Bird flu outbreak prediction via satellite tracking

Zhou, Y; Tang, M; Pan, W; Li, J; Wang, W; Shao, J; Wu, L; Yang, Q; Yan, B

Bird flu outbreak prediction via satellite tracking

Zhou, Y Tang, M Pan, W Li, J

Wang, W Shao, J Wu, L Yang, Q Yan, B

Permalink

Publication Type:: Journal Article
Citation:: IEEE Intelligent Systems, 2014, 29 (4), pp. 10 - 17
Issue Date:: 2014-07-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download full textAdobe PDF (801.25 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Zhou, Y	en_US
dc.contributor.author	Tang, M	en_US
dc.contributor.author	Pan, W	en_US
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413	en_US
dc.contributor.author	Wang, W	en_US
dc.contributor.author	Shao, J	en_US
dc.contributor.author	Wu, L	en_US
dc.contributor.author	Yang, Q	en_US
dc.contributor.author	Yan, B	en_US
dc.date.issued	2014-07-01	en_US
dc.identifier.citation	IEEE Intelligent Systems, 2014, 29 (4), pp. 10 - 17	en_US
dc.identifier.issn	1541-1672	en_US
dc.identifier.uri	http://hdl.handle.net/10453/35655
dc.description.abstract	© 2001-2011 IEEE. Advanced satellite tracking technologies have collected huge amounts of wild bird migration data. Biologists use these data to understand dynamic migration patterns, study correlations between habitats, and predict global spreading trends of avian influenza. The research discussed here transforms the biological problem into a machine learning problem by converting wild bird migratory paths into graphs. H5N1 outbreak prediction is achieved by discovering weighted closed cliques from the graphs using the mining algorithm High-wEight cLosed cliquE miNing (HELEN). The learning algorithm HELEN-p then predicts potential H5N1 outbreaks at habitats. This prediction method is more accurate than traditional methods used on a migration dataset obtained through a real satellite bird-tracking system. Empirical analysis shows that H5N1 spreads in a manner of high-weight closed cliques and frequent cliques.	en_US
dc.relation.ispartof	IEEE Intelligent Systems	en_US
dc.relation.isbasedon	10.1109/MIS.2013.38	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Bird flu outbreak prediction via satellite tracking	en_US
dc.type	Journal Article
utslib.citation.volume	4	en_US
utslib.citation.volume	29	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
utslib.for	0806 Information Systems	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
utslib.copyright.status	open_access
pubs.issue	4	en_US
pubs.publication-status	Published	en_US
pubs.volume	29	en_US

Abstract:

© 2001-2011 IEEE. Advanced satellite tracking technologies have collected huge amounts of wild bird migration data. Biologists use these data to understand dynamic migration patterns, study correlations between habitats, and predict global spreading trends of avian influenza. The research discussed here transforms the biological problem into a machine learning problem by converting wild bird migratory paths into graphs. H5N1 outbreak prediction is achieved by discovering weighted closed cliques from the graphs using the mining algorithm High-wEight cLosed cliquE miNing (HELEN). The learning algorithm HELEN-p then predicts potential H5N1 outbreaks at habitats. This prediction method is more accurate than traditional methods used on a migration dataset obtained through a real satellite bird-tracking system. Empirical analysis shows that H5N1 spreads in a manner of high-weight closed cliques and frequent cliques.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/35655