Paper Info
Title
Cluster-Based Epidemic Control through Smartphone-Based Body Area Networks
Abstract
Increasing population density, closer social contact and interactions make epidemic control difficult. Traditional offline epidemic control methods (e.g., using medical survey or medical records) or model-based approach are not effective due to its inability to gather health data and social contact information simultaneously or impractical statistical assumption about the dynamics of social contact networks, respectively. In addition, it is challenging to find optimal sets of people to be quarantined to contain the spread of epidemics for large populations due to high computational complexity. Unlike these approaches, in this paper, a novel cluster-based epidemic control scheme is proposed based on Smartphone-based body area networks. The proposed scheme divides the populations into multiple clusters based on their physical location and social contact information. The proposed control schemes are applied within the cluster or between clusters. Further, we develop a computational efficient approach called UGP to enable an effective clusterbased quarantine strategy using graph theory for large scale networks (i.e., populations). The effectiveness of the proposed methods is demonstrated through both simulations and experiments on real social contact networks.
Year
DOI
Venue
2015
10.1109/TPDS.2014.2313331
IEEE Trans. Parallel Distrib. Syst.
Keywords
Field
DocType
medical records,social networks,clustering,clustering algorithms,sensors,statistics,sociology,health care,graph theory,population density
Graph theory,Data mining,Cluster (physics),Computer science,Cluster analysis,Statistical assumption,Computational complexity theory,Distributed computing
Journal
Volume
Issue
ISSN
26
3
1045-9219
Citations 
PageRank 
References 
9
0.64
13
Authors
4
Name
Order
Citations
PageRank
Z. Zhang12308198.54
Honggang Wang21365124.06
Chonggang Wang3382.14
Hua Fang434332.48