Steady-State Models

Dynamic modeling is important to many areas of medical decision making. Cancer screening, infectious disease transmission, demographic modeling of healthcare and pension costs, economic growth, and budget forecasting provide a few arenas in which dynamic modeling plays a critical role. Differential or difference equations provide a framework for many of these problems.

In many cases, analysts can define differential equations that cannot be solved analytically at each moment in time. Fortunately, the long-term behavior of these systems can be explored or characterized through steady-state analysis.

This piece presents the basic character and limitations of steady-state analysis. It begins with some general background. It then discusses a specific steady-state analysis: the random-mixing model of infectious disease transmission in a population of injection drug users (IDUs).


Suppose one derives some ...

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