Bird Flu Outbreak Prediction via Satellite Tracking

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 th...

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Bibliographic Details
Published in:IEEE intelligent systems 2014-07, Vol.29 (4), p.10-17
Main Authors: Zhou, Yuanchun, Tang, Mingjie, Pan, Weike, Li, Jinyan, Wang, Weihang, Shao, Jing, Wu, Liang, Li, Jianhui, Yang, Qiang, Yan, Baoping
Format: Article
Language:English
Subjects:
Algorithm design and analysis
Algorithms
Artificial intelligence
Aves
Avian flu
bird flu prediction
Bird migration
Birds
China
computational sustainability
Data mining
Diseases
Dynamics
Epidemics
Graph algorithms
graph mining
Graphs
H5N1 prediction in Qinghai Lake
Habitats
intelligent systems
Machine learning
Migration
Outbreaks
Prediction algorithms
Satellite navigation systems
Satellite tracking
Target tracking
Tracking control systems
wild bird migration data mining
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Summary: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.
ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2013.38