Determination of threshold conditions for a non-linear stochastic partnership model for heterosexually transmitted diseases with stages

Math Biosci. 2002 May-Jun:177-178:287-315. doi: 10.1016/s0025-5564(01)00093-1.

Abstract

When comparing the performance of a stochastic model of an epidemic at two points in a parameter space, a threshold is said to have been crossed when at one point an epidemic develops with positive probability; while at the other there is a tendency for an epidemic to become extinct. The approach used to find thresholds in this paper was to embed a system of ordinary non-linear differential equations in a stochastic process, accommodating the formation and dissolution of marital partnerships in a heterosexual population, extra-marital sexual contacts, and diseases such as HIV/AIDS with stages. A symbolic representation of the Jacobian matrix of this system was derived. To determine whether this matrix was stable or non-stable at a particular parameter point, the Jacobian was evaluated at a disease-free equilibrium and its eigenvalues were computed. The stability or non-stability of the matrix was then determined by checking if all real parts of the eigenvalues were negative. By writing software to repeat this process for a selected set of points in the parameter space, it was possible to develop search engines for finding points in the parameter space where thresholds were crossed. The results of a set of Monte Carlo simulation experiments were reported which suggest that, by combining the stochastic and deterministic paradigms within a single formulation, it was possible to obtain more informative interpretations of simulation experiments than if attention were confined solely to either paradigm.

Publication types

  • Comparative Study

MeSH terms

  • Acquired Immunodeficiency Syndrome / epidemiology*
  • Acquired Immunodeficiency Syndrome / transmission
  • Algorithms
  • Computer Simulation
  • Extramarital Relations*
  • Female
  • HIV / growth & development*
  • Heterosexuality
  • Humans
  • Male
  • Marriage*
  • Models, Immunological*
  • Stochastic Processes