Analysis of the 2009 pandemic to date in Britain, and elsewhere, indicates that there are some strong agedependent signatures. Most notably, school children have suffered the greatest per capita burden of infection as recorded by surveillance systems, whereas pre-school children have experienced the greatest per capita level of severe infection (as measured by hospital admissions), while the over 65 age group were most likely to suffer severe problems if they became infected [9, 16] . These different age-dependent effects are due to several interacting and conflicting factors: the highly structured mixing between age groups, the age-related I I I I I I I I I I I I I I I I   I I I I R I I I I I I I I I I I   I I R R R I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I R R I I I I I I I I I I   I I R R R I I I I I I I I I I I   I R R R I I I I I I I I I I I I   I R R I I I I I I I I I I I I I   districts I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I R I I I I I I I I I I I   I I R R R I I I I I I I I I I I   I R R R I I I I I I I I I I I I   I R R I I I I I I I I I I I I I   districts I I I I I I I I I I I I S S S S   I I I I I I I I I I I I S S S S   I R I I I I I I I I I I I S S S   I R I I I I I I I I I I I I S S   I R I I I I I I I I I I I I I S   I R I I I I I I I I I I I I I S   I R I I I I I I I I I I I I I I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I time to complete vaccination (days) I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I I   I I I I I I I I I I I I I I I susceptibility to infection and the age-dependent risk of severe symptoms following infection. To combine these factors require a mathematical model based on the available age-structured information. Here we use data from the POLYMOD study [24] to parametrize agerelated mixing patterns, where P a,b captures the estimated contact rate between individuals of ages a and b. In the electronic supplementary material we also show that an age-dependent susceptibility vector (q a ) can be estimated such that the early dynamics, as predicted by the dominant eigenvector of the transmission matrix, agree with the observed early age-structured distribution of infection. However, for greater generality, we set q ¼ 1 in figure 3 , although even using the estimated age-dependent susceptibility from the 2009 pandemic, together with the impact of school holidays, does not dramatically change the predictions (see electronic supplementary material). We use the age-dependent transmission matrix b to determine an optimal priority for a rapid age-dependent vaccination programme [23, [25] [26] [27] (figure 3). The methodology is as follows: for each single dose of vaccine, we consider which age class should be immunized such that the resultant growth-rate (as predicted by the dominant eigenvalue) is minimized; repeating this process successively generates a vaccination strategy that should rapidly control the epidemic for any given level of vaccine coverage. (We note that [27] provide an alternative, more analytical method of minimizing the eigenvalue, which is equivalent to our approach once the total level of vaccine exceeds a threshold.) The vaccination strategies given in figure 3 therefore inform about the instantaneous epidemiological significance of each age group at a particular point during an epidemic. We do not claim that these strategies are truly optimal (in terms of minimizing the predicted total number of cases across all possible distributions of vaccine), nor that such strategies are entirely relevant if vaccination is slow relative to the epidemic timescales (owing to the changes in the priorities we observe as the epidemic progresses, as shown in the sub-graphs). However, these age-specific vaccination profiles do provide an intuitive means of sequentially and efficiently increasing the vaccination coverage at any given point in the epidemic and have been found to agree with the optimal distribution of a fixed quantity of vaccine that minimizes the dominant eigenvalue [27] . What is crucial to note in these plots is that they represent a theoretical ideal when vaccine supply is limited rather than an achievable goal. If vaccine is not in short supply then it is clearly always better (both in terms of reducing growth rate and total epidemic size) to vaccinate someone than not, even if this leads to substantial deviation away from the optimal age profile. Therefore, these plots inform about possible prioritization of the vaccine campaign.