Professor Daniela De Angelis
(she/her)About Professor Daniela De Angelis
I am a Professor of Statistical Science for Health at the University of Cambridge in the Department of Primary Care and Public Health, and Group Leader at the MRC Biostatistics Unit, where I lead the Population Health research theme.
I have over 25 years of experience of working at the interface between statistics and infectious disease epidemiology. Marrying epidemiology and cutting-edge statistical/computational techniques, I have established and led multi-disciplinary teams developing novel methodology and tools to enable epidemic monitoring and prediction. An important focus of my research has been on the statistical questions posed by the combination of heterogeneous data, which are generally of different types, relevance, and quality, affected by observational biases, and present “big data” and privacy challenges. My methodologies have been adopted as methods of choice by national and international Health Agencies to monitor epidemics, informing the implementation and evaluation of public health policies.
I am a member of several scientific advisory groups including the Statistical Expert Group for the Infected Blood Inquiry; the Scientific Pandemic Influenza Advisory Committee (SPI-M); the Royal Statistical Society Task Force for Covid-19. In 2022, for my work during the SARS-CoV2 pandemic, I was awarded the Weldon Memorial Prize as core member of SPI-M; received the University of Cambridge Vice-Chancellor’s Award for Impact and Engagement in the Established Academic category; and became Member of the Most Excellent Order of the British Empire (MBE), Honorary Awards to Foreign Nationals in recognition of her services to Medical Research and Public Health.
Publications
Showing 1 to 10 of 200 publications
Real-time modelling of the SARS-CoV-2 pandemic in England 2020–2023: a challenging data integration
Authors: PJ Birrell, J Blake, J Kandiah, A Alexopoulos, E van Leeuwen, KB Pouwels, S Ghosh, C Starr, AS Walker, TA House, N Gay, T Finnie, N Gent, A Charlett, D De Angelis
Published:
Journal: Journal of the Royal Statistical Society Series A (Statistics in Society)
DOI: 10.1093/jrsssa/qnaf030
Towards the endgame: achieving elimination of viral hepatitis in England.
Authors: S Mandal, PC Matthews, M Desai, M Hickman, UK National Strategic Group on Viral Hepatitis (NSGVH)
Journal: Lancet Microbe
DOI: 10.1016/j.lanmic.2025.101332
Paul Birrell, Joel Kandiah, and Daniela De Angelis’ contribution to the Discussion of ‘Some statistical aspects of the Covid-19 response’ by Wood et al.
Authors: PJ Birrell, J Kandiah, D De Angelis
Published:
Journal: Journal of the Royal Statistical Society Series A (Statistics in Society)
DOI: 10.1093/jrsssa/qnaf151
Daniela De Angelis' invited contribution to the Discussion of ‘Some statistical aspects of the Covid-19 response’ by Wood et al. with contributions from Joel Kandian and Paul Birrell
Authors: D De Angelis, J Kandiah, PJ Birrell
Published:
Journal: Journal of the Royal Statistical Society Series A (Statistics in Society)
DOI: 10.1093/jrsssa/qnaf150
The cumulative incidence and infection hospitalization risk of SARS-CoV-2 by variant: a longitudinal study in England.
Authors: C Gaughan, D Braunholtz, L Massie, T Khera, PJ Birrell, D De Angelis, J Blake, J Preece, K Pouwels, AS Walker, COVID-19 Infection Survey Team
Published:
Journal: Am J Epidemiol
DOI: 10.1093/aje/kwaf203
Inferring epidemics from multiple dependent data via pseudo-marginal methods
Authors: A Corbella, AM Presanis, PJ Birrell, D De Angelis
Published:
Journal: The Annals of Applied Statistics
DOI: 10.1214/25-aoas2015
Analysing HCV incidence trends in people who inject drugs using serial behavioural and seroprevalence data: A modelling study.
Authors: C Egan, RJ Harris, HD Mitchell, M Desai, S Mandal, D De Angelis
Journal: Int J Drug Policy
DOI: 10.1016/j.drugpo.2024.104469
Assessing the Impact of Influenza Epidemics in Hong Kong.
Authors: JY Wong, JK Cheung, AM Presanis, D De Angelis, AD Iuliano, P Wu, BJ Cowling
Published:
Journal: J Infect Dis
DOI: 10.1093/infdis/jiaf140
Contact data and SARS-CoV-2: Retrospective analysis of the estimated impact of the first UK lockdown.
Authors: J Kandiah, E van Leeuwen, PJ Birrell, D De Angelis
Published:
Journal: J Theor Biol
DOI: 10.1016/j.jtbi.2025.112158
Modelling with SPEED: a Stochastic Predictor of Early Epidemic Detection.
Authors: KH Bowers, D De Angelis, PJ Birrell
Published:
Journal: J Theor Biol
DOI: 10.1016/j.jtbi.2025.112120