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A comparison of computational efficiencies of stochastic algorithms in terms of two infection models
Author(s) -
H. T. Banks,
Shuhua Hu,
Michele L. Joyner,
Anna Broido,
Brandi N. Canter,
Kaitlyn Gayvert,
Kathryn G. Link
Publication year - 2012
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2012.9.487
Subject(s) - algorithm , simplicity , human immunodeficiency virus (hiv) , computer science , population , computational model , biology , immunology , demography , sociology , philosophy , epistemology
In this paper, we investigate three particular algorithms: a stochastic simulation algorithm (SSA), and explicit and implicit tau-leaping algorithms. To compare these methods, we used them to analyze two infection models: a Vancomycin-resistant enterococcus (VRE) infection model at the population level, and a Human Immunodeficiency Virus (HIV) within host infection model. While the first has a low species count and few transitions, the second is more complex with a comparable number of species involved. The relative efficiency of each algorithm is determined based on computational time and degree of precision required. The numerical results suggest that all three algorithms have the similar computational efficiency for the simpler VRE model, and the SSA is the best choice due to its simplicity and accuracy. In addition, we have found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.

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