Abstract
AbstractTo model the HIV epidemic, there are four different approaches: The deterministic models, the stochastic models, the statistical models, and the state space models. The last three models treat the HIV epidemic as random processes. This article presents a systematic review of these random processes. It illustrates how to develop stochastic models and statistical models of the HIV epidemic and how to combine these models into state space models. Using a homosexual population as an example, it illustrates how to develop stochastic equations for the state variables and how to use the state space models to develop a generalized Bayesian method to estimate the unknown parameters and the state variables. The methodology is illustrated by using the AIDS incidence data of the San Francisco homosexual population.
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