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

Sifat Afroj Moon, Faryad Darabi Sahneh, Caterina Scoglio

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

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.

Original languageEnglish
Article number9266089
Pages (from-to)434-446
Number of pages13
JournalIEEE Transactions on Network Science and Engineering
Volume8
Issue number1
DOIs
StatePublished - Jan 1 2021
Externally publishedYes

Funding

Manuscript Revised October 15, 2020; received August 29, 2020; accepted November 11, 2020. Date of publication November 23, 2020; date of current version March 17, 2021. This work was supported by the NSF/NIH/USDA/ BBSRC Ecology and Evolution of Infectious Diseases (EEID) Program through USDA-NIFA Award 2015-67013-23818. Recommended for acceptance by Dr. G. Xiao. (Corresponding author: Sifat Afroj Moon.) Sifat Afroj Moon and Caterina Scoglio are with the Department of Electrical and Computer Engineering, Kansas State University, Kansas, KS 66502 USA (e-mail: [email protected]; [email protected]).

Keywords

  • Compartmental model
  • computational time
  • continuous-time Markov process
  • epidemic model
  • graph partitioning
  • mean-field approximation
  • network
  • scaling
  • spreading process

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