MRC Biostatistics Unit
Research theme(s): Population Health
Role: Senior Statistician
Telephone number: 01223 330374
Email Address: anne.presanis@mrc-bsu.cam.ac.uk
Role: Senior Statistician
Telephone number: 01223 330374
Email Address: anne.presanis@mrc-bsu.cam.ac.uk
My current research lies at the intersection of statistics and epidemiology. My focus is on building and assessing Bayesian evidence synthesis models, motivated by a need, for healthcare policy-makers, to know the burden of infectious disease: prevalence, incidence and severity. Available evidence is usually observational, from multiple, disparate and biased data sources. Key projects include:
Google Scholar profile
- the estimation of HIV and HCV prevalence, particularly undiagnosed prevalence, in the UK and in Europe, in collaboration with the UK Health Security Agency (UKHSA, formerly Public Health England) and Health Protection Scotland;
- the estimation of severity of respiratory infections, including seasonal and pandemic influenza, secondary bacterial infection, and COVID-19, in collaboration with UKHSA, WHO Europe and ECDC;
- and generalised methods for model building, criticism and comparison in complex evidence synthesis, motivated by the above applications and focussing on conflict diagnostic methods.
Google Scholar profile
Selected Papers
Kirwan PD, Charlett A, Birrell PJ, Elgohari S, Hope R, Mandal S, De Angelis D, Presanis AM. (2022)Trends in COVID-19 hospital outcomes in England before and after vaccine introduction, 2020-2021: a cohort study.Nature Communications 13: 4834
Seaman SR, Presanis AM, Jackson CH (2021)Estimating a time-to-event distribution from right-truncated data in an epidemic: A review of methods.
Statistical Methods in Medical Research 31: (9):1641–1655
Presanis AM, Harris RJ, Kirwan PD, Miltz A, Croxford S, Heinsbroek E, Jackson CH, Mohammed H, Brown AE, Delpech VC, Gill ON, De Angelis D (2021)Trends in undiagnosed HIV prevalence in England and implications for eliminating HIV transmission by 2030: an evidence synthesis model.
The Lancet Public Health 6: (10):e739-51.
Nyberg T*, Twohig KA*, Harris RJ, Seaman SR, Flannagan J, Allen H, Charlett A, De Angelis D, Dabrera G, Presanis AM (2021)Risk of hospital admission for patients with SARS-CoV-2 variant B.1.1.7: cohort analysis.
BMJ 373:
Jackson CH, Presanis AM, Conti S, De Angelis D (2019)Value of information: Sensitivity analysis and research design in Bayesian evidence synthesis.
Journal of the American Statistical Association 23: 1-22
Goudie RJB, Presanis AM, Lunn D, De Angelis D, Wernisch L (2019)Joining and splitting models with Markov melding.
Bayesian Analysis 14: (1), 81-109
De Angelis D, Presanis AM, Birrell PJ, Tomba GS, House T (2015)Four key challenges in infectious disease modelling using data from multiple sources.
Epidemics 10: , 83–7
Presanis AM, Pebody RG, Birrell PJ, Tom BDM, Green HK, Durnall H, Fleming D, De Angelis D (2014)Synthesising evidence to estimate pandemic (2009) A/H1N1 influenza severity in 2009-2011
Annals of Applied Statistics 8: (4), 2378-2403
De Angelis D, Presanis AM, Conti S, Ades AE (2014)Estimation of HIV burden through Bayesian evidence synthesis.
Statistical Science 29: (1), 9-17
Presanis AM, Ohlssen D, Spiegelhalter DJ, De Angelis D (2013)Conflict diagnostics in directed acyclic graphs, with applications in Bayesian evidence synthesis.
Statistical Science 28: (3), 376-397