title: Numerical integration of the master equation in some models of stochastic epidemiology.
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.
authors.affiliation: Whitaker Biomedical Engineering Institute, The Johns Hopkins University, Baltimore, Maryland, United States of America.
authors.forename: Garrett
authors.initials: G
authors.lastname: Jenkinson
authors.forename: John
authors.initials: J
authors.lastname: Goutsias
mesh_terms: D004813:Epidemiology; D011336:Probability; D013269:Stochastic Processes