CHARACTERIZATION OF HIV-INFECTION AND SEROCONVERSION BY A STOCHASTIC-MODEL OF THE HIV EPIDEMIC

In this paper we use a stochastic model for the HIV epidemic in homose xual populations to characterize the HIV infection and seroconversion. Using computer generated data, we compare the fitting of infection di stributions and of seroconversion distributions by different parametri c models as well as by nonparametric methods. The nonparametric method s include the Kaplan-Meier method, EMS method, Bacchetti's method, and the spline approximation. The parametric models include most of the m odels which have been used in the literature. The comparison criteria are the chi-square statistic, the AIC (Akaike Information Criterion) a nd the residual sums of squares. The numerical results suggest that fo r the proportional mixing pattern, the EMS method, the spline method, Bacchetti's method, and the generalized log-logistic distributions wit h three and with four parameters provide better fitting for the infect ion and the seroconversion distributions in most cases. For the restri cted mixing pattern, the EMS method, the spline method, Bacchetti's me thod, and some mixtures of distributions provide close fitting to the infection and the seroconversion distributions.