Document Type

Conference Paper

Department/Unit

Department of Computer Science

Title

Inferring metapopulation based disease transmission networks

Language

English

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. © 2014 Springer International Publishing.

Keywords

Bayesian learning, disease transmission networks, metapopulation, Network inference, partial correlation networks

Publication Date

2014

Source Publication Title

Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part II

Start Page

385

End Page

399

Conference Location

Tainan, Taiwan

Publisher

Springer International Publishing

DOI

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

ISBN (print)

9783319066042

ISBN (electronic)

9783319066059

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