MASCOT-Skyline integrates population and migration dynamics to enhance phylogeographic reconstructions

Area of Science:

• Epidemiology
• Genomics
• Computational Biology

Background:

• Infectious disease spread is influenced by spatial and temporal factors.
• Phylodynamic methods analyze pathogen genomes to understand disease spread.
• Current methods often analyze spatial or temporal dynamics separately, leading to potential biases.

Purpose of the Study:

• To develop a novel phylodynamic method for jointly inferring spatial and temporal transmission dynamics.
• To address limitations and biases in existing phylogeographic methods.
• To provide a robust framework for analyzing infectious disease spread.

Main Methods:

• Introduction of the MASCOT-Skyline structured coalescent skyline approach.
• Utilizing Markov chain Monte Carlo inference for joint spatial and temporal dynamics.
• Modeling effective population size dynamics non-parametrically across locations.

Main Results:

• Demonstrated potential issues with existing phylogeographic methods using viral outbreak datasets.
• Simulations revealed biases in phylogeographic methods, particularly with subsampling.
• Showcased the necessity of jointly modeling spatial and temporal dynamics for accurate inference.

Conclusions:

• Spatial and temporal transmission dynamics should be modeled jointly for accurate phylodynamic inference.
• Identified conditions leading to biased phylogeographic analyses and provided recommendations.
• MASCOT-Skyline is implemented in the open-source MASCOT package for BEAST2.