The three dynamic simulation modeling methods can be used for evaluating system interventions for the delivery of health care. They include system dynamics, discrete event simulation, and agent-based modeling. Compared to conventional simulations, the approach through dynamic systems involves a degree of complexity of a system and expects both upstream and downstream implications of changes that occur within complex systems of health care. System dynamics (SD) refers to the method of modeling employed for representing the structure of complex systems and understanding the way they behave over a certain period. The key elements of SA are feedback, accumulations, rates, and time delays. Non-linearity is a crucial SD concept because of the existence of feedback processes. When applied to healthcare, SD models usually characterize the population and offer a cross-sectional view of a system by counting the number of times a particular group of people exhibited particular characteristics.
Discrete event simulation (DES) is used to analyze queuing processes and queue networks, the focus of which is the use of resources. Specifically, in health care, DES is used for analyzing the impact on health outcomes. It is especially beneficial for being able to capture the changing characteristics of patients in regards to a specific donation. Agent-based modeling (ABM) is a simulation method for modeling dynamic and autonomous systems; it uses a great degree of deductive and inductive reasoning. Agents are usually seen as social interaction with others and the environment. ABM’s use is attributed to large-scale natural or anthropogenic disasters. The response to the implications of such disasters is seen to be influenced by the existing information about it, various behaviors, and containment strategies.