Bayesian Phylodynamic Inference of Multitype Population Trajectories Using Genomic Data.

Area of Science:

• Computational Biology
• Epidemiology
• Evolutionary Biology

Background:

• Phylodynamic methods analyze genetic data to infer population parameters.
• Multitype birth-death models track discrete traits, geographic movements, and disease stages.
• Current methods integrate over population trajectories, limiting direct demographic inference.

Purpose of the Study:

• To develop a phylodynamic method for inferring multitype population trajectories directly from genetic data.
• To enable the estimation of trait-specific population sizes and demographic quantities.
• To enhance understanding of epidemic spread and macroevolutionary trends.

Main Methods:

• Developed a novel phylodynamic approach based on multitype birth-death models.
• Integrated inference of population trajectories with existing parameter estimation.
• Applied the method to Middle East respiratory syndrome coronavirus (MERS-CoV) genomic data.

Main Results:

• Successfully inferred multitype population trajectories with minimal computational overhead.
• Quantified human and camel cases of MERS-CoV over time.
• Determined the number and timing of spillovers from the camel reservoir to humans.

Conclusions:

• The new method allows direct inference of demographic quantities previously inaccessible.
• This approach provides deeper insights into population dynamics beyond directly ancestral sampled members.
• Demonstrated utility in understanding zoonotic disease transmission, exemplified by MERS-CoV.