Assessing HIV/AIDS intervention strategies using an integrative macro-micro level computational epidemiologic modeling approach.

INTRODUCTION Epidemiologic research involves the study of a complex set of host, environmental and causative agent factors as they interact to impact health and diseases in any population. The most advanced of these efforts have focused on micro (cellular) or macro (human) population level studies but lacked the integrative framework as presented in this article. Modeling the cumulative impact of HIV/AIDS at the cellular, molecular, and individual behaviors at the population-level can be complex. The main objective of our research is to develop a macro-micro level computational epidemiologic model that integrates the dynamic interplay of HIV/AIDS at the cellular and molecular level (micro-epidemiologic modeling), and the dynamic interplay of multifactorial determinants: biomedical, behavioral, and socioeconomic factors at the human population level (macro-epidemiologic modeling). METHODS The computational epidemiologic model was constructed using systems dynamics modeling methodology. The dynamics of the relationships was described by means of ordinary/partial differential equations. All state equations in the model were approximated using the Runge-Kutta 4th order numerical approximation method. RESULTS Computational tools and mathematical approaches that integrate models from micro to macro levels in a seamless fashion have been developed to study the population-level effects of various intervention strategies on HIV/AIDS. The critical variables that facilitate transmission of HIV and intracellular interactions and molecular kinetics were examined to assess different interventions strategies. Such multilevel models are essential if we are to develop quantitative, predictive models of complex biological systems such as HIV/AIDS.