The preceding papers have been sufficiently diverse to expose (at least) two apparent dichotomies in quantitative epidemiology. These concern the kinds of models needed to solve epidemiological problems: simple versus complex models, and dynamics versus statistical models. A minimum of three papers is needed to highlight the issues: this session has done it with just four.

Those who prefer to begin with complex (Habbema) or simple (Agur, Medley) dynamic models take different views of how many parameters and variables are needed to make these models useful. Crudely, the former hold that model utility is a monotonically increasing function of the number of parameters and variables, whilst the later believe that the slope of this graph will usually be negative. The two schools have different approaches because they have different aims. The view of the complex modellers is epitomized by the statement (Habbema et al. 1992):

The effort to deal with ‘many aspects’ is characteristic, but so too is the attempt to construct a model which is sufficiently detailed to predict the absolute (Habbema et al 1992):

It is clear, however, that the more robust policy statements will emerge when detailed models of this kind are used in a comparative way – to choose the best among available options.