BACKGROUND: The term “eco-epidemiology” has been used to describe an evolving paradigm shift within epidemiology. Driven by increased recognition that many determinants of disease cannot be adequately understood within the conceptual framework of “individualized risk”, eco-epidemiology assumes that risk factors operate not only on individuals within populations, but also on the interactions of individuals. This perspective provides a conceptual framework that can investigate the effects of risk factors operating on the level of interactions between individuals, and the hierarchical contextual environments in which individuals exist. This paradigm shift is accelerated and supported by key methodological developments, such as mathematical modeling, computer simulation, and hierarchical statistical analysis. METHODS: The importance of mathematical and computer simulation modeling within the eco-epidemiology framework is demonstrated with two HIV modeling examples: 1) A deterministic mathematical model consisting of a nonlinear system of differential equations is used to model optimal HIV vaccine distribution policies; 2) A stochastic, discrete-event simulation is used in Monte Carlo simulation experiments on vaccine efficacy estimation. RESULTS: The first model illustrates that the inherent interdependence of individuals caused by HIV transmission dynamics must be represented in order to accurately assess the potential impact of HIV vaccination on the population level. The second model demonstrates that standard relative risk estimation methods produce inaccurate vaccine efficacy estimates due to the violation of statistical independence assumptions (i.e., individualized risk assumptions). CONCLUSIONS: Mathematical and simulation modeling methods provide valuable tools as epidemiologists increasingly utilize the eco-epidemiological framework for investigations of infectious disease and social epidemiology.
Learning Objectives: At the conclusion of the session, the participant (learner) in this session will be able to: 1) Differentiate analytic methods that involve implicit assumptions from the “individualized risk” or the “eco-epidemiology” conceptual frameworks. 2) Understand why assumptions regarding the independence of observations (i.e., individuals in populations) are often unwarranted in infectious disease and social epidemiology investigations. 3) Appreciate how mathematical and computer simulation models can be used within the “eco-epidemiology” framework to represent and account for interdependent relationships between individuals and the effects of risk factors that act on the interactions of individuals.
Keywords: Simulation, Epidemiology
Presenting author's disclosure statement:
Organization/institution whose products or services will be discussed: None
Disclosure not received
Relationship: Not Received.