Influenza: H1N1 goes to school

The effect of age on transmission of 2009 pandemic influenza A (H1N1) in a camp and associated households

BACKGROUND

A major portion of influenza disease burden during the 2009 pandemic was observed among young people.

METHODS

We examined the effect of age on the transmission of influenza-like illness associated with the 2009 pandemic influenza A (H1N1) virus (pH1N1) for an April-May 2009 outbreak among youth-camp participants and household contacts in Washington State.

RESULTS

An influenza-like illness attack rate of 51% was found among 96 camp participants. We observed a cabin secondary attack rate of 42% (95% confidence interval = 21%-66%) and a camp local reproductive number of 2.7 (1.7-4.1) for influenza-like illness among children (less than 18 years old). Among the 136 contacts in the 41 households with an influenza-like illness index case who attended the camp, the influenza-like illness secondary attack rate was 11% for children (5%-21%) and 4% for adults (2%-8%). The odds ratio for influenza-like illness among children versus adults was 3.1 (1.3-7.3).

CONCLUSIONS

The strong age effect, combined with the low number of susceptible children per household (1.2), plausibly explains the lower-than-expected household secondary attack rate for influenza-like illness, illustrating the importance of other venues where children congregate for sustaining community transmission. Quantifying the effects of age on pH1N1 transmission is important for informing effective intervention strategies.

A Bayesian framework for parameter estimation in dynamical models

Mathematical models in biology are powerful tools for the study and exploration of complex dynamics. Nevertheless, bringing theoretical results to an agreement with experimental observations involves acknowledging a great deal of uncertainty intrinsic to our theoretical representation of a real system. Proper handling of such uncertainties is key to the successful usage of models to predict experimental or field observations. This problem has been addressed over the years by many tools for model calibration and parameter estimation. In this article we present a general framework for uncertainty analysis and parameter estimation that is designed to handle uncertainties associated with the modeling of dynamic biological systems while remaining agnostic as to the type of model used. We apply the framework to fit an SIR-like influenza transmission model to 7 years of incidence data in three European countries: Belgium, the Netherlands and Portugal.

Absolute humidity and pandemic versus epidemic influenza

Experimental and epidemiologic evidence indicates that variations of absolute humidity account for the onset and seasonal cycle of epidemic influenza in temperate regions. A role for absolute humidity in the transmission of pandemic influenza, such as 2009 A/H1N1, has yet to be demonstrated and, indeed, outbreaks of pandemic influenza during more humid spring, summer, and autumn months might appear to constitute evidence against an effect of humidity. However, here the authors show that variations of the basic and effective reproductive numbers for influenza, caused by seasonal changes in absolute humidity, are consistent with the general timing of pandemic influenza outbreaks observed for 2009 A/H1N1 in temperate regions, as well as wintertime transmission of epidemic influenza. Indeed, absolute humidity conditions correctly identify the region of the United States vulnerable to a third, wintertime wave of pandemic influenza. These findings suggest that the timing of pandemic influenza outbreaks is controlled by a combination of absolute humidity conditions, levels of susceptibility, and changes in population-mixing and contact rates.

Age, influenza pandemics and disease dynamics

The world is currently confronting the first influenza pandemic of the 21st century [caused by a novel pandemic influenza A (H1N1) virus]. Earlier pandemics have been characterized by age distributions that are distinct from those observed with seasonal influenza epidemics, with higher attack rates (and correspondingly increased proportionate or relative mortality) in younger individuals. While the genesis of protection against infection in older individuals during a pandemic is uncertain, differential vulnerability to infection by age has important implications for disease dynamics and control, and for choice of optimal vaccination strategies. Age-related vulnerability to infection may explain differences between school- and community-derived estimates of the reproductive number (R) for a newly emerged pandemic strain, and may also help explain the failure of a newly emerged influenza A (H1N1) virus strain to cause a pandemic in 1977. Age-related factors may also help explain variability in attack rates, and the size and impact of influenza epidemics across jurisdictions and between populations. In Canada, such effects have been observed in the apparently increased severity of outbreaks on Indigenous peoples' reserves. The implications of these patterns for vaccine allocation necessitate targeted research to understand age-related vulnerabilities early in an influenza pandemic.

Estimated epidemiologic parameters and morbidity associated with pandemic H1N1 influenza

BACKGROUND

In the face of an influenza pandemic, accurate estimates of epidemiologic parameters are required to help guide decision-making. We sought to estimate epidemiologic parameters for pandemic H1N1 influenza using data from initial reports of laboratory-confirmed cases.

METHODS

We obtained data on laboratory-confirmed cases of pandemic H1N1 influenza reported in the province of Ontario, Canada, with dates of symptom onset between Apr. 13 and June 20, 2009. Incubation periods and duration of symptoms were estimated and fit to parametric distributions. We used competing-risk models to estimate risk of hospital admission and case-fatality rates. We used a Markov Chain Monte Carlo model to simulate disease transmission.

RESULTS

The median incubation period was 4 days and the duration of symptoms was 7 days. Recovery was faster among patients less than 18 years old than among older patients (hazard ratio 1.23, 95% confidence interval 1.06-1.44). The risk of hospital admission was 4.5% (95% CI 3.8%-5.2%) and the case-fatality rate was 0.3% (95% CI 0.1%-0.5%). The risk of hospital admission was highest among patients less than 1 year old and those 65 years or older. Adults more than 50 years old comprised 7% of cases but accounted for 7 of 10 initial deaths (odds ratio 28.6, 95% confidence interval 7.3-111.2). From the simulation models, we estimated the following values (and 95% credible intervals): a mean basic reproductive number (R0, the number of new cases created by a single primary case in a susceptible population) of 1.31 (1.25-1.38), a mean latent period of 2.62 (2.28-3.12) days and a mean duration of infectiousness of 3.38 (2.06-4.69) days. From these values we estimated a serial interval (the average time from onset of infectiousness in a case to the onset of infectiousness in a person infected by that case) of 4-5 days.

INTERPRETATION

The low estimates for R0 indicate that effective mitigation strategies may reduce the final epidemic impact of pandemic H1N1 influenza.