In this talk, Dr Violet Warwick and Dr Emmanuel Olawale Olamijuwon introduced CAID, a user-friendly, no-code web application designed to support the discovery of immune correlates of protection.
The CAID app enabled users to apply machine learning to feature selection, helping them identify immune attributes associated with an outcome of interest. This approach could accommodate large numbers of attributes, including both immune and sociodemographic factors.
The selected features could then be incorporated into classical regression models, including mixed models, to assess their statistical significance. Users could also visualise patterns through correlograms, boxplots and scatter plots, append related datasets, identify missing data and define subsets.
Designed to run on devices with limited computing power, CAID could be particularly valuable to researchers in low- and middle-income countries, while helping researchers worldwide accelerate the vaccine-development pipeline.
About the Speakers
Dr Violet Warwick is a Research Fellow at the University of St Andrews with a background in biochemistry and a PhD in drug design. She has extensive experience as a clinical trialist and has since gained additional qualifications in computing, enabling her to apply data science techniques to medical challenges. Her current research focuses on using machine learning to identify immune correlates of protection (CoP), which can serve as predictors of vaccine efficacy and inform decision-making in clinical development, licensing, and deployment.
Dr Emmanuel Olawale Olamijuwon is a Lecturer in the School of Geography and Sustainable Development at the University of St Andrews. He has over five years’ experience analysing clinical, survey, and digital data, and he is particularly interested in the development of digital platforms that harness computational approaches to address pressing global health challenges, including vaccine development and antimicrobial resistance. Emmanuel also leads workshops across Africa to build capacity in data analysis and visualisation.
