This paper focuses on the study of a Stochastic Model for predicting the

seroconversion time of HIV transmission. As the immune capacities of an individual

vary and also have its own resistance, the antigenic diversity threshold is different

for different person. We propose a stochastic model to study the damage process

acting on the immune system that is non- linear. The mean of seroconversion time of

HIV and its variance are derived. A numerical example is given to illustrate the

seroconversion times of HIV transmission

Kannan, R

Nanthakumar, C

Santhakumaran, A

Venkatesan, P

NIRT Institutional Repository

A STOCHASTIC MODEL FOR SEROCONVERSION TIMES OF HIV TRANSMISSION A. SanthakumaraN, C. Nanthakumar, P, Venkatesan* and R. Kannan** Department of Statistics, Salem Sowdaswari College, Salem - 636 010, India ABSTRACT This paper focuses on the study of a Stochastic Model for predicting the seroconversion time of HIV transmission. As the immune capacities of an individual vary and also have its own resistance, the antigenic diversity threshold is different for different person. We propose a stochastic model to study the damage process acting on the immune system that is non- linear. The mean of seroconversion time of HIV and its variance are derived. A numerical example is given to illustrate the seroconversion times of HIV transmission. Key words : Antigenic diversity threshold, T4, Cells, Seroconversion, Human Immuno deficiency Virus. Acquired lmmuno Deficiency Syndrome. Alpha poisson Process. Mittag- Laffler distribution. INTRODUCTION The study of the science of epidemiology has gained great significance to the epidemic diseases, namely, Severe Acute Respiratory Syndrome (SARS) and Acquired Immuno Deficiency Syndrome (AIDS). SARS is a respiratory illness that has recently reported in Asia, North America, and Europe. SARS has rapidly generated a serious worldwide concern within a short span of time. In 1981, AIDS was first reported in USA. From then to till now the AIDS epidemic remains a mysterious phenomenon and intensive research is in progress throughout the world in an effort to understand it and develop therapies and vaccines for this terrifying disease. The causative agent of AIDS disease is the retrovirus HIV (Human Imunodeficiency Virus). primarily through sexual contacts, sharing of contaminated needles, transfu infected blood / blood products, infected mother's breast milk to infants or Epidemiological studies (Brook Meyer Ron and Gail Mitchell, 1994) have pointed out that the route of HIV transmission is blood and that the transmission occurs sion of vertical transmission from mother to fetus in uterus or at delivery. Many authors studied the seroconversion time of HIV transmission by taking sexual contact alone is the only mode of HIV transmission. The sexual contacts are assumed between a seropositive person who is labeled as index case and seronegative counterpart is called as partner. A seroconversion from seronegative to seropositive state takes place after an incubation period due to the contraction of HIV to the partner from the index case by sexual contacts. Several authors have studied the stochastic models for the projection of number of cases of incidence in futute, rate of spread, etc. Shiboski and Jewel1 (1990) have obtained the expression for hazard rate and prevalence function for the transmission of HIV. They * Tuberculosis Research Center, Indian Council of Medical Research, Chetput, Chennai - 600 032, India ** Department of Statistics, Annamalai University, Annamalai Nagar- 608 002, India Bio-Science Research Bulletin. vol. 19 (No.1) 2003; P 23-39 A. Santhakumaran, C. Nanthakumar, P. Venkatesan and R. Kannan also have developed parametric and non-parametric methods of estimation of infectivity on the basis of available data from the partner studies. The concept of antigenic diversity threshold is found in Nowak and May (1991). Stilianakis el. al. (1994) discussed the transmission of HIV in successive contacts and its effects on antigenic diversity. Sathiyamoorthy and Kannan (1998) also obtained the expression for the expected time to seroconversion and its variance assuming the inter contact times to be correlated random variables. Sathiyamoorthy and Kannan (2000) derived a stochastic model based on the cumulative damage process with the assumption that the antigenic diversity threshold is a random variable and the damage process acting on the immune system is assumed to be linear. But as the immune capacities of an individual vary and also have its own resistance, the assumption of the antigenic diversity threshold acting on the immune system to be “linear” is