  Knowledge of the severity of an influenza outbreak is crucial for informing
and monitoring appropriate public health responses, both during and after an
epidemic. However, case-fatality, case-intensive care admission and
case-hospitalisation risks are difficult to measure directly. Bayesian evidence
synthesis methods have previously been employed to combine fragmented,
under-ascertained and biased surveillance data coherently and consistently, to
estimate case-severity risks in the first two waves of the 2009 A/H1N1
influenza pandemic experienced in England. We present in detail the complex
probabilistic model underlying this evidence synthesis, and extend the analysis
to also estimate severity in the third wave of the pandemic strain during the
2010/2011 influenza season. We adapt the model to account for changes in the
surveillance data available over the three waves. We consider two approaches:
(a) a two-stage approach using posterior distributions from the model for the
first two waves to inform priors for the third wave model; and (b) a one-stage
approach modelling all three waves simultaneously. Both approaches result in
the same key conclusions: (1) that the age-distribution of the case-severity
risks is "u"-shaped, with children and older adults having the highest
severity; (2) that the age-distribution of the infection attack rate changes
over waves, school-age children being most affected in the first two waves and
the attack rate in adults over 25 increasing from the second to third waves;
and (3) that when averaged over all age groups, case-severity appears to
increase over the three waves. The extent to which the final conclusion is
driven by the change in age-distribution of those infected over time is subject
to discussion.
