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Conference Paper: Event-based structural change detection in urban-scale contact network
Title | Event-based structural change detection in urban-scale contact network |
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Authors | |
Keywords | Change-point Detection Structural Change Multiple Data Sources |
Issue Date | 2017 |
Publisher | Association for the Advancement of Artificial Intelligence. |
Citation | Workshops at the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, 4-5 February 2017. In The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence, 2017, p. 529-532 How to Cite? |
Abstract | © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The detection of structural changes is an important task in analyzing network evolution, especially for interactions between people, that may be driven by external events. Existing work relies on snapshot data and misses out some key functions of networks. Here, we study contact network evolution where no snapshot data are available. In spite of the challenge, this study demonstrates how contact networks can be used to predict and control infectious disease epidemics. We first model structural changes in contact networks during the 2009 influenza pandemic in Hong Kong, and then present a probabilistic framework to address it, aiming to answer when and how the underlying structure changes, utilizing multiple data sources including demographic data, and epidemic surveillance data. The efficacy and public health utility of the method are demonstrated using both synthetic and real data. |
Description | WS-17-09: Joint Workshop on Health Intelligence |
Persistent Identifier | http://hdl.handle.net/10722/296168 |
ISBN |
DC Field | Value | Language |
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dc.contributor.author | Bai, Yuan | - |
dc.contributor.author | Yang, Bo | - |
dc.contributor.author | Eggo, Rosalind M. | - |
dc.contributor.author | Du, Zhanwei | - |
dc.date.accessioned | 2021-02-11T04:52:59Z | - |
dc.date.available | 2021-02-11T04:52:59Z | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Workshops at the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17), San Francisco, CA, 4-5 February 2017. In The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence, 2017, p. 529-532 | - |
dc.identifier.isbn | 9781577357865 | - |
dc.identifier.uri | http://hdl.handle.net/10722/296168 | - |
dc.description | WS-17-09: Joint Workshop on Health Intelligence | - |
dc.description.abstract | © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. The detection of structural changes is an important task in analyzing network evolution, especially for interactions between people, that may be driven by external events. Existing work relies on snapshot data and misses out some key functions of networks. Here, we study contact network evolution where no snapshot data are available. In spite of the challenge, this study demonstrates how contact networks can be used to predict and control infectious disease epidemics. We first model structural changes in contact networks during the 2009 influenza pandemic in Hong Kong, and then present a probabilistic framework to address it, aiming to answer when and how the underlying structure changes, utilizing multiple data sources including demographic data, and epidemic surveillance data. The efficacy and public health utility of the method are demonstrated using both synthetic and real data. | - |
dc.language | eng | - |
dc.publisher | Association for the Advancement of Artificial Intelligence. | - |
dc.relation.ispartof | The Workshops of the Thirty-First AAAI Conference on Artificial Intelligence | - |
dc.subject | Change-point Detection | - |
dc.subject | Structural Change | - |
dc.subject | Multiple Data Sources | - |
dc.title | Event-based structural change detection in urban-scale contact network | - |
dc.type | Conference_Paper | - |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.scopus | eid_2-s2.0-85046102578 | - |
dc.identifier.spage | 529 | - |
dc.identifier.epage | 532 | - |