The shifting demographic landscape of influenza

Determining the dynamics of influenza transmission by age

Background

It is widely accepted that influenza transmission dynamics vary by age; however methods to quantify the reproductive number by age group are limited. We introduce a simple method to estimate the reproductive number by modifying the method originally proposed by Wallinga and Teunis and using existing information on contact patterns between age groups. We additionally perform a sensitivity analysis to determine the potential impact of differential healthcare seeking patterns by age. We illustrate this method using data from the 2009 H1N1 Influenza pandemic in Gauteng Province, South Africa.

Results

Our results are consistent with others in showing decreased transmission with age. We show that results can change markedly when we make the account for differential healthcare seeking behaviors by age.

Conclusions

We show substantial heterogeneity in transmission by age group during the Influenza A H1N1 pandemic in South Africa. This information can greatly assist in targeting interventions and implementing social distancing measures.

Low transmission rate of 2009 H1N1 Influenza during a long-distance bus trip

Background

Current data on the risk of transmission of 2009 H1N1 Influenza in public transportation systems (e.g., public trains, busses, airplanes) are conflicting. The main transmission route of this virus is thought to be via droplets, but airborne transmission has not been completely ruled out.

Methods

This is a contact tracing investigation of a young woman subsequently diagnosed with the 2009 H1N1 Influenza virus who was symptomatic during a long-distance bus trip from Spain to Switzerland. Fever and cough had begun 24 h earlier, 2 h before she stepped onto a bus for a long-distance trip. After the 2009 H1N1 virus had been confirmed in the patient, the other bus travellers were contacted by telephone on day 7 and 10 after the bus trip.

Results

Of the 72 individuals travelling on the bus with the H1N1-infected young woman, 52 (72%) could be contacted. Only one of these 52 developed fever, with onset of symptoms 3 days after the bus trip, and rRT-PCR analysis of the nasopharyngeal swab showed the infection to be caused by the 2009 H1N1 virus. One other person complained of coughing 1 day after the bus trip, but without fever, and no further investigation was carried out. All other passengers remained without fever, coughing, or arthralgia. The risk of transmission was calculated as 1.96% (95% confidence interval 0–5.76%).

Conclusion

The transmission rate of 2009 H1N1 Influenza was low on a long-distance bus trip.

Electronic supplementary material

The online version of this article (doi:10.1007/s15010-011-0084-x) contains supplementary material, which is available to authorized users.

What happened after the initial global spread of pandemic human influenza virus A (H1N1)? A population genetics approach

Viral population evolution dynamics of influenza A is crucial for surveillance and control. In this paper we analyzed viral genetic features during the recent pandemic caused by the new influenza human virus A H1N1, using a conventional population genetics approach based on 4689 hemagglutinin (HA) and neuraminidase (NA) sequences available in GenBank submitted between March and December of 2009. This analysis showed several relevant aspects: a) a scarce initial genetic variability within the viral isolates from some countries that increased along 2009 when influenza was dispersed around the world; b) a worldwide virus polarized behavior identified when comparing paired countries, low differentiation and high gene flow were found in some pairs and high differentiation and moderate or scarce gene flow in others, independently of their geographical closeness, c) lack of positive selection in HA and NA due to increase of the population size of virus variants, d) HA and NA variants spread in a few months all over the world being identified in the same countries in different months along 2009, and e) containment of viral variants in Mexico at the beginning of the outbreak, probably due to the control measures applied by the government.

Optimizing allocation for a delayed influenza vaccination campaign

During unexpected infectious disease outbreaks, public health agencies must make effective use of limited resources. Vaccine distribution may be delayed and staggered through time, as underscored by the 2009 H1N1 influenza pandemic. Using a mathematical model parametrized with data from the 2009 H1N1 pandemic, we found that optimal allocations of vaccine among people in different age groups and people with high-risk conditions depends on the schedule of vaccine availability relative to the progress of the epidemic. For the projected schedule of H1N1 vaccine availability, the optimal strategy to reduce influenza-related deaths is to initial target high-risk people, followed by school-aged children (5-17) and then young adults (18-44). The optimal strategy to minimize hospitalizations, however, is to target ages 5-44 throughout the vaccination campaign, with only a tiny amount of vaccine used on high-risk people. We find that optimizing at each vaccine release time independently does not give the overall optimal strategy. In this manuscript, we derive policy recommendations for 2009 H1N1 vaccine allocation using a mathematical model. In addition, our optimization procedures, which consider staggered releases over the entire epidemic altogether, are applicable to other outbreaks where not all supplies are available initially.