A computer exploration of some properties of non-linear stochastic partnership models for sexually transmitted diseases with stages

In this paper, branching process approximations to non-linear stochastic partnership models for sexually transmitted diseases in heterosexual populations were used to find points in the parameter space such that an epidemic would occur. At selected points in the parameter space, samples of Monte Carlo realizations of the process were computed and analyzed statistically to gain insights into the stochastic evolution of epidemics seeded by one infective single female and male. Non-linear difference equations were embedded in the stochastic processes, making it possible to compare trajectories computed according to the deterministic model with those computed from samples of Monte Carlo realizations. From these trajectories it was shown that stochastic fluctuations may have a profound effect on the long-term evolution of an epidemic, and examples demonstrate that an investigator may be misled if a deterministic model alone were used to project an epidemic, particularly when there is a significant probability of extinction.