Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population

Yao Hu; Lequan Min; Yongmei Su; Yang Kuang

doi:10.1145/3036331.3036348

Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population

Yao Hu, Lequan Min, Yongmei Su, Yang Kuang

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

1 Scopus citations

Abstract

Epidemic Spreading is a major global health problem. Modeling epidemic spreading dynamics is important for understanding and controlling epidemic spreading, providing prevention strategies. This paper points out some flaws existing in the susceptibleinfected - susceptible (SIS) model proposed by Safan and Rihan, and proposes a modified susceptible-infected-recovered (SIR) model on homogenous networks. It is proved that if the basic reproduction number Rv of the model is less than one, then the infection-free equilibrium of the model is globally asymptotically stable. On the other hand, if Rv of the model is more than one, the endemic equilibrium of the model is globally asymptotically stable. This paper also numerically predicts the effect of vaccination ratio on the size of HBV infected mainland Chinese population.

Original language	English (US)
Title of host publication	Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017
Publisher	Association for Computing Machinery
Pages	7-13
Number of pages	7
ISBN (Electronic)	9781450348164
DOIs	https://doi.org/10.1145/3036331.3036348
State	Published - Jan 20 2017
Event	8th International Conference on Computer Modeling and Simulation, ICCMS 2017 - Canberra, Australia Duration: Jan 20 2017 → Jan 23 2017

Publication series

Name	ACM International Conference Proceeding Series
Volume	Part F128047

Other

Other	8th International Conference on Computer Modeling and Simulation, ICCMS 2017
Country/Territory	Australia
City	Canberra
Period	1/20/17 → 1/23/17

Keywords

Birth and death rate
Global stability
Homogenous network
Local stability
Sir model

ASJC Scopus subject areas

Software
Human-Computer Interaction
Computer Vision and Pattern Recognition
Computer Networks and Communications

Access to Document

10.1145/3036331.3036348

Cite this

Hu, Y., Min, L., Su, Y., & Kuang, Y. (2017). Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population. In Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017 (pp. 7-13). (ACM International Conference Proceeding Series; Vol. Part F128047). Association for Computing Machinery. https://doi.org/10.1145/3036331.3036348

Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population. / Hu, Yao; Min, Lequan; Su, Yongmei et al.
Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017. Association for Computing Machinery, 2017. p. 7-13 (ACM International Conference Proceeding Series; Vol. Part F128047).

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

Hu, Y, Min, L, Su, Y & Kuang, Y 2017, Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population. in Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017. ACM International Conference Proceeding Series, vol. Part F128047, Association for Computing Machinery, pp. 7-13, 8th International Conference on Computer Modeling and Simulation, ICCMS 2017, Canberra, Australia, 1/20/17. https://doi.org/10.1145/3036331.3036348

@inproceedings{e5b10f1f29e74d1fa7b8e80d3056929f,

title = "Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population",

abstract = "Epidemic Spreading is a major global health problem. Modeling epidemic spreading dynamics is important for understanding and controlling epidemic spreading, providing prevention strategies. This paper points out some flaws existing in the susceptibleinfected - susceptible (SIS) model proposed by Safan and Rihan, and proposes a modified susceptible-infected-recovered (SIR) model on homogenous networks. It is proved that if the basic reproduction number Rv of the model is less than one, then the infection-free equilibrium of the model is globally asymptotically stable. On the other hand, if Rv of the model is more than one, the endemic equilibrium of the model is globally asymptotically stable. This paper also numerically predicts the effect of vaccination ratio on the size of HBV infected mainland Chinese population.",

keywords = "Birth and death rate, Global stability, Homogenous network, Local stability, Sir model",

author = "Yao Hu and Lequan Min and Yongmei Su and Yang Kuang",

note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 8th International Conference on Computer Modeling and Simulation, ICCMS 2017 ; Conference date: 20-01-2017 Through 23-01-2017",

year = "2017",

month = jan,

day = "20",

doi = "10.1145/3036331.3036348",

language = "English (US)",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery",

pages = "7--13",

booktitle = "Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017",

}

TY - GEN

T1 - Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population

AU - Hu, Yao

AU - Min, Lequan

AU - Su, Yongmei

AU - Kuang, Yang

PY - 2017/1/20

Y1 - 2017/1/20

N2 - Epidemic Spreading is a major global health problem. Modeling epidemic spreading dynamics is important for understanding and controlling epidemic spreading, providing prevention strategies. This paper points out some flaws existing in the susceptibleinfected - susceptible (SIS) model proposed by Safan and Rihan, and proposes a modified susceptible-infected-recovered (SIR) model on homogenous networks. It is proved that if the basic reproduction number Rv of the model is less than one, then the infection-free equilibrium of the model is globally asymptotically stable. On the other hand, if Rv of the model is more than one, the endemic equilibrium of the model is globally asymptotically stable. This paper also numerically predicts the effect of vaccination ratio on the size of HBV infected mainland Chinese population.

AB - Epidemic Spreading is a major global health problem. Modeling epidemic spreading dynamics is important for understanding and controlling epidemic spreading, providing prevention strategies. This paper points out some flaws existing in the susceptibleinfected - susceptible (SIS) model proposed by Safan and Rihan, and proposes a modified susceptible-infected-recovered (SIR) model on homogenous networks. It is proved that if the basic reproduction number Rv of the model is less than one, then the infection-free equilibrium of the model is globally asymptotically stable. On the other hand, if Rv of the model is more than one, the endemic equilibrium of the model is globally asymptotically stable. This paper also numerically predicts the effect of vaccination ratio on the size of HBV infected mainland Chinese population.

KW - Birth and death rate

KW - Global stability

KW - Homogenous network

KW - Local stability

KW - Sir model

UR - http://www.scopus.com/inward/record.url?scp=85021396001&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85021396001&partnerID=8YFLogxK

U2 - 10.1145/3036331.3036348

DO - 10.1145/3036331.3036348

M3 - Conference contribution

AN - SCOPUS:85021396001

T3 - ACM International Conference Proceeding Series

SP - 7

EP - 13

BT - Proceedings of the 8th International Conference on Computer Modeling and Simulation, ICCMS 2017

PB - Association for Computing Machinery

T2 - 8th International Conference on Computer Modeling and Simulation, ICCMS 2017

Y2 - 20 January 2017 through 23 January 2017

ER -

Mathematical analysis of an SIR network model with imperfect vaccination and varying size of population

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this