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.
Type
Journal articleSubject
AlgorithmsBayes Theorem
Computational Biology
Disease Transmission, Infectious
Epidemics
Epidemiologic Methods
Models, Biological
Monte Carlo Method
Nonlinear Dynamics
Phylogeny
Population Dynamics
Prevalence
Stochastic Processes
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https://hdl.handle.net/10161/5952Published Version (Please cite this version)
10.1371/journal.pcbi.1002136Publication Info
Rasmussen, David A; Ratmann, Oliver; & Koelle, Katia (2011). Inference for nonlinear epidemiological models using genealogies and time series.
PLoS Comput Biol, 7(8). pp. e1002136. 10.1371/journal.pcbi.1002136. Retrieved from https://hdl.handle.net/10161/5952.This is constructed from limited available data and may be imprecise. To cite this
article, please review & use the official citation provided by the journal.
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Show full item recordScholars@Duke
Katharina V. Koelle
Associate Professor in the Department of Biology
My research focuses on the ecology and evolution of infectious diseases. I use a combination
of mathematical and statistical approaches to understand the processes driving the
disease dynamics of pathogens. My interests include developing models to improve our
understanding of how immune escape and other viral phenotypes impact the ecological
dynamics of RNA viruses, and, in turn, how these ecological dynamics create selection
pressures on viral pathogens. Additional interests include developing
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