Matching model complexity with data detail: influenza propagation modeling as a case study

In this paper, we propose methods to find models of optimal complexity for a given level of detail of the epidemic data. Following this aim, we perform a retrospective analysis influenza outbreaks by means of a family of SEIR-type compartmental models. Comparing the values of information indicators, we rate the models of various structural complexity and draw conclusions on quality of matching the models with data.