The process of using genetic markers to estimate CST rates should take into account several important factors to reduce bias. One is that the phylogenetic tree constructed in the analysis needs to capture the underlying epidemiological process generating the tree. The models need to account for how the genetic variability of a pathogen influences a disease in a species, not just general differences in genomic structure. Two, the strength of the analysis will depend on the amount of mutation accumulated since the pathogen was introduced in the system. This is due to many models using the number of mutations as an indicator of CST frequency. Therefore, efforts are focused on estimating either time since the introduction or the substitution rate of the marker (from laboratory experiments or genomic comparative analysis). This is important not only when using the MPR method but also for Likelihood approaches that require an estimation of the mutation rate. Three, CST will also affect disease prevalence in the potential host, so combining both epidemiological time series data with genetic data may be an excellent approach to CST study

Bayesian analysis
Bayesian frameworks are a form of maximum likelihood-based analyses and can be very effective in cross-species transmission studies. Bayesian inference of character evolution methods can account for phylogenetic tree uncertainty and more complex scenarios, with models such as the character diffusion model currently being developed for the study of CST in RNA viruses. A Bayesian statistical approach presents advantages over other analyses for tracking CST origins.
Computational techniques allow integration over an unknown phylogeny, which cannot be directly observed, and unknown migration process, which is usually poorly understood.The Bayesian frameworks are also well suited to bring together different kinds of information. The BEAST software, which has a strong focus on calibrated phylogenies and genealogies, illustrates this by offering a large number of complementary evolutionary models including substitution models, demographic and relaxed clock models that can be combined into a full probabilistic model. By adding spatial reconstruction, these models create the probability of biogeographical history reconstruction from genetic data. This could be useful for determining the origins of cross-species transmissions. The high effectiveness of Bayesian statistical methods has made them instrumental in evolutionary studies. Bayesian ancestral host reconstruction under discrete diffusion models can be used to infer the origin and effects of pathogens associated with CST. One study on Human adenoviruses using Bayesian supported a gorilla and chimpanzee origin for the viral species, aiding prevention efforts. Despite presumably rare direct contact between sympatric populations of the two species, CST events can occur between them. The study also determined that two independent HAdV-B transmission events to humans occurred and that the HAdV-Bs circulating in humans are of zoonotic origin and have probably affected global health for most of our species lifetime.Phylogenetic diffusion models are frequently used for phylogeographic analyses, with the inference of host jumping becoming of increasing interest.