Background: Stochastic models of discrete individuals and deterministic models of continuous populations may give different answers to questions about infectious diseases.
Goal: Discrete individual model formulations are sought that extend deterministic models of infection transmission systems so that both model forms contribute cooperatively to model-based decision making.
Study design: GERMS models are defined as stochastic processes in continuous time with parameters analogous to those in deterministic models. A GERMS model simulator was developed that insured that the rate of events depended only on the current state of model.
Results: The confidence intervals of long-term averages of infection level in simulated GERMS models were shown to contain the deterministic model means.
Conclusion: GERMS models provide a convenient framework for testing the sensitivity of model-based decisions to a variety of unrealistic assumptions that are characteristic of differential equation models. GERMS especially facilitates making more realistic assumptions about contact patterns in geographic and social space.