A computationally tractable birth-death model that combines phylogenetic and epidemiological data

Area of Science:

• Epidemiology
• Mathematical Biology
• Computational Biology

Background:

• Inferring pathogen transmission dynamics is crucial for outbreak management.
• Traditional methods use either case data (epidemiology) or genetic sequences (phylodynamics).
• Combining these data sources can improve estimates but is computationally challenging.

Purpose of the Study:

• To develop a novel, computationally efficient method for inferring pathogen transmission dynamics.
• To integrate epidemiological and phylodynamic data for more robust estimates.
• To enable real-time analysis of large-scale outbreak data.

Main Methods:

• Developed a novel birth-death phylogenetic model.
• Derived a tractable analytic approximation for the model's likelihood.
• Achieved linear computational complexity with respect to dataset size.

Main Results:

• The analytic approximation shows good agreement with existing methods.
• Validated the linear computational complexity, enabling scalability.
• Demonstrated robustness to model misspecification using simulated data.

Conclusions:

• The novel birth-death model and likelihood approximation offer an efficient approach to combine diverse data sources for transmission inference.
• This method facilitates the analysis of large genomic datasets, crucial for modern infectious disease epidemiology.
• The approach enhances the utility of phylodynamic and epidemiological data for real-time outbreak monitoring.