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 language | English |
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Article number | 9266089 |
Pages (from-to) | 434-446 |
Number of pages | 13 |
Journal | IEEE Transactions on Network Science and Engineering |
Volume | 8 |
Issue number | 1 |
DOIs | |
State | Published - Jan 1 2021 |
Externally published | Yes |
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