Data from: Real-time characterization of the molecular epidemiology of an influenza pandemic

Early characterization of the epidemiology and evolution of a pandemic is
essential for determining the most appropriate interventions. During the
2009 H1N1 influenza A pandemic, public databases facilitated widespread
sharing of genetic sequence data from the outset. We employ Bayesian
phylogenetics to simulate real-time estimation of the evolutionary rate,
date of emergence and intrinsic growth rate (r0) of the pandemic from
whole-genome sequences. We investigate the effects of temporal range of
sampling and dataset size on the precision and accuracy of parameter
estimation. Parameters can be accurately estimated as early as two months
after the first reported case, from 100 genomes. Early deleterious
mutations were purged from the population during the second pandemic wave
and the choice of growth model is important for accurate estimation of r0.
This demonstrates the utility of simple coalescent models to rapidly
inform intervention strategies during a pandemic.