  Mathematical models are increasing adopted for setting targets for disease
prevention and control. As model-informed policies are implemented, however,
the inaccuracies of some forecasts become apparent, for example overprediction
of infection burdens and overestimation of intervention impacts. Here, we
attribute these discrepancies to methodological limitations in capturing the
heterogeneities of real-world systems. The mechanisms underpinning single
factors for infection and their interactions determine individual propensities
to acquire disease. These are potentially so numerous that to attain a full
mechanistic description may be unfeasible. To contribute constructively to the
development of health policies, model developers either leave factors out
(reductionism) or adopt a broader but coarse description (holism). In our view,
predictive capacity requires holistic descriptions of heterogeneity which are
currently underutilised in infectious disease epidemiology but common in other
disciplines.
