Olivia Adair (She/Her)

Current research project

Employing Data Analytics to inform the development of innovative Cost-Effectiveness Cancer Screening Models in the COVID-19 era

Cancer Screening is a critical component of our armamentarium against cancer, facilitating identification of citizens at risk of developing the disease at the earliest stage, thus informing effective health management of the newly identified patient when cancer treatment may be more effective, avoiding significant ill health and in some cases premature death. However, cost-effectiveness analysis (CEA) must be included in any screening model, to ensure the best use of often-limited resources and deliver sustainable solutions. Striving for optimal health resource utilisation is particularly relevant in the context of the coronavirus pandemic, with COVID-19-repurposing of our health service leading to unintended de-prioritisation of non-COVID-related healthcare activities, including cancer screening. Developing robust and resilient COVID-era cancer screening programme configurations requires new, more precise data-enabled modelling approaches, which will have relevance locally, nationally and globally. This project aims to develop such a model to be potentially used for the NI Bowel Cancer Screening program, with the help of the Public Health Agency.

Biography

I started my undergraduate degree in Mathematics at QUB. Throughout, I took a shine to Statistics and changed my degree to an MSci in Mathematics and Statistics & Operational Research. I learnt a lot of my research skills from my undergrad dissertation which look at evaluating the differences between White-box and Black-box classification methods. In July 2021, I graduated with a First Class Honours degree and started my PhD in October 2021.

Research interests

• Decision Modelling Cost-effectiveness
• Analysis Data Mining

Pure page

https://pure.qub.ac.uk/en/persons/olivia-adair