Inference for nonlinear epidemiological models using genealogies and time series.

Published

Journal Article

Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from reconstructed genealogies. As sequence data have become more abundant, these approaches are beginning to be used on populations undergoing rapid and rather complex dynamics. In such cases, the simple demographic models that current phylodynamic methods employ can be limiting. First, these models are not ideal for yielding biological insight into the processes that drive the dynamics of the populations of interest. Second, these models differ in form from mechanistic and often stochastic population dynamic models that are currently widely used when fitting models to time series data. As such, their use does not allow for both genealogical data and time series data to be considered in tandem when conducting inference. Here, we present a flexible statistical framework for phylodynamic inference that goes beyond these current limitations. The framework we present employs a recently developed method known as particle MCMC to fit stochastic, nonlinear mechanistic models for complex population dynamics to gene genealogies and time series data in a Bayesian framework. We demonstrate our approach using a nonlinear Susceptible-Infected-Recovered (SIR) model for the transmission dynamics of an infectious disease and show through simulations that it provides accurate estimates of past disease dynamics and key epidemiological parameters from genealogies with or without accompanying time series data.

Full Text

Cited Authors

  • Rasmussen, DA; Ratmann, O; Koelle, K

Published Date

  • August 25, 2011

Published In

Volume / Issue

  • 7 / 8

Start / End Page

  • e1002136 -

PubMed ID

  • 21901082

Pubmed Central ID

  • 21901082

Electronic International Standard Serial Number (EISSN)

  • 1553-7358

International Standard Serial Number (ISSN)

  • 1553-734X

Digital Object Identifier (DOI)

  • 10.1371/journal.pcbi.1002136

Language

  • eng