XML for input into BEAST with increasing temporal sampling rangeXML were created in BEAUti v1.5.4 for analysis in BEAST v1.7.4, as described in Hedge et al. 2013 (Drummond & Rambaut, 2007, Drummond et al. 2012). Each file specifies the EpiFlu accession numbers of the segments concatenated into the whole-genome sequences and used as input data for the analysis (http://platform.gisaid.org/). Each file is labelled with the name of the last month during which sequences included in the analysis were sampled and the growth modelled employed. For example, June.exp.xml comprises EpiFlu accession numbers of sequences sampled from the start of the pandemic until June 2009 and specifies the use of an exponential growth model. Details of the substitution, clock and coalescent models are provided along with the priors placed on the model parameters. An additional block implementing the estimation of the marginal likelihood of the model using path-sampling is given after the Markov chain Monte Carlo (MCMC) block (Baele et al. 2012).BEAST_XML.zip

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.