DocumentCode :
2320007
Title :
Evolving a social fabric to fit and epidemic profile
Author :
Ashlock, Daniel ; Shiller, Elisabeth
Author_Institution :
Dept. of Math. & Stat., Univ. of Guelph, Guelph, ON, Canada
fYear :
2012
fDate :
9-12 May 2012
Firstpage :
363
Lastpage :
370
Abstract :
Epidemic models often incorporate contact networks along which the disease can be passed. This study follows up on an earlier one which evolved full general contact networks. This study uses an evolvable network representation inspired by the idea of a social fabric. The resulting representation is based on selecting overlapping groups of agents that interact as if they are well mixed. The groups in this representation are intended to represent groups that are, in fact, well mixed such as schools, families, or workplaces. The new representation permits a substantial improvement in the speed with which a contact model can be fit to an epidemic profile. There is a cost in the form of additional model parameters that must be tuned. A parameter setting study is performed for a simple epidemic profile, providing proof of concept for the evolvable social fabric representation. A number of potential improvements and directions for future work are outlined.
Keywords :
complex networks; epidemics; evolutionary computation; epidemic models; epidemic profile; evolvable network representation; evolvable social fabric representation; full general contact networks; overlapping agent groups; parameter setting study; social fabric evolution; Computational modeling; Contracts; Diseases; Educational institutions; Evolutionary computation; Fabrics; Social network services; epidemiology; evolutionary computation; evolvable network; network representation; social fabric;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), 2012 IEEE Symposium on
Conference_Location :
San Diego, CA
Print_ISBN :
978-1-4673-1190-8
Type :
conf
DOI :
10.1109/CIBCB.2012.6217253
Filename :
6217253
Link To Document :
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