Some other developments would require more effort. In particular, it would be desirable to consider the various parameters of the model from a probabilistic perspective. For instance, the date of probable transmission could be better captured by a distribution of the time during which a specific sequence remains unaltered rather than a fixed collection date for the isolate. Moreover, a probabilistic framework would also allow incorporation of information besides genetic sequences and collection dates, such as prevalence data or spatial connectivity between locations. A final possible extension of SeqTrack relates to the inference of unsampled ancestral isolates. Although phylogenetic methods assume that no isolate in the sample is (directly or indirectly) ancestral to another one, our method considers that ancestries can be inferred between the sampled isolates. In practice, however, the ancestral population of a given isolate may not have been sampled. In such cases, SeqTrack is likely to identify the most recent sampled ancestor of the isolate as a substitute for the unsampled direct ancestor (Figures 1d, 2b, and 3b) . In other words, the algorithm is likely to retrieve an actual branch of the transmission tree by overlooking unsampled intermediate nodes. The possibility of inferring unobserved intermediate nodes based on a probabilistic model for sequence evolution and spatial dispersal (Lemey et al., 2009a) would represent an additional significant improvement in the present approach.