Imperial College London
Alternate

DrOliverRatmann

Faculty of Natural Sciences, Department of Mathematics

Reader in Statistics and Machine Learning for Public Good
 
 
 
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Contact

 

oliver.ratmann05 Website

 
 
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Location

 

525Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Rasmussen:2011:10.1371/journal.pcbi.1002136,
author = {Rasmussen, DA and Ratmann, O and Koelle, K},
doi = {10.1371/journal.pcbi.1002136},
journal = {PLOS Computational Biology},
title = {Inference for nonlinear epidemiological models using genealogies and time series},
url = {http://dx.doi.org/10.1371/journal.pcbi.1002136},
volume = {7},
year = {2011}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Rasmussen,DA
AU  - Ratmann,O
AU  - Koelle,K
DO  - 10.1371/journal.pcbi.1002136
PY  - 2011///
SN  - 1553-734X
TI  - Inference for nonlinear epidemiological models using genealogies and time series
T2  - PLOS Computational Biology
UR  - http://dx.doi.org/10.1371/journal.pcbi.1002136
UR  - http://hdl.handle.net/10044/1/33465
VL  - 7
ER  -
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