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Numerical Integration of the Master Equation in Some Models of Stochastic Epidemiology

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  • Garrett Jenkinson
  • John Goutsias

Abstract

The processes by which disease spreads in a population of individuals are inherently stochastic. The master equation has proven to be a useful tool for modeling such processes. Unfortunately, solving the master equation analytically is possible only in limited cases (e.g., when the model is linear), and thus numerical procedures or approximation methods must be employed. Available approximation methods, such as the system size expansion method of van Kampen, may fail to provide reliable solutions, whereas current numerical approaches can induce appreciable computational cost. In this paper, we propose a new numerical technique for solving the master equation. Our method is based on a more informative stochastic process than the population process commonly used in the literature. By exploiting the structure of the master equation governing this process, we develop a novel technique for calculating the exact solution of the master equation – up to a desired precision – in certain models of stochastic epidemiology. We demonstrate the potential of our method by solving the master equation associated with the stochastic SIR epidemic model. MATLAB software that implements the methods discussed in this paper is freely available as Supporting Information S1.

Suggested Citation

  • Garrett Jenkinson & John Goutsias, 2012. "Numerical Integration of the Master Equation in Some Models of Stochastic Epidemiology," PLOS ONE, Public Library of Science, vol. 7(5), pages 1-9, May.
    • Handle: RePEc:plo:pone00:0036160
    DOI: 10.1371/journal.pone.0036160
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    References listed on IDEAS

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    1. Sidje, Roger B. & Stewart, William J., 1999. "A numerical study of large sparse matrix exponentials arising in Markov chains," Computational Statistics & Data Analysis, Elsevier, vol. 29(3), pages 345-368, January.
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    Cited by:

    1. Ankit Gupta & Corentin Briat & Mustafa Khammash, 2014. "A Scalable Computational Framework for Establishing Long-Term Behavior of Stochastic Reaction Networks," PLOS Computational Biology, Public Library of Science, vol. 10(6), pages 1-16, June.
    2. Bashkirtseva, Irina & Perevalova, Tatyana & Ryashko, Lev, 2022. "Regular and chaotic variability caused by random disturbances in a predator–prey system with disease in predator," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    3. Timothy Kinyanjui & Jo Middleton & Stefan Güttel & Jackie Cassell & Joshua Ross & Thomas House, 2018. "Scabies in residential care homes: Modelling, inference and interventions for well-connected population sub-units," PLOS Computational Biology, Public Library of Science, vol. 14(3), pages 1-24, March.
    4. Chris Sherlock, 2021. "Direct statistical inference for finite Markov jump processes via the matrix exponential," Computational Statistics, Springer, vol. 36(4), pages 2863-2887, December.

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