Modelling viral and CD4 cellular population dynamics in HIV: approaches to evaluate intervention strategies.

Computational models, such as in epidemiology, provide a powerful tool that can be used to systematically examine an array of dynamic interactions among populations as well as to evaluate altemate disease intervention strategies. The specific objectives in this study were to: a/ examine the interaction of cellular (CD4) and HIV population dynamics and evaluate the impact of the use of combination chemotherapies on viral and CD4 populations (Experiment #1), b/ demonstrate how modelling can be used to evaluate the impact of an intervention (condom use) on reducing the rate of HIV/AIDS (Experiment #2). In this study, we used state transition models and conducted simulation experiments to evaluate various alternatives for the control and/or prevention of HIV/AIDS. The result indicated that combination therapy (double or triple drug therapies) was very effective. The HIV viral population decreased rapidly and remained suppressed for years. On the other hand, the CD4 cell population increased above 400 cells per ml and was maintained above that level for many years. Mono-therapy was not as effective; although the viral load decreased rapidly, it increased to its original levels within a few months. Since condom use is one of the key interventions of HIV/AIDS, we evaluated its use in 25%, 50% and 75% of an adult, sexually active population. Increasing condom use by 50% and 75% above an estimated baseline of 25% reduced the incidence of AIDS by 53% in Blacks, 49% in Hispanics and 43% in Whites. The study shows how a cellular/molecular level model can be incorporated within a macro-epidemiologic systems dynamics model to evaluate a variety of scientific questions such as to see if cellular/molecular level interventions reduce morbidity and mortality rates in HIV.