This work focuses on the analysis of HIV infection dynamics during the initial stages of infection when the viral load is low and random fluctuations may have a significant effect on the dynamics of the disease. Deterministic models that describe the expected progress of the infection cannot be employed to predict the probability of infection establishment at the primary stage. Consequently, stochastic simulations are used to determine the probability of successful infection in an average patient. A stochastic model based on Gillespie's algorithm is derived which includes mutant species and employed to determine the sensitivity of HIV infection to different treatment strategies such as single therapy and combination therapy with various efficacy values. The effect of treatment latency on virus clearance probability is also investigated.
All Science Journal Classification (ASJC) codes
- Chemical Engineering(all)
- Industrial and Manufacturing Engineering