To demonstrate the capabilities of RT-PCR/ESI-MS to track the evolution of circulating influenza viruses, we used a bioinformatic approach to develop a framework on which to display the RT-PCR/ ESI-MS results obtained with H3N2 viruses. Complete genome sequences of all H3N2 human influenza viruses available in GenBank were analyzed. A total of 731 genomes were included, from which we inferred the phylogeny of H3N2 influenza virus since 1996. Using the 565-nucleotide concatenated sequence of the six loci that we analyzed by RT-PCR/ESI-MS, we constructed a nonredundant alignment of 105 sequence types. Base compositions were determined for each genome segment (i.e., locus) analyzed and each unique base composition at each of these loci was assigned a letter according to decreasing number of occurrences (therefore, the letter A represents the most common allele identified at each locus). The concatenation of the six base-composition letters from the PB1, NP, M1, PA, NS1, and NS2 loci from the sequences of 731 H3N2 viruses (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) available in GenBank yielded 95 six-letter codes referred to as base composition types (BC types). The predominant type is labeled AAAAAA. The topology of the tree was then deduced from the alignment of non-redundant sequences using the programs dnadist and neighbor from the Phylip package (http://evolution. genetics.washington.edu/phylip.html). Since the sequence types differ mostly by discrete single mutations, the original computergenerated tree was then extensively edited using graphics tools to place the labels of intermediate types within the tree itself in lieu of zero-length branches. Readability was further improved by sorting parallel branches chronologically. Table S1 Influenza virus primer pairs used in this study. Genbank reference sequence for each segment is indicated; however, the primer sequences are not identical to the reference sequence as described in the Methods. The limits of detection for each RT-PCR primer pair were determined using known quantities of a synthetic calibrant RNA template. Found at: doi:10.1371/journal.pone.0000489.s001 (0.06 MB PDF) Table S2 Distribution of BC-types observed in influenza A H3N2 positive human respiratory samples. Unique base compositions at each genome segment locus analyzed were assigned letter codes and concatenation of letter codes across the six loci analyzed yielded BC-types. H1N1 samples were not assigned a BC-type. Experimentally determined BC-types (marked RT-PCR/ESI-MS Analysis results) were compared to BC-type signature information of sequences currently available in GenBank and the closest matching strain is shown (right pane). Last column shows comparison to clade designation described in Holmes et al [12] . Found at: doi:10.1371/journal.pone.0000489.s002 (0.24 MB PDF)  Transmission Parameters of the 2001 Foot and Mouth Epidemic in Great Britain Despite intensive ongoing research, key aspects of the spatial-temporal evolution of the 2001 foot and mouth disease (FMD) epidemic in Great Britain (GB) remain unexplained. Here we develop a Markov Chain Monte Carlo (MCMC) method for estimating epidemiological parameters of the 2001 outbreak for a range of simple transmission models. We make the simplifying assumption that infectious farms were completely observed in 2001, equivalent to assuming that farms that were proactively culled but not diagnosed with FMD were not infectious, even if some were infected. We estimate how transmission parameters varied through time, highlighting the impact of the control measures on the progression of the epidemic. We demonstrate statistically significant evidence for assortative contact patterns between animals of the same species. Predictive risk maps of the transmission potential in different geographic areas of GB are presented for the fitted models. The 2001 FMD epidemic in the UK had a substantial cost in human, animal health and economic terms (Alexandersen et al. [1] , Kao [2] ). Understanding the risk factors underlying the transmission dynamics of that epidemic and evaluating the effectiveness of the control measures are essential to minimise the scale and cost of any future outbreak. Epidemic modelling [3, 4, [5] [6] [7] proved critical to decision making about control policies which were (in some cases controversially) adopted to control the 2001 epidemic [8] [9] [10] . Modelling now has a 'peace-time' contingency planning role.