Email Address: firstname.lastname@example.org
Other Research Theme Collaborations: SOMX PREM
BSU Research Overview
To inform and evaluate public health policies in the area of infectious diseases, information is needed on the size of the affected population and the current levels of disease transmission, possibly in specific groups of the population, in different locations and perhaps in real time. This information is not readily available but could be acquired through the analysis of available data. These data are typically incomplete, are affected by biases and may arrive in real time, often challenging standard estimation approaches. The aim of our work is, therefore, to develop and apply statistical methods to characterise epidemics, fully and correctly exploiting the complex body of available information on different aspects of the disease of interest. Our goal is to provide accurate and timely quantitative support to the implementation and evaluation of policies, particularly in the areas of HIV, hepatitis and influenza.
Daniela is the Deputy Director of the MRC Biostatistics Unit.To view Daniela's recently submitted papers in arXiv, click here: Daniela De Angelis - Submitted recent papers in arXiv
Selected PapersPaul J Birrell, Daniela De Angelis, Anne M Presanis (in press)Evidence synthesis of stochastic epidemic models
Statistical Science :
Birrell PJ, Pebody RG, Charlett A, Zhang XS, De Angelis D (2017)Real-time modelling of a pandemic influenza outbreak.
Health Technol Assess 21(58): 1-118
Birrell PJ, Zhang XS, Pebody RG, Gay NJ, De Angelis D. (2016)Reconstructing a spatially heterogeneous epidemic: Characterising the geographic spread of 2009 A/H1N1pdm infection in England.
Sci Rep Jul 11;6: 29004. doi: 10.1038/srep29004.
Zhang XS, De Angelis D (2016)Construction of the influenza A virus transmission tree in a college-based population: co-transmission and interactions between influenza A viruses
BMC Infect Dis 16:38:
Worby CJ, O’Neill PD, Kypraios T, Robotham JV, De Angelis D, Cartwright EJP, Peacock S, Cooper BS (2016)Reconstructing transmission networks for communicable diseases using densely sampled genomic data: a generalized approach
Annals of Applied Statistics 10(1): 395-417
Rosinska M, Gwiazda P, De Angelis D, Presanis AM. (2016)Bayesian evidence synthesis to estimate HIV prevalence in men who have sex with men in Poland at the end of 2009.
Epidemiol Infect 144(6): 1175-91
Heesterbeek H1, Anderson RM2, Andreasen V3, Bansal S4, De Angelis D5, Dye C6, Eames KT7, Edmunds WJ7, Frost SD8, Funk S4, Hollingsworth TD9, House T10, Isham V11, Klepac P8, Lessler J12, Lloyd-Smith JO13, Metcalf CJ14, Mollison D15, Pellis L10, Pulliam JR16, Roberts MG17, Viboud C18; Isaac Newton Institute IDD Collaboration. (2015)Modeling infectious disease dynamics in the complex landscape of global health.
Science 13: 347(6227):aaa4339. doi: 10.1126/science.aaa4339
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
Prevost TC, Presanis AM, Taylor A, Goldberg DJ, Hutchinson SJ, De Angelis D. (2015)Estimating the number of people with hepatitis C virus who have ever injected drugs and have yet to be diagnosed: an evidence synthesis approach for Scotland.
Addiction 110(8): 1287-300
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 AM, Pebody R, 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
M. Farah, P. Birrell, S. Conti, and D. De Angelis (2014)Bayesian Emulation and Calibration of a Dynamic Epidemic Model for H1N1 Influenza
The Journal of the American Statistical Association 109(508): 1398-1411
Jackson, J., Jit, M., Sharples, L. & De Angelis, D (2013)Calibration of complex models through Bayesian evidence synthesis: a demonstration and tutorial.
Medical Decision Making 35(2): 148-161
Birrell PJ, Gill ON, Delpech VC, Brown AE, Desai S, Chadbon TR, Rice BD, De Angelis D. (2013)Trends in HIV incidence among men who have sex with men in England and Wales in the era of increased HIV testing and treatment: a nationwide population study 2001 to 2010
Lancet Infectious Diseases, 13: 313–318
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
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
Birrell, P. J., Gill, O. N., Delpech, V. C., Brown, A. E., Desai, S., Chadborn, T. R., Rice, B. D. & De Angelis, D. (2013)HIV incidence in men who have sex with men in England and Wales 2001–10: a nationwide population study.
Lancet Infectious Diseases 13: (4): 313-318
Harris, R. J., Hope, V., Marongiou, A., Hickman, M., Ncube, F. & De Angelis, D. (2012)Spatial mapping of Hepatitis C prevalence in recent injecting drug users in contact with services.
Epidemiology and Infection 140: 1-10
Harris, R. J., Ramsay, M., Hope, V., Brant, L., Hickman, M., Foster, G. R. & De Angelis, D. (2012)Hepatitis C prevalence in England remains low and varies by ethnicity: an updated evidence synthesis.
European Journal of Public Health 22: 187-192