Most deterministic epidemiological mathematical models are variations
on a template model. Developing a model for a specific situation involves
parameterizing the peculiarities of the given disease and population into
the model by the appropriate adjustment of population groups, dynamics,
and constants. Being able to actually implement these adjustments requires
a strong familiarity with the disease (and the population if it is
abnormal), especially in finding appropriate values for constants such as
the infection rate. Often, a more precise representation of the disease
will result in a more mathematically complex model.
Different types of models can have different uses and limitations.
Stochastic models, for instance, are well-suited to small populations,
but their application to larger populations is widely debated. In order
to develop a useful model, one must be aware of such limitations, and
choose the appropriate type of model for the situation.
An accurate model of a disease’s behavior can predict the most effective
response to that disease. Epidemiologists can use mathematical models to
determine the relative effectiveness (if any) of different approaches to a
disease’s containment as well as the targeting of different subsets of the
susceptible community for particular preventative measures. Models can
also be used to simply model the severity of an epidemic’s impact over
time without intervention.