Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic

Fangzhou Liu; Yuhong Chen; Tong Liu; Dong Xue; Martin Buss

doi:10.1109/CDC45484.2021.9683314

Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic

Fangzhou Liu, Yuhong Chen, Tong Liu, Dong Xue, Martin Buss

Chair of Automatic Control Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

This paper studies the distributed link removal problem for controlling epidemic spreading in a networked meta-population system. A deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. To curb the spread of epidemics, we reformulate the original topology design problem into a minimization program of the Perron-Frobenius eigenvalue of the matrix involving the network topology and transition rates. A modified distributed link removal strategy is developed such that it can be applied to the SIR model with heterogeneous transition rates on weighted digraphs. The proposed approach is implemented to control the COVID-19 pandemic by using the infected and recovered data reported by the German federal states. The numerical experiment shows that the infected percentage can be significantly reduced by employing the distributed link removal strategy.

Original language	English
Title of host publication	60th IEEE Conference on Decision and Control, CDC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2824-2829
Number of pages	6
ISBN (Electronic)	9781665436595
DOIs	https://doi.org/10.1109/CDC45484.2021.9683314
State	Published - 2021
Event	60th IEEE Conference on Decision and Control, CDC 2021 - Austin, United States Duration: 13 Dec 2021 → 17 Dec 2021

Publication series

Name	Proceedings of the IEEE Conference on Decision and Control
Volume	2021-December
ISSN (Print)	0743-1546
ISSN (Electronic)	2576-2370

Conference

Conference	60th IEEE Conference on Decision and Control, CDC 2021
Country/Territory	United States
City	Austin
Period	13/12/21 → 17/12/21

Keywords

Distributed link removal
controlling of COVID-19 pandemic
networked epidemics

Access to Document

10.1109/CDC45484.2021.9683314

Cite this

Liu, F., Chen, Y., Liu, T., Xue, D., & Buss, M. (2021). Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic. In 60th IEEE Conference on Decision and Control, CDC 2021 (pp. 2824-2829). (Proceedings of the IEEE Conference on Decision and Control; Vol. 2021-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CDC45484.2021.9683314

Liu, Fangzhou ; Chen, Yuhong ; Liu, Tong et al. / Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic. 60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 2824-2829 (Proceedings of the IEEE Conference on Decision and Control).

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title = "Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic",

abstract = "This paper studies the distributed link removal problem for controlling epidemic spreading in a networked meta-population system. A deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. To curb the spread of epidemics, we reformulate the original topology design problem into a minimization program of the Perron-Frobenius eigenvalue of the matrix involving the network topology and transition rates. A modified distributed link removal strategy is developed such that it can be applied to the SIR model with heterogeneous transition rates on weighted digraphs. The proposed approach is implemented to control the COVID-19 pandemic by using the infected and recovered data reported by the German federal states. The numerical experiment shows that the infected percentage can be significantly reduced by employing the distributed link removal strategy.",

keywords = "Distributed link removal, controlling of COVID-19 pandemic, networked epidemics",

author = "Fangzhou Liu and Yuhong Chen and Tong Liu and Dong Xue and Martin Buss",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 60th IEEE Conference on Decision and Control, CDC 2021 ; Conference date: 13-12-2021 Through 17-12-2021",

year = "2021",

doi = "10.1109/CDC45484.2021.9683314",

language = "English",

series = "Proceedings of the IEEE Conference on Decision and Control",

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Liu, F, Chen, Y, Liu, T, Xue, D & Buss, M 2021, Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic. in 60th IEEE Conference on Decision and Control, CDC 2021. Proceedings of the IEEE Conference on Decision and Control, vol. 2021-December, Institute of Electrical and Electronics Engineers Inc., pp. 2824-2829, 60th IEEE Conference on Decision and Control, CDC 2021, Austin, United States, 13/12/21. https://doi.org/10.1109/CDC45484.2021.9683314

Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic. / Liu, Fangzhou; Chen, Yuhong; Liu, Tong et al.
60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 2824-2829 (Proceedings of the IEEE Conference on Decision and Control; Vol. 2021-December).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Xue, Dong

AU - Buss, Martin

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N2 - This paper studies the distributed link removal problem for controlling epidemic spreading in a networked meta-population system. A deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. To curb the spread of epidemics, we reformulate the original topology design problem into a minimization program of the Perron-Frobenius eigenvalue of the matrix involving the network topology and transition rates. A modified distributed link removal strategy is developed such that it can be applied to the SIR model with heterogeneous transition rates on weighted digraphs. The proposed approach is implemented to control the COVID-19 pandemic by using the infected and recovered data reported by the German federal states. The numerical experiment shows that the infected percentage can be significantly reduced by employing the distributed link removal strategy.

AB - This paper studies the distributed link removal problem for controlling epidemic spreading in a networked meta-population system. A deterministic networked susceptible-infected-recovered (SIR) model is considered to describe the epidemic evolving process. To curb the spread of epidemics, we reformulate the original topology design problem into a minimization program of the Perron-Frobenius eigenvalue of the matrix involving the network topology and transition rates. A modified distributed link removal strategy is developed such that it can be applied to the SIR model with heterogeneous transition rates on weighted digraphs. The proposed approach is implemented to control the COVID-19 pandemic by using the infected and recovered data reported by the German federal states. The numerical experiment shows that the infected percentage can be significantly reduced by employing the distributed link removal strategy.

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Liu F, Chen Y, Liu T, Xue D, Buss M. Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic. In 60th IEEE Conference on Decision and Control, CDC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 2824-2829. (Proceedings of the IEEE Conference on Decision and Control). doi: 10.1109/CDC45484.2021.9683314

Distributed Link Removal Strategy for Networked Meta-Population Epidemics and Its Application to the Control of the COVID-19 Pandemic

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