Influenza-like illnesses (ILIs) are caused by several respiratory pathogens. These pathogens show weak to strong seasonal activity implying seasonality in ILI consultations. In this paper, the contribution of pathogens to seasonality of ILI consultations was statistically modelled. Virological count data were first smoothed using modulation models for seasonal time series. Second, Poisson regression was used regressing ILI consultation counts on the smoothed time series. Using ratios of the estimated regression parameters, relative measures of the underreporting of pathogens were obtained. Influenza viruses A and B, parainfluenza virus and respiratory syncytial virus (RSV) significantly contributed to explain the seasonal variation in ILI consultations. We also found that RSV was the least and influenza virus A is the most underreported pathogen in Belgian laboratory surveillance. The proposed methods and results are helpful in interpreting the data of clinical and laboratory surveillance, which are the essential parts of influenza surveillance.