Oliver Ratmann

Summary

Affiliation: Imperial College
Country: UK

Publications

  1. doi request reprint Sources of HIV infection among men having sex with men and implications for prevention
    Oliver Ratmann
    Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W21PG, UK
    Sci Transl Med 8:320ra2. 2016
  2. pmc 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
    PLoS Comput Biol 8:e1002835. 2012
  3. pmc 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
  4. pmc 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
  5. pmc 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
  6. pmc 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
  7. doi request reprint A Method to Estimate the Size and Characteristics of HIV-positive Populations Using an Individual-based Stochastic Simulation Model
    Fumiyo Nakagawa
    From the aResearch Department of Infection and Population Health, UCL, London, United Kingdom bStichting HIV Monitoring, Amsterdam, The Netherlands cInserm, centre Inserm U897, Bordeaux, France dDepartment of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom eDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden fDepartment of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden gEuropean Centre for Disease Prevention and Control ECDC, Stockholm, Sweden hCEEISCAT, Generalitat de Catalunya, Barcelona, Spain iWHO Regional Office for Europe, Copenhagen, Denmark jInstitute of Clinical Trials and Methodology, UCL, London, United Kingdom kDivision of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland lResearch Department of Primary Care and Population Health, UCL, London, United Kingdom mCHIP Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark nDepartment of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece oUCL Institute of Child Health, UCL, London
    Epidemiology 27:247-56. 2016

Collaborators

Detail Information

Publications7

  1. doi request reprint Sources of HIV infection among men having sex with men and implications for prevention
    Oliver Ratmann
    Department of Infectious Disease Epidemiology, School of Public Health, Imperial College London, London W21PG, UK
    Sci Transl Med 8:320ra2. 2016
    ..With increasing sequence coverage, molecular epidemiological analyses can be a key tool to direct HIV prevention strategies to the predominant sources of infection, and help send HIV epidemics among MSM into a decisive decline. ..
  2. pmc 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
    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...
  3. pmc 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
    ....
  4. pmc 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...
  5. pmc 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
    ....
  6. pmc 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...
  7. doi request reprint A Method to Estimate the Size and Characteristics of HIV-positive Populations Using an Individual-based Stochastic Simulation Model
    Fumiyo Nakagawa
    From the aResearch Department of Infection and Population Health, UCL, London, United Kingdom bStichting HIV Monitoring, Amsterdam, The Netherlands cInserm, centre Inserm U897, Bordeaux, France dDepartment of Infectious Disease Epidemiology, Imperial College London, London, United Kingdom eDepartment of Microbiology, Tumor and Cell Biology, Karolinska Institute, Stockholm, Sweden fDepartment of Clinical Microbiology, Karolinska University Hospital, Stockholm, Sweden gEuropean Centre for Disease Prevention and Control ECDC, Stockholm, Sweden hCEEISCAT, Generalitat de Catalunya, Barcelona, Spain iWHO Regional Office for Europe, Copenhagen, Denmark jInstitute of Clinical Trials and Methodology, UCL, London, United Kingdom kDivision of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland lResearch Department of Primary Care and Population Health, UCL, London, United Kingdom mCHIP Department of Infectious Diseases, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark nDepartment of Hygiene, Epidemiology and Medical Statistics, University of Athens Medical School, Athens, Greece oUCL Institute of Child Health, UCL, London
    Epidemiology 27:247-56. 2016
    ..We demonstrate that our method can be applied to settings with less data, however plausibility ranges for estimates will be wider to reflect greater uncertainty of the data used to fit the model. ..