Estimation of HIV infection and seroconversion probabilities in IDU and non-IDU populations by state


To estimate the probabilities of HIV infection and HIV seroconversion and to compare HIV seroconversions from different populations, in this paper we have developed some statistical models and state space models for HIV infection and seroconversion. By combining these models with the multi-level Gibbs sampling procedures, in this paper we have developed some efficient methods to estimate simultaneously these probabilities and the state variables as well as other unknown parameters. By using the complete likelihood function, we have also developed a generalized likelihood ratio test for comparing several HIV seroconversion distributions. As an illustration, we have applied the models and the methods to some data generated by the cooperative study on HIV under IDU and cocaine crack users by the National Institute of Drug Abuse/NIH. Our results show that there are significant differences in HIV seroconversion and HIV infection between populations of IDU, homosexuals and individuals with both IDU and homosexual behavior. For homosexuals, IDU and homosexuals with IDU, the probability density functions of times to HIV infection and HIV seroconversion are bi-model curves with two peaks and with heavy weights on the right. The average window period is about 2.75 months. Also, there are significant differences between the death and retirement rates of S (susceptible) people and I (infected but not seroconverted) people in all populations.

Publication Title

Deterministic and Stochastic Models of AIDS Epidemics and HIV Infections with Intervention