Daniela De Angelis
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.
- De Angelis, D., Presanis, A. M., Conti, S. & Ades, A. E. (2014)
Estimation of HIV burden through Bayesian evidence synthesis.
Statistical Science, (in press)
- Turner, E. L., Sweeting M. J., Lindfield, R. J. & De Angelis, D. (2014)
Incidence estimation using a single cross-sectional age-specific prevalence survey with differential mortality.
Statistics in Medicine 33: (3), 422-435
- 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, (early view)
- 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
- Birrell, P. J., Ketsetzis, G., Gay, N. J., Cooper, B. S., Presanis, A. M., Harris, R. J., Charlett, A., Zhang, X.-S., White, P. J., Pebody, R. G. & De Angelis, D. (2011)
Unmasking the pandemic: a Bayesian reconstruction of influenza A/H1N1pdm dynamics in London.
Proceedings of the National Academy of Sciences USA 108: 18238-18243
- Presanis, A. M., De Angelis, D., Goubar, A., Gill, O. N. & Ades, A. E. (2011)
Bayesian evidence synthesis for a transmission dynamic model for HIV among MSM in England and Wales.
Biostatistics 12: 666-681
- Goubar, A., Ades, A. E., De Angelis, D., McGarrigle, C. A., Mercer, C., Tookey, P., Fenton, K. & Gill, O. N. (2008)
Bayesian multi-parameter synthesis of HIV surveillance data in England and Wales, 2001. (With discussion).
Journal of the Royal Statistical Society: Series A 171: 541-580
- Sweeting, M. J., Farewell, V. & De Angelis, D. (2010)
Multi-state Markov models for disease progression in the presence of informative examination times: an application to hepatitis C.
Statistics in Medicine 29: 1161-1174
- Sweeting, M. J., De Angelis, D., Hickman, M. & Ades, A. E. (2008)
Estimating Hepatitis C Prevalence by synthesising evidence from multiple data sources. Assessing data conflict and model fit.
Biostatistics 9: 715-734