Long-Covid and long-term unattributed symptoms: a data science approach

Reference: SHLS20051

Following the first wave of the Covid-19 pandemic, a new syndrome emerged: Long-Covid – characterised by poor general health, fatigue and other symptoms that persisted long after remission from infection with the SARS-CoV-2 virus. Initial studies into Long-Covid have been hampered by poor case definition (e.g., no confirmed Covid-19 prior to Long-Covid) and there are suggestions that in some cases Long-Covid is essentially a ‘re-labelling’ of existing unattributed (i.e, not caused by diagnosed pathology) symptoms and health issues.

This studentship will take a data science approach to (1) estimate the incidence and course of Long-Covid in a population of confirmed Covid-19 patients, (2) identify risk factors for Long-Covid, explicitly including socio-economic factors, and (3) explore overlap of Long-Covid with pre-existing unattributed symptoms. 

Applicants should have an MSc in a relevant field (data science, epidemiology, statistics, health informatics),  and preferably experience with health-related data science projects 

How to Apply

This project is available as a 3 years full-time or 6 years part-time PhD study programme with expected start date of 1 October 2021

Candidates are encouraged to contact the research supervisors for the project before applying. 

For full-time study of this project, apply here

For part-time study of this project, apply here

Applicants shortlisted for the PhD project will be contacted for an interview within four weeks from the closing date.