Oliver Ratmann

Summary

Affiliation: Imperial College
Country: UK

Publications

  1. ncbi Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study
    Oliver Ratmann
    Department of Biology, Duke University, Durham, North Carolina, United States of America Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    PLoS Comput Biol 8:e1002835. 2012
  2. ncbi Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum
    Oliver Ratmann
    Department of Public Health and Epidemiology, Imperial College London, London, United Kingdom
    PLoS Comput Biol 3:e230. 2007
  3. ncbi Model criticism based on likelihood-free inference, with an application to protein network evolution
    Oliver Ratmann
    Department of Public Health and Epidemiology, Imperial College London, London, United Kingdom
    Proc Natl Acad Sci U S A 106:10576-81. 2009
  4. ncbi Inference for nonlinear epidemiological models using genealogies and time series
    David A Rasmussen
    Department of Biology, Duke University, Durham, North Carolina, United States of America
    PLoS Comput Biol 7:e1002136. 2011
  5. ncbi A dimensionless number for understanding the evolutionary dynamics of antigenically variable RNA viruses
    Katia Koelle
    Department of Biology, Duke University, PO Box 90338, Durham, NC 27708, USA
    Proc Biol Sci 278:3723-30. 2011

Detail Information

Publications5

  1. ncbi Phylodynamic inference and model assessment with approximate bayesian computation: influenza as a case study
    Oliver Ratmann
    Department of Biology, Duke University, Durham, North Carolina, United States of America Department of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom
    PLoS Comput Biol 8:e1002835. 2012
    ..ABC, one of several data synthesis approaches, can easily interface a broad class of phylodynamic models with various types of data but requires careful calibration of the summaries and tolerance parameters...
  2. ncbi Using likelihood-free inference to compare evolutionary dynamics of the protein networks of H. pylori and P. falciparum
    Oliver Ratmann
    Department of Public Health and Epidemiology, Imperial College London, London, United Kingdom
    PLoS Comput Biol 3:e230. 2007
    ....
  3. ncbi Model criticism based on likelihood-free inference, with an application to protein network evolution
    Oliver Ratmann
    Department of Public Health and Epidemiology, Imperial College London, London, United Kingdom
    Proc Natl Acad Sci U S A 106:10576-81. 2009
    ..Our results make a number of model deficiencies explicit, and suggest that the T. pallidum network topology is inconsistent with evolution dominated by link turnover or lateral gene transfer alone...
  4. ncbi Inference for nonlinear epidemiological models using genealogies and time series
    David A Rasmussen
    Department of Biology, Duke University, Durham, North Carolina, United States of America
    PLoS Comput Biol 7:e1002136. 2011
    ....
  5. ncbi A dimensionless number for understanding the evolutionary dynamics of antigenically variable RNA viruses
    Katia Koelle
    Department of Biology, Duke University, PO Box 90338, Durham, NC 27708, USA
    Proc Biol Sci 278:3723-30. 2011
    ..We end with predictions of our framework and work that remains to be done to further integrate viral evolutionary dynamics with disease ecology...