Fitting stochastic epidemic models to gene genealogies using linear noise approximation.

Area of Science:

• Population genetics
• Epidemiology
• Computational biology

Background:

• Phylodynamics reconstructs population history from molecular sequences.
• Estimating population size changes from infectious disease data is crucial for understanding epidemic dynamics.
• Current methods have limitations in interpretability, epidemiological parameter estimation, or computational efficiency.

Purpose of the Study:

• To develop a computationally tractable Bayesian model combining phylodynamic inference and stochastic epidemic models.
• To improve the estimation of infectious disease transmission parameters and population size trajectories.
• To enable more robust analysis of epidemic spread using genetic data.

Main Methods:

• Proposed a Bayesian model integrating phylodynamics with stochastic epidemic models.
• Utilized linear noise approximation (LNA) for computational tractability of epidemic model trajectories.
• Employed Markov chain Monte Carlo (MCMC) methods for posterior distribution approximation.

Main Results:

• The developed method successfully recovers parameters of stochastic epidemic models in simulations.
• Demonstrated the application of the technique to Ebola virus genetic data from the 2014 West Africa epidemic.
• The LNA-based approach allows for efficient joint posterior distribution approximation.

Conclusions:

• The novel Bayesian model offers a computationally efficient and statistically advantageous approach for phylodynamic inference.
• This method enhances our ability to understand and track infectious disease dynamics using molecular and epidemiological data.
• The approach is applicable to real-world epidemic scenarios, such as the Ebola outbreak.