Email Address: firstname.lastname@example.org
Other Research Theme Collaborations: PREM
My current research is on Bayesian evidence synthesis methods using complex probabilistic models, with a particular focus on model criticism. The research is motivated by a need to estimate key characteristics of infectious disease, from multiple data sources that are often disparate, available at different levels of aggregation and biased. Current projects and interests include:
- evidence synthesis models for estimating the severe burden of influenza;
- investigating strategies for combining complex evidence synthesis models, with a focus on conflict and consistency;
- extending an evidence synthesis-based dynamic transmission model of HIV to jointly estimate prevalence, incidence and transmission;
- investigating, developing and disseminating general evidence synthesis methods for estimating (severe) disease burden, e.g.: prevalence of HCV in Scotland; prevalence of HIV in Poland; incidence of influenza in the Netherlands;
- and generalised methods for model criticism and comparison in complex evidence synthesis, motivated by the above applications and focussing on conflict diagnostic methods.
Selected PapersBirrell, P. J., De Angelis, D., Presanis, A. M. (2017)Evidence synthesis for stochastic epidemic models.
Statistical Science : In press
Presanis, A. M., Ohlssen D., Cui K., Rosinska M., De Angelis, D. (2017)Conflict diagnostics for evidence synthesis in a multiple testing framework.
E-print : arXiv:1702.07304
Goudie, R. J. B., Presanis, A. M., Lunn D., De Angelis, D., Wernisch L. (2016)Joining and splitting models with Markov melding.
E-print : arXiv:1607.06779
Rosinska M., Gwiazda P., De Angelis, D., Presanis, A. M. (2016)Bayesian evidence synthesis to estimate HIV prevalence in men who have sex with men in Poland at the end of 2009.
Epidemiology and Infection 144: (6)
De Angelis, D., Presanis, A. M., Birrell, P. J., Tomba G. S. , House T. (2015)Four key challenges in infectious disease modelling using data from multiple sources.
Epidemics 10: , 83–7
Presanis, A. M., Pebody, R. G., Birrell, P. J., Tom, B. D. M., Green, H. K., 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, A. M., Conti, S. & Ades, A. E. (2014)Estimation of HIV burden through Bayesian evidence synthesis.
Statistical Science 29: (1), 9-17
Presanis, A. M., Ohlssen, D., Spiegelhalter, D. J. & De Angelis, D. (2013)Conflict diagnostics in directed acyclic graphs, with applications in Bayesian evidence synthesis.
Statistical Science 28: (3), 376-397