DrBethan Cracknell Daniels
Honorary Research Associate
School of Public Health - Faculty of Medicine
- Honorary Research AssociateSchool of Public Health - Faculty of Medicine
- 47 Praed Street, St Mary's Campus, United Kingdom
BIO
I am a final year PhD student in the Department of Infectious Disease Epidemiology, supervised by Professor Neil Ferguson and Dr Ilaria Dorigatti.
Research
My research uses statistical and mathematical models to evaluate interventions against dengue and COVID-19.
Dengue Virus Serotype Prediction using Machine Learning:
As part of my PhD, I trained machine learning algorithms to predict the infecting dengue virus serotype using neutralising antibody titre data. These algorithms are useful tools in understanding dengue immune dynamics, infection history, and identifying serotype-specific vaccine correlates of protection.
Bayesian Survival Model for Dengue Vaccine Efficacy:
I have also developed a Bayesian survival model calibrated to published phase III trial data to estimate dengue vaccine efficacy by serotype, serostatus, and age over time. Incorporating an adapted correlate of protection model, first proposed by Khoury et al. (Nat Medicine, 2021), allows for a mechanistic link between antibody titre data and the risk ratio of disease. These efficacy estimates have been used to predict the population impact of routine vaccination across various scenarios, supporting policymakers, including WHO and GAVI.
SARS-CoV-2 Transmission Dynamics and Variant Impact:
In addition to dengue research, I developed a framework to infer transmission dynamics of co-circulating SARS-CoV-2 variants using epidemiological and genomic data. Using this model, I estimated the impact of antigen test target failure and testing scenarios on the transmission of variants. My findings highlight novel limitations of mass antigen testing and demonstrate the importance of maintaining molecular testing, not only for diagnostics but also for effective monitoring and surveillance efforts.
Education
Prior to my PhD, I studied Epidemiology (MSc) at Imperial. For my MSc thesis, I analysed the risk of yellow fever in Asia, supervised by Dr Natsuko Imai , Dr Katy Gaythorpe and Dr Ilaria Dorigatti. My undergraduate degree was in Immunology (BSc) at the University of Manchester.
Teaching
In parallel with my doctoral research, I work as a graduate teaching assistant. I develop and deliver educational materials for MSc modules, such as "Introduction to Statistical Thinking and Data Analysis" and "Outbreaks." Additionally, I have created a research computing exampler for PhD students, focusing on Bayesian inference for SARS-CoV-2 transmission modelling, using Stan.
Email
bethan.cracknell-daniels19@imperial.ac.uk
Research
My research uses statistical and mathematical models to evaluate interventions against dengue and COVID-19.
Dengue Virus Serotype Prediction using Machine Learning:
As part of my PhD, I trained machine learning algorithms to predict the infecting dengue virus serotype using neutralising antibody titre data. These algorithms are useful tools in understanding dengue immune dynamics, infection history, and identifying serotype-specific vaccine correlates of protection.
Bayesian Survival Model for Dengue Vaccine Efficacy:
I have also developed a Bayesian survival model calibrated to published phase III trial data to estimate dengue vaccine efficacy by serotype, serostatus, and age over time. Incorporating an adapted correlate of protection model, first proposed by Khoury et al. (Nat Medicine, 2021), allows for a mechanistic link between antibody titre data and the risk ratio of disease. These efficacy estimates have been used to predict the population impact of routine vaccination across various scenarios, supporting policymakers, including WHO and GAVI.
SARS-CoV-2 Transmission Dynamics and Variant Impact:
In addition to dengue research, I developed a framework to infer transmission dynamics of co-circulating SARS-CoV-2 variants using epidemiological and genomic data. Using this model, I estimated the impact of antigen test target failure and testing scenarios on the transmission of variants. My findings highlight novel limitations of mass antigen testing and demonstrate the importance of maintaining molecular testing, not only for diagnostics but also for effective monitoring and surveillance efforts.
Education
Prior to my PhD, I studied Epidemiology (MSc) at Imperial. For my MSc thesis, I analysed the risk of yellow fever in Asia, supervised by Dr Natsuko Imai , Dr Katy Gaythorpe and Dr Ilaria Dorigatti. My undergraduate degree was in Immunology (BSc) at the University of Manchester.
Teaching
In parallel with my doctoral research, I work as a graduate teaching assistant. I develop and deliver educational materials for MSc modules, such as "Introduction to Statistical Thinking and Data Analysis" and "Outbreaks." Additionally, I have created a research computing exampler for PhD students, focusing on Bayesian inference for SARS-CoV-2 transmission modelling, using Stan.
bethan.cracknell-daniels19@imperial.ac.uk
FACULTY
- Faculty of Medicine
POSITION NAME
- Honorary Research Associate