Spreading in time and space, rapidly evolving viruses can accumulate a considerable amount of genetic variation. As a consequence, viral genomes become valuable resources to reconstruct the spatial and temporal processes that are shaping epidemic or endemic dynamics. In molecular epidemiology, spatial inference is often limited to the interpretation of evolutionary histories with respect to the sampling locations of the pathogens. To test hypotheses about the spatial diffusion patterns of viruses, analytical techniques are required that enable us to reconstruct how viruses migrated in the past. Here, we develop a model to infer diffusion processes among discrete locations in timed evolutionary histories in a statistically efficient fashion. Applications to Avian Influenza A H5N1 and Rabies virus in Central and West African dogs demonstrate several advantages of simultaneously inferring spatial and temporal processes from gene sequences. quantification of uncertainty in both the ancestral state reconstructions and the underlying phylogeographic process. Further, as we are able to infer plausible root positions unlike the original analysis, we are not required to include outgroup sequences. To model sequence evolution, we employ the [29] (HKY85) CTMC model of nucleotide substitution; we include discrete gamma-distributed rate variation [30] and assume an unknown, constant population-size coalescent process prior over the unknown phylogeny [31] . Exploratory analyses using the less restrictive Bayesian skyline plot model indicate that the demographic prior has little influence on the phylogeographic inference (data not shown). Figure 1 summarizes the Bayesian maximum clade credibility (MCC) trees for the A-H5N1 HA and NA segments. An MCC tree is a point-estimate characterizing the posterior distribution of trees and represents the tree topology yielding the highest product of individual clade probabilities in their posterior sample [2] ; branch lengths in these MCC trees are posterior median estimates.