PhD project in collaboration with industry

School of Engineering, Warwick Medical School and Siemens.

Start date: Oct 2023

Predicting long COVID complications via the analysis and modelling of dynamic biomarker trajectories

One in 32 people in the UK has some form of long Covid with symptoms adversely affecting their daily activities. Fatigue, deteriorating lung function and neurological manifestations are some of the most common symptoms. COVID-19 survivors, even after 2 years post-infection, exhibit problems with fatigue, muscle weakness, sleep difficulties, dizziness, headache, endocrine and metabolic disturbance, lung fibrosis and myalgia. There is also increasing evidence of brain-related injuries in patients with COVID-19. Even mild COVID-19 disease is linked to brain damage, which might provide insight into neurological or cognitive symptoms such as difficulty concentrating, trouble sleeping, memory loss, or confusion associated with long COVID. There is an urgent need for more research into refining clinical pathways and more targeted clinical service provision. Many studies focus on the patient's previous history and responses during the acute infection stage to uncover clues that can be used to predict duration and severity. Increasingly, studies use big data to determine the prevalence, symptoms, or risk factors for long COVID and provide insights into the characteristics of patients with long COVID and classifications of distinct groupings of symptoms reported by patients. Data science approaches and artificial intelligence offer the promise of innovative tools to support clinical teams in the management of COVID patients.

The supervisory team involves clinical academics from the University of Warwick and diagnostic industry professionals from Siemens Healthineers. This PhD project aims to analyse longitudinal real-world clinical and laboratory data from UHCW NHS Trust to help predict the likelihood of long COVID disease progression to severe disease endpoints. The availability of an extensive biobank of samples from COVID patients will allow investigations of biomarkers as novel classifiers of disease progression. Collaboration with the Siemens team that developed the Atellica® COVID-19 Severity Algorithm offers the opportunity to exploit the potential of artificial intelligence in delineating disease trajectories and phenotypes of long COVID-19 patients and explore possible novel uses of emerging biomarkers.

Please get in touch with Dr Khovanova at n.khovanova@warwick.ac.uk if you are interested in this project. This project will be funded jointly with industry, and to secure funding, the candidates will need to apply for the School of Engineering Scholarship with the deadline on the 31^st of March 2023 (applicants must be eligible for home fees): https://warwick.ac.uk/fac/sci/eng/postgraduate/funding/pss