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Phylodynamic inference for structured epidemiological models.

David A Rasmussen1, Erik M Volz2, Katia Koelle3

  • 1Biology Department, Duke University, Durham, North Carolina, United States of America.

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This study introduces a new statistical framework to fit complex epidemiological models to genetic data. The method combines particle filtering and Bayesian inference to analyze population dynamics and structure from genealogies.

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Area of Science:

  • Evolutionary biology
  • Epidemiology
  • Computational biology

Background:

  • Coalescent theory is vital for estimating population dynamics from genealogies.
  • Complex demographic scenarios and pathogen populations (e.g., RNA viruses) pose challenges for traditional models.
  • Existing methods struggle with nonlinear dynamics, population structure, and stochastic variation in epidemiological data.

Purpose of the Study:

  • Develop a statistical framework to fit stochastic epidemiological models to genealogies.
  • Accommodate complex population dynamics and structure using advanced coalescent models.
  • Enable robust inference for rapidly changing and structured populations.

Main Methods:

  • Utilized recently developed structured coalescent models.
  • Combined particle filtering with Bayesian Markov chain Monte Carlo (MCMC) methods.
  • Applied the framework to multi-stage disease progression and two-population spatial structure models.

Main Results:

  • Successfully fitted a wide class of stochastic, nonlinear epidemiological models to genealogies.
  • Applied the multi-stage model to HIV genealogies, estimating stage-specific transmission rates and prevalence.
  • Explored information content in genealogies for population structure and migration rate inference.

Conclusions:

  • The developed framework enables fitting complex stochastic epidemiological models to genealogies.
  • Demonstrated utility in analyzing HIV transmission dynamics and spatial population structure.
  • Provides insights into sample size requirements for inferring population structure parameters like migration rates.