not appropriate. In this paper, we propose a stochastic model of Seroconversion time of HIV transmission at time t with contact n (n = 0,1,2 ...) in which the antigenic diversity threshold level is Erlang k=2 distribution with the damage process, acting on the immune system of an infected individual is non linear and cumulative (Esary et al., 1973). There are certain number of HIV is getting transmitted during every contact and they in turn contribute to the antigenic diversity during the regenerative process. When the total antigenic diversity crosses a threshold level, the seroconversion takes place due to the depleting T4 cells. The damage caused to the immune system of an individual can be interpreted as the cumulative antigenic diversity in successive contacts. Assumptions: i) Sexual contact is the only source of HIV transmission. ii) An uninfected individual has sexual contacts with a HIV infected partner. iii) Damages to individuals are caused by transmission of HIV at each contact and the inter arrival time between the contacts are independent identically distributed random variables. iv) The damage process acting non-li vi) The process that generates the contacts, the sequence of damages and near and cumulative. on the immune system of an infected individual threshold are mutually independent. Notations: (.) -probability distribution function of Xi (.) -the probability density function of Xi Xi -the increase in the antigenic diversity arising due to the HIV transmitted G g during ith contact. k convolution of g(.) Bio-Science Research Bulletin. vol. I9 (No. I) 2003 24 The total damage caused exceeds a threshold level Y is itself a random variable V) gk(.) - the probability function of the random variable S .Xi which is the k i=1 A Stochastic Model for Seroconversion times of HIV transmission Y F (.) -the probability density function of threshold. T -random variable denoting the time to seroconversion. a -contact rate of the infected partner. a - intensity of the HIV of the infected partner Vk(t) -the probability that there are exactly k contacts in (0,t] with intensity - random variable denoting the antigenic diversity threshold has an Erlang k=2 distribution with parameter µ. represented as a Alpha Poisson process with parameters ‘a’ and ' a ' is The inter arrival time between the contacts follow Mittag-Leffler (1990,2001) distribution which is given by Now the prevalence function = L (t) = 1 - S (t). S (t) = P {T > t} =Probability that the seroconversion does not take before t [No seroconversion before t / exactly k contact in (o, t] with intensity a ] x P [exactly k contacts in (o, t] with intensity a ] where Vk (t) = probability of exactly k contacts in (o, t]. Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 25 A. Santhakumaran, C. Nanthakumar, P. Venkatesan and R. Kannan If g (.) has Mittag - Leffler distribution with parameter a and a, then The probability density function of seroconversion time T is The expected time of seroconversion is given by Bio-Science Research Bulletin. Vol. I 9  (No. 1 )  2003 26 A Stochastic Model for Seroconversion times of HIV transmission On simplification, we get the variance, It is interesting to note that when a = 1 these results act as a generalised result of the model where the contacts represented as Poisson process with assumption that the damage process acting on the immune system of an infected individual is linear. Particular case: Case (i): When k = 1 the mean and variance of the time to seroconversion of HIV transmission becomes This is the Mean and Variance of the seroconversion time of HIV transmission for the contacts as Alpha Poisson Process and the antigenic diversity threshold is exponential distribution Case (ii) transmiss E V : When a = 1, the mean and variance of the time ion becomes (T)= ( µ+2a)/aµ to seroconversion of HIV This is the Mean and Variance of the seroconversion time of HIV transmission for the contact as Poisson Processes and the antigenic diversity threshold is Erlang k=2 distribution. Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 27 A. Santhakumaran, C. Nanthakumar, P. Venkatesan and R. Kannan Case (iii): When a = 1 and k = 1, the Mean and Variance of the seroconversion time of HIV transmission becomes, E(T) = ( µ + a )/aµ which coincides with (Sathiyamoorthi and Kannan, 2001 ; p.26) For the model with threshold level distributed as Erlang k=2 with parameter µ, a numerical example is given to illustrate the mean and variance of seroconversion time for different a and the same are given in the Tables 1.1 to1.6 and Tables 2.1 to 2.6 respectively. Table 1.1 : Mean of Seroconversion Time with a = 0.1 Table 1.2 : Mean of Seroconversion Time with a = 0.2 Bio-Science Research Bulletin. Vol. I9 (No. I) 2003 28 A Stochastic Model for Seroconversion times of HIV transmission Table 1.3 : Mean of Seroconversion Time with a = 0.3 Table 1.4 : Mean of Seroconversion Time with a = 0.4 Bio-Science Research Bulletin. Vol. I9 (No. 1) 2003 29 Table 1.5 : Mean of Seroconversion Time with a = 0.5 A. Santhakumaran, C. Nanthakumar, P. Venkatesan and R. Kannan Table 1.6 : Mean of Seroconversion Time with a = 1.0 Table 2.1 : Variance of Seroconversion time with a = 0.1 Table 2.2 : Variance of Seroconversion time with a = 0.2 Bio-Science Research Bulletin Vol. 19 (No. 1) 2003 30 A Stochastic Model for Seroconversion times of HIV transmission Table 2.3 : Variance of Seroconversion time with a = 0.3 Table 2.4 : Variance of Seroconversion time with a = 0.4 Table 2.5 : Variance of Seroconversion time with a = 0.5 Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 31 Fig. 1.1 : Mean of Seroconversion Time with a = 0.1 A. Santhakumaran, C. Nanthakumar, P. Venkatesan and R. Kannan Table 2.6 : Variance of Seroconversion time with a = 1.0 Bio-Science Research Bulletin. Vol.19 (No. 1) 2003 32 A Stochastic Model for Seroconversion times of HIV transmission Fig. 1.2 : Mean of Seroconversion Time with a = 0 2 Fig. 1.3 : Mean of Seroconversion Time with a = 0.3 Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 33 A. Santhakumaran C. Nanthakumar, P. Venkatesan and R. Kannan Fig. 1.4 : Mean of Seroconversion Time with a = 0.4 Fig. 1.5 : Mean of Seroconversion Time with a = 0 5 Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 34 A Stochastic Model for Seroconversion times of HIV transmission Fig. 1.6 : Mean of Seroconversion Time with a = 1.0 Fig. 2.1 : Variance of Seroconversion Time with a = 0.1 Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 35 A. Santhakumaran, C. Nanthakumar, P. Venkatesan and R. Kannan Fig. 2.2 : Variance of Seroconversion Time with a = 0.2 Fig. 2.3 : Variance of Seroconversion Time with a = 0.3 Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 36 A Stochastic Model for Seroconversion times of HIV transmission Fig. 2.4 : Variance of Seroconversion Time with a = 0.4 Fig. 2.5 : Variance of Seroconversion Time with a = 0.5 Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 37 A. Santhakumaran, C. Nanthakumar, P. Venkatesan and R. Kannan ~ Fig. 2.6 : Variance of Seroconversion Time with a = 1.0 From the Figures 1.1 to 1.6 and Figures 2.1 to 2.6, we observed that for fixed ‘ µ ’ when ‘a’ (contact rate) increases, the mean of seroconversion time decreases. Also if ‘a’ is fixed and ‘µ’ (Antigenic diversity threshold) is allowed to increase then the mean time to seroconversion decreases. The same tendency is also noticed on the variances of the seroconversion time of HIV transmission. Also the intensity of the HIV of the infected partner increases, the Mean and Variance of seroconversion time decreases. The practical implication of the result is that the spread of HIV is faster as the intensity of the immune system is lower. REFERENCES 1. Anil, V. (2001). A Generalized Poisson distribution and its application. Journal of the Kerala Statistical Association, Vol 12; p. 11-22. 2. Brook Meyer Ron. .and Gail Mitchell, H. (1994). AIDS Epidemiology: A Quantitative Approach. Oxford University Press. 3. Esary, J.D.; Marshall, A.N. and Proschal, F. (1973). Shock models and wear processes, Ann. Probability; p. 627 - 649. 4. Jewell, N.P. and Shiboski, S.(1990). Statistical analysis of HIV infectivity based on partner studies, Biometrics, 46; p. 1133-1150. 5. Pillai, R.N. (1990). On Mittag-Leffler function and related distributions, Annals of the Institute of statistical Mathematics, 42; p. 157-161. Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 38 A Stochastic Model for Seroconversion times of HIV transmission 6. Stiliatiakis, N.: Schenzle, D. and Dietz, K. (1994). On the antigenic diversity threshold model for AIDS, MathematicalBiosciences, 121; p. 235-247. 7. Sathiyamoorthi, R. and Kannan, R. (1998). On the time to seroconversion of HIV patients under correlated inter contact times, Pure and Applied Mathematika Sciences. Vol.XLVIII, No.1-2; p. 75-87. Sathiyamoorthi, R. and Kannan, R. (2001). A Stochastic model for time to seroconversion of HIV transmission, Journal of the Kerala Statistical Association, Vo1.12; p. 23-28. 8. Bio-Science Research Bulletin. Vol. 19 (No. 1) 2003 39 

A stochastic model for sero conversion times of HIV transmission

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A stochastic model for sero conversion times of HIV transmission

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