Inferring Metapopulation Based Disease Transmission Networks

Xiaofei Yang; Jiming Liu; Kwok Wai Cheung; Xiao Nong Zhou

doi:10.1007/978-3-319-06605-9_32

Inferring Metapopulation Based Disease Transmission Networks

Xiaofei Yang, Jiming Liu, Kwok Wai Cheung, Xiao Nong Zhou

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

4 Citations (Scopus)

Abstract

To investigate how an infectious disease spreads, it is most desirable to discover the underlying disease transmission networks based on surveillance data. Existing studies have provided some methods for inferring information diffusion networks, where nodes correspond to individual persons. However, in the case of disease transmission, to effectively develop intervention strategies, it would be more realistic and reasonable for policy makers to study the diffusion patterns at the metapopulation level, that is, to consider disease transmission networks where nodes represent subpopulations, and links indicate their interrelationships. Such networks are useful to: (i) investigate hidden factors that influence epidemic dynamics, (ii) reveal possible sources of epidemic outbreaks, and (iii) practically develop and improve strategies for disease control. Therefore, based on such a real-world motivation, we aim to address the problem of inferring disease transmission networks at the metapopulation level. Specifically, we propose an inference method called NetEpi (Network Epidemic), and evaluate the method by utilizing synthetic and real-world datasets. The experiments show that NetEpi can recover most of the ground-truth disease transmission networks based only on the surveillance data. Moreover, it can help detect and interpret patterns and transmission pathways from the real-world data.

Original language	English
Title of host publication	Advances in Knowledge Discovery and Data Mining
Subtitle of host publication	18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II
Editors	Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L. P. Chen, Hung-Yu Kao
Place of Publication	Cham
Publisher	Springer
Pages	385-399
Number of pages	15
Edition	1st
ISBN (Electronic)	9783319066059
ISBN (Print)	9783319066042
DOIs	https://doi.org/10.1007/978-3-319-06605-9_32
Publication status	Published - 8 May 2014
Event	18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 - Tainan, Taiwan, Province of China Duration: 13 May 2014 → 16 May 2014 https://link.springer.com/book/10.1007/978-3-319-06608-0 https://link.springer.com/book/10.1007/978-3-319-06605-9 https://link.springer.com/book/10.1007/978-3-319-13186-3

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer
Volume	8444
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Name	Lecture Notes in Artificial Intelligence
ISSN (Print)	2945-9133
ISSN (Electronic)	2945-9141

Name	PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining

Conference

Conference	18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014
Country/Territory	Taiwan, Province of China
City	Tainan
Period	13/05/14 → 16/05/14
Internet address	https://link.springer.com/book/10.1007/978-3-319-06608-0 https://link.springer.com/book/10.1007/978-3-319-06605-9 https://link.springer.com/book/10.1007/978-3-319-13186-3

Scopus Subject Areas

Theoretical Computer Science
Computer Science(all)

User-Defined Keywords

Bayesian learning
disease transmission networks
metapopulation
Network inference
partial correlation networks

UN SDGs

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

Access to Document

10.1007/978-3-319-06605-9_32

Cite this

Yang, X., Liu, J., Cheung, K. W., & Zhou, X. N. (2014). Inferring Metapopulation Based Disease Transmission Networks. In V. S. Tseng, T. B. Ho, Z.-H. Zhou, A. L. P. Chen, & H.-Y. Kao (Eds.), Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II (1st ed., pp. 385-399). (Lecture Notes in Computer Science; Vol. 8444), (Lecture Notes in Artificial Intelligence), (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining). Springer. https://doi.org/10.1007/978-3-319-06605-9_32

Yang, Xiaofei ; Liu, Jiming ; Cheung, Kwok Wai et al. / Inferring Metapopulation Based Disease Transmission Networks. Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II. editor / Vincent S. Tseng ; Tu Bao Ho ; Zhi-Hua Zhou ; Arbee L. P. Chen ; Hung-Yu Kao. 1st. ed. Cham : Springer, 2014. pp. 385-399 (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining).

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title = "Inferring Metapopulation Based Disease Transmission Networks",

abstract = "To investigate how an infectious disease spreads, it is most desirable to discover the underlying disease transmission networks based on surveillance data. Existing studies have provided some methods for inferring information diffusion networks, where nodes correspond to individual persons. However, in the case of disease transmission, to effectively develop intervention strategies, it would be more realistic and reasonable for policy makers to study the diffusion patterns at the metapopulation level, that is, to consider disease transmission networks where nodes represent subpopulations, and links indicate their interrelationships. Such networks are useful to: (i) investigate hidden factors that influence epidemic dynamics, (ii) reveal possible sources of epidemic outbreaks, and (iii) practically develop and improve strategies for disease control. Therefore, based on such a real-world motivation, we aim to address the problem of inferring disease transmission networks at the metapopulation level. Specifically, we propose an inference method called NetEpi (Network Epidemic), and evaluate the method by utilizing synthetic and real-world datasets. The experiments show that NetEpi can recover most of the ground-truth disease transmission networks based only on the surveillance data. Moreover, it can help detect and interpret patterns and transmission pathways from the real-world data.",

keywords = "Bayesian learning, disease transmission networks, metapopulation, Network inference, partial correlation networks",

author = "Xiaofei Yang and Jiming Liu and Cheung, {Kwok Wai} and Zhou, {Xiao Nong}",

note = "The authors would like to acknowledge the funding support from Hong Kong Research Grants Council (HKBU211212) and from National Natural Science Foundation of China (NSFC81273192) for the research work being presented in this paper.; 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014 ; Conference date: 13-05-2014 Through 16-05-2014",

