City, KS 66502 USA
[2] Univ Arizona, Dept Comp Sci, Tucson, AZ 85721 USA
基金
英国生物技术与生命科学研究理事会;
关键词
Epidemics; Computational modeling; Markov processes; Mathematical model; Nonhomogeneous media; Complex networks; Biological system modeling; Compartmental model; epidemic model; continuous-time Markov process; mean-field approximation; network; spreading process; scaling; graph partitioning; computational time;
D O I
10.1109/TNSE.2020.3039494
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We develop a group-based continuous-time Markov general epidemic modeling (GroupGEM) framework for any compartmental epidemic model (e.g., susceptible-infected-susceptible, susceptible-infected-recovered, susceptible-exposed-infected-recovered). Here, a group consists of a collection of individual nodes of a network. This model can be used to understand the critical dynamic characteristics of a stochastic epidemic spreading over large complex networks while being informative about the state of groups. Aggregating nodes by groups, the state-space becomes smaller than the one of individual-based approach at the cost of an aggregation error, which is bounded by the well-known isoperimetric inequality. We also develop a mean-field approximation of this framework to reduce the state-space size further. Finally, we extend the GroupGEM to multilayer networks. Individual-based frameworks are in general not computationally efficient. However, the individual-based approach is essential when the objective is to study the local dynamics at the individual level. Therefore, we propose a group-based framework to reduce the computational time of the Individual-based generalized epidemic modeling framework (GEMF) but retain its advantages.
引用
收藏
页码:434 / 446
页数:13
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