DocumentCode :
3394796
Title :
Characterization of extremal epidemic networks with diffusion characters
Author :
Ashlock, Daniel ; Lee, Colin
Author_Institution :
Dept. of Math. & Stat., Univ. of Guelph, Guelph, ON
fYear :
2008
fDate :
15-17 Sept. 2008
Firstpage :
264
Lastpage :
271
Abstract :
Epidemic models often incorporate contact networks along which the disease can be passed. The connectivity of the network can have a substantial impact on the course of the epidemic. In this study an evolutionary computation system is used to optimizes networks with a fixed distribution of contacts to yield either long-lasting epidemics or epidemics in which a maximal number of individuals are infected in a given time step. These networks represent extremal cases of network behavior. A novel network analysis tool called the diffusion character matrix, derived from the Leontief inverse of a modified adjacency matrix, is used to demonstrate that the networks located for the two optimizations are substantially different. The diffusion character matrix analysis allows us to place several metric-like dissimilarity measure on the space of graphs with a fixed number of nodes. The evolutionary algorithm used searches the space of networks with a specified degree sequence, with degrees representing the number of contacts for each member of the population. The representation used to evolve networks is a linear chromosome specifying a series of degree-preserving editing moves applied to an initial network that specifies the degree sequence of the searched networks. The evolutionary algorithm uses a non-standard type of restart called recentering in which the currently best network in the population replaces the initial network at intervals. The recentering operator moves the evolving population to successively higher fitness regions of the search space. In this study the algorithm is applied to networks with constant degrees from 3 to 7. The diffusion character matrix analysis also demonstrates that the volume of the search space occupied by networks maximizing the number of individuals that fall sick in one time step is much smaller than that occupied by networks that maximize epidemic length.
Keywords :
biology computing; diffusion; diseases; evolutionary computation; Leontief inverse; contact networks; diffusion character matrix; disease; epidemic length; evolutionary algorithm; extremal epidemic networks; linear chromosome; modified adjacency matrix; network connectivity; search space; Biological cells; Diseases; Evolutionary computation; Extraterrestrial measurements; Mathematical model; Network topology; Privacy; Social network services;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Computational Intelligence in Bioinformatics and Computational Biology, 2008. CIBCB '08. IEEE Symposium on
Conference_Location :
Sun Valley, ID
Print_ISBN :
978-1-4244-1778-0
Electronic_ISBN :
978-1-4244-1779-7
Type :
conf
DOI :
10.1109/CIBCB.2008.4675789
Filename :
4675789
Link To Document :
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