Abstract
Substantial progress in the understanding of HIV and CD4 cell dynamics using computational models undergirded by sound epidemiologic and mathematical principles has been achieved. The early stages of the applications of these models were based on relatively simple mathematical models that considered the body as a one-compartment system. In spite of these models attractiveness due to the experimental and/or mathematical standpoints, the underlying simplification neglected a lot of important factors affecting the population dynamics both on macro (human) and micro (cellular) population levels. This simplification also affected the kinetics linked to the immunology, infection and chemotherapy dynamics throughout the host. 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 given population whether biotic or abiotic. This leads in generating large data sets which require the use of powerful computational methods for studying these large and complex models by means of computational epidemiologic methods. Another dimension of a great challenging problem to public health decision makers is that of emerging diseases, as they have to face and deal with a lot of uncertainty at the early stages of disease outbreaks. However, at this juncture, epidemiologic problem-solving and decision-making often proceeds in the face of uncertainties and limited information. One methodology to address these types of shortcomings is the application of risk analysis. Risk analysis is a process for decision making under uncertainty that consists of three fundamental tasks: risk management, risk assessment, and risk communication. Excitingly, the prospective role that computational models and risk analysis may possibly play in the advancement of the theoretical understanding of disease processes and the identification of specific intervention strategies holds the potential to impact and save human lives.