Statistical analysis and a social network model based on the SEIQR framework

B. Chimmalee; W. Sawangtong; R. Suwandechochai; F. Chamchod

doi:10.1109/IEEM.2014.7058671

Statistical analysis and a social network model based on the SEIQR framework

Mahidol University

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

Abstract

Understanding, the spread of infectious diseases is an important key to efficiently control them. In this study, a susceptible-exposed-infectious-quarantined-recovered (SEIQR) model incorporated with adynamic social network is proposed to investigate the disease transmission dynamics in the human population and how the number of individual's neighbor (degree of a node), and the longest distance between any two neighboring nodes (the contact radius) influence the number of infectious individuals. Our results indicate that(l) the larger contact radius of an individual node leads to the higher number of infectious individuals (2) the degree of a node has significant effect on individual infection (the higher the degree of the node, the higher the possibility that individuals represented by those nodes spread the disease) and (3) the probability of successful infection can be estimated as a function of the degree of a node by the binary logistic regression model and we found that it may affect the outbreak period.

Original language	English
Title of host publication	IEEM 2014 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management
Publisher	IEEE Computer Society
Pages	414-418
Number of pages	5
ISBN (Electronic)	9781479964109
DOIs	https://doi.org/10.1109/IEEM.2014.7058671
Publication status	Published - 2014
Externally published	Yes
Event	2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014 - Selangor, Malaysia Duration: 9 Dec 2014 → 12 Dec 2014

Publication series

Name	IEEE International Conference on Industrial Engineering and Engineering Management
Volume	2015-January
ISSN (Print)	2157-3611
ISSN (Electronic)	2157-362X

Conference

Conference	2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014
Country/Territory	Malaysia
City	Selangor
Period	9/12/14 → 12/12/14

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

SEIQR model
degree of a node
disease transmission
social networks

Access to Document

10.1109/IEEM.2014.7058671

Cite this

Chimmalee, B., Sawangtong, W., Suwandechochai, R., & Chamchod, F. (2014). Statistical analysis and a social network model based on the SEIQR framework. In IEEM 2014 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management (pp. 414-418). Article 7058671 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2015-January). IEEE Computer Society. https://doi.org/10.1109/IEEM.2014.7058671

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title = "Statistical analysis and a social network model based on the SEIQR framework",

abstract = "Understanding, the spread of infectious diseases is an important key to efficiently control them. In this study, a susceptible-exposed-infectious-quarantined-recovered (SEIQR) model incorporated with adynamic social network is proposed to investigate the disease transmission dynamics in the human population and how the number of individual's neighbor (degree of a node), and the longest distance between any two neighboring nodes (the contact radius) influence the number of infectious individuals. Our results indicate that(l) the larger contact radius of an individual node leads to the higher number of infectious individuals (2) the degree of a node has significant effect on individual infection (the higher the degree of the node, the higher the possibility that individuals represented by those nodes spread the disease) and (3) the probability of successful infection can be estimated as a function of the degree of a node by the binary logistic regression model and we found that it may affect the outbreak period.",

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author = "B. Chimmalee and W. Sawangtong and R. Suwandechochai and F. Chamchod",

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Chimmalee, B, Sawangtong, W , Suwandechochai, R & Chamchod, F 2014, Statistical analysis and a social network model based on the SEIQR framework. in IEEM 2014 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management., 7058671, IEEE International Conference on Industrial Engineering and Engineering Management, vol. 2015-January, IEEE Computer Society, pp. 414-418, 2014 IEEE International Conference on Industrial Engineering and Engineering Management, IEEM 2014, Selangor, Malaysia, 9/12/14. https://doi.org/10.1109/IEEM.2014.7058671

Statistical analysis and a social network model based on the SEIQR framework. / Chimmalee, B.; Sawangtong, W.; Suwandechochai, R. et al.
IEEM 2014 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE Computer Society, 2014. p. 414-418 7058671 (IEEE International Conference on Industrial Engineering and Engineering Management; Vol. 2015-January).

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

TY - GEN

T1 - Statistical analysis and a social network model based on the SEIQR framework

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AU - Sawangtong, W.

AU - Suwandechochai, R.

AU - Chamchod, F.

PY - 2014

Y1 - 2014

N2 - Understanding, the spread of infectious diseases is an important key to efficiently control them. In this study, a susceptible-exposed-infectious-quarantined-recovered (SEIQR) model incorporated with adynamic social network is proposed to investigate the disease transmission dynamics in the human population and how the number of individual's neighbor (degree of a node), and the longest distance between any two neighboring nodes (the contact radius) influence the number of infectious individuals. Our results indicate that(l) the larger contact radius of an individual node leads to the higher number of infectious individuals (2) the degree of a node has significant effect on individual infection (the higher the degree of the node, the higher the possibility that individuals represented by those nodes spread the disease) and (3) the probability of successful infection can be estimated as a function of the degree of a node by the binary logistic regression model and we found that it may affect the outbreak period.

AB - Understanding, the spread of infectious diseases is an important key to efficiently control them. In this study, a susceptible-exposed-infectious-quarantined-recovered (SEIQR) model incorporated with adynamic social network is proposed to investigate the disease transmission dynamics in the human population and how the number of individual's neighbor (degree of a node), and the longest distance between any two neighboring nodes (the contact radius) influence the number of infectious individuals. Our results indicate that(l) the larger contact radius of an individual node leads to the higher number of infectious individuals (2) the degree of a node has significant effect on individual infection (the higher the degree of the node, the higher the possibility that individuals represented by those nodes spread the disease) and (3) the probability of successful infection can be estimated as a function of the degree of a node by the binary logistic regression model and we found that it may affect the outbreak period.

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Chimmalee B, Sawangtong W , Suwandechochai R , Chamchod F. Statistical analysis and a social network model based on the SEIQR framework. In IEEM 2014 - 2014 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE Computer Society. 2014. p. 414-418. 7058671. (IEEE International Conference on Industrial Engineering and Engineering Management). doi: 10.1109/IEEM.2014.7058671

Statistical analysis and a social network model based on the SEIQR framework

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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