Simulation Algorithms for Continuous Time Markov Chain Models
Document Type
Conference Proceeding
Publication Date
12-1-2012
Description
Continuous time Markov chains are often used in the literature to model the dynamics of a system with low species count and uncertainty in transitions. In this paper, we investigate three particular algorithms that can be used to numerically simulate continuous time Markov chain models (a stochastic simulation algorithm, explicit and implicit tau-leaping algorithms). To compare these methods, we used them to analyze two stochastic infection models with different level of complexity. One of these models describes the dynamics of Vancomycin-Resistant Enterococcus (VRE) infection in a hospital, and the other is for the early infection of Human Immunodeficiency Virus (HIV) within a host. 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 similar computational efficiency for the VRE model due to the low number of species and small number of transitions. However, we found that with the larger and more complex HIV model, implementation and modification of tau-Leaping methods are preferred.
Citation Information
Banks, H. T.; Broido, Anna; Canter, Brandi; Gayvert, Kaitlyn; Hu, Shuhua; Joyner, Michele; and Link, Kathryn. 2012. Simulation Algorithms for Continuous Time Markov Chain Models. Studies in Applied Electromagnetics and Mechanics. Vol.37 3-18. https://doi.org/10.3233/978-1-61499-092-5-3 ISSN: 1383-7281 ISBN: 9781614990918