title: Inference for nonlinear epidemiological models using genealogies and time series.
abstract: 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.
authors: Department of Biology, Duke University, Durham, North Carolina, United States of America. dar24@duke.edu
authors: David A
authors: DA
authors: Rasmussen
authors: Oliver
authors: O
authors: Ratmann
authors: Katia
authors: K
authors: Koelle
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019295:Computational Biology / Q000379:methods; D018562:Disease Transmission, Infectious; D058872:Epidemics; D004812:Epidemiologic Methods; D008954:Models, Biological; D009010:Monte Carlo Method; D017711:Nonlinear Dynamics; D010802:Phylogeny; D011157:Population Dynamics; D015995:Prevalence; D013269:Stochastic Processes