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Conceived and designed the experiments: LAM. Analyzed the data: SB. Wrote the paper: SB. Performed modeling and simulation: SB. Edited the manuscript: BP NH BTG LAM.

The authors have declared that no competing interests exist.

As Pandemic (H1N1) 2009 influenza spreads around the globe, it strikes school-age children more often than adults. Although there is some evidence of pre-existing immunity among older adults, this alone may not explain the significant gap in age-specific infection rates.

Based on a retrospective analysis of pandemic strains of influenza from the last century, we show that school-age children typically experience the highest attack rates in primarily naive populations, with the burden shifting to adults during the subsequent season. Using a parsimonious network-based mathematical model which incorporates the changing distribution of contacts in the susceptible population, we demonstrate that new pandemic strains of influenza are expected to shift the epidemiological landscape in exactly this way.

Our analysis provides a simple demographic explanation for the age bias observed for H1N1/09 attack rates, and suggests that this bias may shift in coming months. These results have significant implications for the allocation of public health resources for H1N1/09 and future influenza pandemics.

In March 2009, a new A/H1N1 influenza strain (Pandemic (H1N1) 2009 Influenza or H1N1/09) emerged in humans in Mexico and by early June 2009, the World Health Organization (WHO) had raised the worldwide pandemic alert level to signal a global pandemic of a novel influenza virus. Since the WHO declaration of a pandemic, the new H1N1/09 virus spread across the globe, causing epidemics in most countries

Influenza is a complex and continually changing disease that infects individuals of all ages. In contrast to diseases like measles and rubella, the dynamics of influenza are strongly influenced by the evolution of immunological properties of the pathogen

Influenza spreads during close contacts between susceptible and infected individuals. The likelihood of a person becoming exposed to disease will strongly depend on the number and intensity of his or her interactions

Our network model represents an urban area population and is based on data for the city of Vancouver, British Columbia

We compare six estimates for the mean degree by age of individuals (left panel) and the mean degree across the population (right panel). Meyers et al.

We assume that an infected node will infect a susceptible contact with a given probability (known as

Lines in these network diagrams indicate contacts through which influenza can spread. Prior to the introduction of a novel pandemic strain, most of the population is susceptible. The pandemic initially sweeps through the most connected portions of the populations, including groups of school-age children, leaving a wake of temporarily immunized individuals. The remaining susceptible population will consist of less connected portions of the population.

Most empirical studies measuring immunity to influenza measure a reduction in infection rate at the population scale. Thus, we model the spread of influenza in a partially immune population assuming perfect partial immunity, using empirical data for infection-acquired immunity to influenza described above. Perfect partial immunity implies that for a level of loss of partial immunity

To consider disease dynamics beyond the initial pandemic period, we have developed a mathematical approach based on percolation methods. The standard bond percolation model

An individual will be susceptible to infection in the second season if they were not infected in the first season or if they were infected and have lost immunity (with probability

Multiple bars for a single strain represent data from different populations. Data are from a:

In contrast to studies assuming instantaneous pre-exposure vaccination of target groups

Pandemic influenza is feared for its severe excess mortality

Data from H1N1/09 outbreaks thus far reveals a similar initial discrepancy in attack rates (

Several diverse studies have estimated the distribution of contact patterns among age groups, primarily in urban populations

(A) During the initial pandemic season, we notice a shift in the attack rate (the number of new cases during a week in an age group divided by the size of the age group). The attack rate among children is initially higher than the attack rate among adults, but this reverses after the epidemic peak. (B) During the initial pandemic, all individuals are susceptible, and

Using our mathematical model, we calculate the expected age-specific attack rates in the first and second seasons given the contact structure of the network (its degree distribution), the infectiousness of the strain, and the level of partial immunity from one season to the next. We find that the attack rate shifts shown in

If the reproductive number in the first season is

The vaccination of school-age children has been suggested as an effective influenza control strategy

(A) The impact of school-aged and adult vaccination priorities at 15% vaccine coverage in a naive (“Season 1”) and partially immune population (“Season 2”) population at

Vaccination reduces the size of the epidemic through both direct protection of 15% of the population and indirect protection of others through partial herd immunity.

Influenza transmission is constrained by contact patterns, which are influenced by individual behavior and sociological events. For example, the early transmission of H1N1/09 in Mexico City was likely hampered by the closing of schools for the two-week Holy Week period and the subsequent implementation of social distancing interventions including school closures

The reverse is also true: the dynamics of infectious diseases can dramatically alter the structure of a host population. Outbreaks of fully immunizing diseases like measles permanently remove cases from the susceptible fraction of the population. Influenza, along with many other partially immunizing diseases such as RSV, pertussis and rotavirus, provides temporary incomplete immunity. Individuals fade in and out of the epidemiological active portion of the population as they become infected and slowly regain susceptibility to future infection. When a novel influenza strain emerges into a pandemic, it works its way through the population, preferentially infecting and thus immunizing individuals with high numbers of contacts. It essentially prunes the underlying contact network by removing highly connected individuals and all of their connections. If the strain reemerges in the following season, it faces much sparser chains of susceptible individuals, in which spread is more limited and new groups are expected to bear the brunt of the epidemic. Our simple network-based mathematical model elucidates this phenomenon by both incorporating the heterogeneous distribution of contacts among age groups and tracking the changing immunological structure of the population from one season to the next

This model does not consider variability in contact patterns due to seasonality, nor do we account for demographic processes such as births, deaths, and aging. We have found, however, that population aging has minimal impact on network structure or disease dynamics across levels of immunity (

When schools are in session, school children tend to have the highest numbers of contacts among all age groups

This analysis suggests that we might experience a shift in H1N1/09 age-specific infection risks (and thus potential for infecting others) over the next 12 to 24 months, and that the optimal distribution of vaccines and other public health resources may change throughout this period. Early data from the Fall wave of the H1N1/09 outbreak in the United States already shows a trend towards a decrease in cases in school-age children

Although our study does not explicitly consider the important option of prioritizing groups at high risk for mortality, we echo our previous claim (

Supporting methods and analysis.

(0.42 MB PDF)

The authors thank Joel Mossong, Jonathan Read, and Phillipe Beutels for sharing contact data; and the U.S. Centers for Disease Control and Prevention (CDC) Epidemiology/Surveillance Team for sharing aggregate case count data on the H1N1 outbreak in the United States. (The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC.)