Export file:

Format

  • RIS(for EndNote,Reference Manager,ProCite)
  • BibTex
  • Text

Content

  • Citation Only
  • Citation and Abstract

A comparison of computational efficiencies of stochastic algorithms in terms of two infection models

1. Center for Research in Scientific Computation, Center for Quantitative Sciences in Biomedicine, North Carolina State University, Raleigh, NC 27695-8212
2. Department of Mathematics and Statistics, East Tennessee State University, Johnson City, TN 37614-70663
3. Department of Mathematics, Boston College, Chestnut Hill, MA 02467-3806
4. Department of Mathematics, State University of New York at Geneseo, Geneseo, NY 14454
5. Department of Mathematics, Bryn Mawr College, Bryn Mawr, PA 19010-2899

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.
  Figure/Table
  Supplementary
  Article Metrics

Keywords continuous time Markov chain models; Gillespie; tau-leaping; Dynamical models; stochastic simulation algorithms; bacterial and viral infection models.

Citation: H. Thomas Banks, Shuhua Hu, Michele Joyner, Anna Broido, Brandi Canter, Kaitlyn Gayvert, Kathryn Link. A comparison of computational efficiencies of stochastic algorithms in terms of two infection models. Mathematical Biosciences and Engineering, 2012, 9(3): 487-526. doi: 10.3934/mbe.2012.9.487

 

This article has been cited by

  • 1. P.A. Maginnis, M. West, G.E. Dullerud, Variance-reduced simulation of lattice discrete-time Markov chains with applications in reaction networks, Journal of Computational Physics, 2016, 322, 400, 10.1016/j.jcp.2016.06.019
  • 2. P. A. Maginnis, M. West, G. E. Dullerud, Exact Variance-Reduced Simulation of Lattice Continuous-Time Markov Chains with Applications in Reaction Networks, Bulletin of Mathematical Biology, 2019, 10.1007/s11538-019-00576-2

Reader Comments

your name: *   your email: *  

Copyright Info: 2012, , licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

Download full text in PDF

Export Citation

Copyright © AIMS Press All Rights Reserved