Anne M Presanis
Affiliation: University of Cambridge
- Insights into the rise in HIV infections, 2001 to 2008: a Bayesian synthesis of prevalence evidenceAnne M Presanis
MRC Biostatistics Unit, Cambridge, UK
AIDS 24:2849-58. 2010..To estimate trends in prevalence of HIV infection, undiagnosed and total, among adults aged 15-44 years in England and Wales since 2001...
- Bayesian evidence synthesis for a transmission dynamic model for HIV among men who have sex with menA M Presanis
MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK
Biostatistics 12:666-81. 2011..Use of additional data or prior information on demographics, risk behavior change and contact parameters allows simultaneous estimation of the transition rates, compartment prevalences, contact rates, and transmission probabilities...
- Changes in severity of 2009 pandemic A/H1N1 influenza in England: a Bayesian evidence synthesisA M Presanis
Medical Research Council Biostatistics Unit, Institute of Public Health, University Forvie Site, Cambridge CB2 0SR, UK
BMJ 343:d5408. 2011....
- The severity of pandemic H1N1 influenza in the United States, from April to July 2009: a Bayesian analysisAnne M Presanis
Medical Research Council Biostatistics Unit, Cambridge, United Kingdom
PLoS Med 6:e1000207. 2009..We sought to estimate the probabilities that symptomatic infection would lead to hospitalization, ICU admission, and death by combining data from multiple sources...
- Four key challenges in infectious disease modelling using data from multiple sourcesDaniela de Angelis
MRC Biostatistics Unit, Cambridge Institute of Public Health, Robinson Way, Cambridge CB2 0SR, UK Public Health England, 61 Colindale Avenue, London NW9 5HT, UK Electronic address
Epidemics 10:83-7. 2015..How to weight evidence from different datasets and handle dependence between them, efficiently estimate and critically assess complex models are key challenges that we expound in this paper, using examples from influenza modelling. ..
- Bayesian modeling to unmask and predict influenza A/H1N1pdm dynamics in LondonPaul J Birrell
Medical Research Council Biostatistics Unit, University Forvie Site, Robinson Way, Cambridge CB2 0SR, United Kingdom
Proc Natl Acad Sci U S A 108:18238-43. 2011..We show how this approach, which allows for real-time learning about model parameters as the epidemic progresses, is also able to provide a sequence of nested projections that are capable of accurately reflecting the epidemic evolution...