The Bayesian phylogeographic inference framework we present here incorporates the spatial and temporal dynamics of gene flow. In this study, we focus on pathogen diffusion because viral sequence sampling on a time-scale commensurate with the rate of substitution permits the inference of spatial patterns in real-time units. In addition, elucidating the phylodynamics of viral epidemics has important implications for public health management. We selected the Avian influenza A-H5N1 example to allow a convenient comparison of Bayesian ancestral state inference with the previous parsimony analysis; on the other hand, statistical analysis of the rabies migration in Africa up to this point has been largely unexplored. Both zoonoses represent a clear threat to human health. The frequent transmission of A-H5N1 from poultry or wild birds to humans suggest that the virus could emerge as or contribute genetically to the next human flu pandemic. Although the lack of a human-to-human transmission mechanism means that rabies will not emerge as a purely human disease, rabies infection causes a fatal neurological disease and at least 55,000 people die from this disease every year, mainly in the developing world [40] . A Bayesian statistical approach presents many advantages over parsimony inference of ancestral states. First, MCMC offers a computational technique to integrate over an unknown phylogeny and unknown migration process as the former is not directly observable in nature and the latter is poorly understood. Accommodating this lack of knowledge protects against potentially severe bias, but can reduce the power to make inferences; phylogeographic analyses are no exception to this. One can regard this uncertainty itself as a 'mixed blessing' because whilst it can hamper drawing definitive conclusions [13] , it protect us from making overstated conclusions. For example, parsimony analysis of the influenza data establishes an epidemiological link between Guangdong and Indonesia [22] . Bayesian inference does not confirm this conclusion and phylogenetic analysis of more recently obtained sequence data now identifies the progenitors of Indonesian strains in the Chinese province of Hunan [26] , a site which our shared analysis also identified as a possible source. Further, unlike parsimony, likelihood-based probabilistic methods consider branch lengths in ancestral reconstructions. The impact of the tree depth on root state reconstructions for the A-H5N1 genome segments clearly illustrates the importance of branch lengths. Moreover, probabilistic methods allow for estimating the relative posterior probability of each location state at any position along the phylogeny; this ability is indispensable in a hypothesis testing framework. As introduced by [19] in a different setting, we also demonstrate how phylogeographic parameters can be estimated from different genomic segments without assuming the same evolutionary history. H5N1 reassortment, however, will have not have fully unlinked HA and NA evolutionary histories and partially shared ancestry may lead to overstated credibility in some aspects of the phylogeographic inference.