Paper Info
Title
Simulating Individual-Based Models of Epidemics in Hierarchical Networks
Abstract
Current mathematical modeling methods for the spreading of infectious diseases are too simplified and do not scale well. We present the Simulator of Epidemic Evolution in Complex Networks (SEECN), an efficient simulator of detailed individual-based models by parameterizing separate dynamics operators, which are iteratively applied to the contact network. We reduce the network generator's computational complexity, improve cache efficiency and parallelize the simulator. To evaluate its running time we experiment with an HIV epidemic model that incorporates up to one million homosexual men in a scale-free network, including hierarchical community structure, social dynamics and multi-stage intranode progression. We find that the running times are feasible, on the order of minutes, and argue that SEECN can be used to study realistic epidemics and its properties experimentally, in contrast to defining and solving ever more complicated mathematical models as is the current practice.
Year
DOI
Venue
2009
10.1007/978-3-642-01970-8_72
Journal of Pharmacology and Experimental Therapeutics
Keywords
Field
DocType
individual-based models,efficient simulator,current practice,epidemic evolution,contact network,complex networks,network generator,hierarchical networks,scale-free network,separate dynamics operator,complicated mathematical model,current mathematical modeling method,mathematical model,computational complexity,social dynamic,community structure,infectious disease,scale free network,complex network,epidemic model
Epidemic model,Computer science,Cache,Operator (computer programming),Memory access pattern,Social dynamics,Complex network,Mathematical model,Distributed computing,Computational complexity theory
Conference
Volume
ISSN
Citations 
5544
0302-9743
1
PageRank 
References 
Authors
0.37
3
3
Name
Order
Citations
PageRank
Rick Quax1194.49
Bader, David A.22507219.90
Peter M. Sloot311.05