In this paper, we identify a set of key features characterizing seasonal influenza-like illness (ILI) incidence time series and use these to define information measures for the distance between a transmission model and empirical observations. Use of summary statistics (rather than attempting to directly fit models to timeseries data) adds robustness to variations in reporting (much ILI is not even caused by influenza) and shortterm fluctuation in the incidence data. Our aim is not to precisely match the incidence time series, but to find broad regions of parameter space which are consistent with the characteristics of seasonal flu epidemics and hence identify which epidemiological mechanisms are essential and acceptable ranges for their parameters. For this purpose, an elaborate model and detailed data are not necessary.