New Phylogenetic Models Incorporating Interval-Specific Dispersal Dynamics Improve Inference of Disease Spread

Area of Science:

• Epidemiology
• Computational Biology
• Viral Evolution

Background:

• Phylodynamic models are crucial for understanding viral geographic spread, notably during the COVID-19 pandemic.
• Existing models often assume constant viral dispersal rates, contradicting real-world observations.

Purpose of the Study:

• To extend phylodynamic models to incorporate time-varying rates of viral geographic dispersal.
• To develop methods for inferring dispersal event timing and statistics for model fit assessment.
• To apply these enhanced models to SARS-CoV-2 data for improved pandemic insights.

Main Methods:

• Developed phylodynamic models allowing independent variation in average and relative dispersal rates across time intervals.
• Implemented methods to infer the number and timing of inter-area viral dispersal events.
• Created statistics to evaluate the absolute fit of discrete-geographic phylodynamic models.

Main Results:

• Simulations demonstrated that ignoring temporal variation severely biases parameter estimates.
• Interval-specific models significantly improved both relative and absolute fit to empirical SARS-CoV-2 data.
• The enhanced models revealed significant temporal variation in global viral dispersal rates and routes.

Conclusions:

• Accounting for temporal variation in viral dispersal is essential for accurate phylodynamic analysis.
• The new models provide qualitatively different and more realistic inferences about the COVID-19 pandemic.
• Findings alter interpretations of intervention efficacy by highlighting dynamic changes in viral spread patterns.