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dc.contributor.author Rasmussen, DA
dc.contributor.author Ratmann, O
dc.contributor.author Koelle, K
dc.coverage.spatial United States
dc.date.accessioned 2012-10-30T17:48:46Z
dc.date.issued 2011-08
dc.identifier http://www.ncbi.nlm.nih.gov/pubmed/21901082
dc.identifier PCOMPBIOL-D-10-00410
dc.identifier.citation PLoS Comput Biol, 2011, 7 (8), pp. e1002136 - ?
dc.identifier.uri http://hdl.handle.net/10161/5952
dc.description.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.
dc.format.extent e1002136 - ?
dc.language eng
dc.relation.ispartof PLoS Comput Biol
dc.relation.isversionof 10.1371/journal.pcbi.1002136
dc.subject Algorithms
dc.subject Bayes Theorem
dc.subject Computational Biology
dc.subject Disease Transmission, Infectious
dc.subject Epidemics
dc.subject Epidemiologic Methods
dc.subject Models, Biological
dc.subject Monte Carlo Method
dc.subject Nonlinear Dynamics
dc.subject Phylogeny
dc.subject Population Dynamics
dc.subject Prevalence
dc.subject Stochastic Processes
dc.title Inference for nonlinear epidemiological models using genealogies and time series.
dc.type Journal Article
duke.description.issue 8 en_US
duke.description.volume 7 en_US
dc.relation.journal PLoS Computational Biology en_US
pubs.author-url http://www.ncbi.nlm.nih.gov/pubmed/21901082
pubs.issue 8
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Institutes and Provost's Academic Units
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers/Global Health Institute
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Biology
pubs.publication-status Published
pubs.volume 7
dc.identifier.eissn 1553-7358

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