ESTIMATION OF THE LIKELY TIME FOR SEROCONVERSION IN HIV INFECTION

Abstract

The acronym AIDS was coined to describe an Acquired Immune Deficiency Syndrome. The causative agent of this disease is human retrovirus – Human Immunodeficiency Virus. AIDS is a chronic, life-threatening condition caused by the HIV. As a retrovirus HIV shows a high replication error rate and leads to the creation of distinct viral genomes with different immunological properties. This character of HIV is called antigenic diversity. Thus, due to the ongoing antigenic variation more and more mutant specific immune responses become activated. It is well known that the HIV transmission occurs through various modes and the most important, widely prevalent mode of transmission is the homo or hetro sexual contacts. On the successive occasion of contact with one or more infected partners, the transmission of more number of HIV is facilitated, which in turn contributes to the increase in antigenic diversity of HIV. The antigenic diversity threshold is a particular level of the antigenic diversity, above which the immune system is unable to suppress the viral population. According to Stilianakis et. al. (1994), the total virus population may escape control through continued generation of new mutants until the total number of different HIV strains exceeds the diversity threshold. This leads to the onset of AIDS symptoms in an infected person. HIV infection and its spread is a serious problem affecting not only the social life of the people but also the economy of the country itself. The progression of HIV is mainly due to what is called the antigenic diversity of the antigen which is HIV. If the antigenic diversity crosses the threshold level then the seroconversion will take place. In this paper two different models are discussed. In model 1, a Stochastic model is discussed for the purpose of estimating the time to seroconversion of the HIV infected. In model 2, a stochastic model is developed under the assumption that the threshold level which is a random variable undergoes a parametric change after crossing a truncation point. The expected time to seroconversion is estimated and its variance is also found out using the Shock model and cumulative damage concept.