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Understanding the epidemiology of seasonal influenza is critical for healthcare resource allocation and early detection of anomalous seasons. It can be challenging to obtain high-quality data of influenza cases specifically, as clinical presentations with influenza-like symptoms may instead be cases of one of a number of alternate respiratory viruses. We use a new dataset of confirmed influenza virological data from 2011-2016, along with high-quality denominators informing a hierarchical observation process, to model seasonal influenza dynamics in New South Wales, Australia. We use approximate Bayesian computation to estimate parameters in a climate-driven stochastic epidemic model, including the basic reproduction number  R  0 , the proportion of the population susceptible to the circulating strain at the beginning of the season, and the probability an infected individual seeks treatment. We conclude that  R  0  and initial population susceptibility were strongly related, emphasising the challenges of identifying these parameters. Relatively high  R  0  values alongside low initial population susceptibility were among the results most consistent with these data. Our results reinforce the importance of distinguishing between  R  0  and the effective reproduction number ( R   e  ) in modelling studies.

Characterising seasonal influenza epidemiology using primary care surveillance data