A multivariate dynamic linear time series model was fitted to data on influenza-like illness (ILI) rates among networks of public and private general practitioners and school absenteeism rates, plus drop-in fever count data from designated flu clinics (DFC) that were created during the pandemic. The data streams were assumed to follow an underlying latent process with local linear trend. The estimated level and trend of the latent process reflect the magnitude and direction of influenza activity, which are then combined to infer an overall influenza activity index. Correlations between the estimated influenza level from the model and laboratory isolation rate were calculated to assess its performance before and during the 2009 pandemic.