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pages = "385--399",

editor = "Tseng, {Vincent S.} and Ho, {Tu Bao} and Zhi-Hua Zhou and Chen, {Arbee L. P.} and Hung-Yu Kao",

booktitle = "Advances in Knowledge Discovery and Data Mining",

edition = "1st",

url = "https://link.springer.com/book/10.1007/978-3-319-06608-0, https://link.springer.com/book/10.1007/978-3-319-06605-9, https://link.springer.com/book/10.1007/978-3-319-13186-3",

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Yang, X, Liu, J , Cheung, KW & Zhou, XN 2014, Inferring Metapopulation Based Disease Transmission Networks. in VS Tseng, TB Ho, Z-H Zhou, ALP Chen & H-Y Kao (eds), Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II. 1st edn, Lecture Notes in Computer Science, vol. 8444, Lecture Notes in Artificial Intelligence, PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Cham, pp. 385-399, 18th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2014, Tainan, Taiwan, Province of China, 13/05/14. https://doi.org/10.1007/978-3-319-06605-9_32

Inferring Metapopulation Based Disease Transmission Networks. / Yang, Xiaofei; Liu, Jiming ; Cheung, Kwok Wai et al.
Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II. ed. / Vincent S. Tseng; Tu Bao Ho; Zhi-Hua Zhou; Arbee L. P. Chen; Hung-Yu Kao. 1st. ed. Cham: Springer, 2014. p. 385-399 (Lecture Notes in Computer Science; Vol. 8444), (Lecture Notes in Artificial Intelligence), (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Inferring Metapopulation Based Disease Transmission Networks

AU - Yang, Xiaofei

AU - Liu, Jiming

AU - Cheung, Kwok Wai

AU - Zhou, Xiao Nong

N1 - The authors would like to acknowledge the funding support from Hong Kong Research Grants Council (HKBU211212) and from National Natural Science Foundation of China (NSFC81273192) for the research work being presented in this paper.

PY - 2014/5/8

Y1 - 2014/5/8

N2 - To investigate how an infectious disease spreads, it is most desirable to discover the underlying disease transmission networks based on surveillance data. Existing studies have provided some methods for inferring information diffusion networks, where nodes correspond to individual persons. However, in the case of disease transmission, to effectively develop intervention strategies, it would be more realistic and reasonable for policy makers to study the diffusion patterns at the metapopulation level, that is, to consider disease transmission networks where nodes represent subpopulations, and links indicate their interrelationships. Such networks are useful to: (i) investigate hidden factors that influence epidemic dynamics, (ii) reveal possible sources of epidemic outbreaks, and (iii) practically develop and improve strategies for disease control. Therefore, based on such a real-world motivation, we aim to address the problem of inferring disease transmission networks at the metapopulation level. Specifically, we propose an inference method called NetEpi (Network Epidemic), and evaluate the method by utilizing synthetic and real-world datasets. The experiments show that NetEpi can recover most of the ground-truth disease transmission networks based only on the surveillance data. Moreover, it can help detect and interpret patterns and transmission pathways from the real-world data.

AB - To investigate how an infectious disease spreads, it is most desirable to discover the underlying disease transmission networks based on surveillance data. Existing studies have provided some methods for inferring information diffusion networks, where nodes correspond to individual persons. However, in the case of disease transmission, to effectively develop intervention strategies, it would be more realistic and reasonable for policy makers to study the diffusion patterns at the metapopulation level, that is, to consider disease transmission networks where nodes represent subpopulations, and links indicate their interrelationships. Such networks are useful to: (i) investigate hidden factors that influence epidemic dynamics, (ii) reveal possible sources of epidemic outbreaks, and (iii) practically develop and improve strategies for disease control. Therefore, based on such a real-world motivation, we aim to address the problem of inferring disease transmission networks at the metapopulation level. Specifically, we propose an inference method called NetEpi (Network Epidemic), and evaluate the method by utilizing synthetic and real-world datasets. The experiments show that NetEpi can recover most of the ground-truth disease transmission networks based only on the surveillance data. Moreover, it can help detect and interpret patterns and transmission pathways from the real-world data.

KW - Bayesian learning

KW - disease transmission networks

KW - metapopulation

KW - Network inference

KW - partial correlation networks

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Yang X, Liu J , Cheung KW, Zhou XN. Inferring Metapopulation Based Disease Transmission Networks. In Tseng VS, Ho TB, Zhou ZH, Chen ALP, Kao HY, editors, Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II. 1st ed. Cham: Springer. 2014. p. 385-399. (Lecture Notes in Computer Science). (Lecture Notes in Artificial Intelligence). (PAKDD: Pacific-Asia Conference on Knowledge Discovery and Data Mining). doi: 10.1007/978-3-319-06605-9_32

Inferring Metapopulation Based Disease Transmission Networks

Abstract

Publication series

Conference

Scopus Subject Areas

User-Defined Keywords

UN SDGs

Access to Document

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