By Lachlan B. Wilson
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Wilson like it is something that can be measured from models and actually means something in the real world. Further, if modeling could show how R0 could be reduced below unity, one would have a theoretical intervention strategy to eradicate the epidemic in question. Consequently, most dynamic transmission models are analyzed in an attempt to obtain a closed-form mathematical expression for R0. The dominant tradition in infection transmission modeling pursues simple models designed to make clear the nature of nonlinear dynamics.
Because of the large investment in development and implementation of the complex models, the practitioners are less likely to adapt or start afresh to meet new research problems but desire to use their existing model and find a problem to which it could be applied. Although there are many considerations that must be evaluated when one is developing a model, one must choose the approach that is most appropriate for the situation at hand. There is not a single approach that is best in all circumstances; each approach has value for different purposes.
But once one is satisfied that the model does not behave strangely and that the model adequately describes the epidemic in question, with appropriate parameter estimates, that is all the mathematical analysis that is required. The mathematical analysis is not necessarily a result; not a result of any utility to influence public policy, but only a preliminary step in the model development process prior to its use in investigation. Occasionally, there are a few instances where simple models that have exact mathematical solutions can provide powerful insight into the relation between parameter values and results and such relationships can be the most precise and elegant conceptual understanding; this is exemplified by some models of the impact of vaccines [134-136].