Paul McKeigue (Affiliate)
Methods for molecular and genetic epidemiology, with applications in clinical prediction and personalised medicine
Research in a Nutshell
My research focuses on methods for molecular and genetic epidemiology, with applications in clinical prediction and personalised medicine. These methods make use of Bayesian and computationally-intensive statistical methods, and machine learning methods for constructing predictors. I work closely with Helen Colhoun’s research group at the Centre for Genomic and Experimental Medicine. This collaboration includes the development of an analysis platform based on de-identified electronic health records and the use of this platform to study drug safety and complications of diabetes.
Since April 2020 most of my time has been spent within the Epidemiology Research Cell of Public Health Scotland’s COVID-19 Health Protection Response. In this role I have led the design and analysis of REACT-SCOT, a rolling case-control study of all cases of COVID-19 in Scotland since the start of the epidemic with up to ten controls per case, matched for age, sex and general practice and linked to electronic health records. This study was used initially to construct and validate a risk score for severe or fatal COVID-19, and to investigate the relation of severe COVID-19 to drug prescribing. In subsequent work the study was extended to study risk in health care workers and teachers, risk to people designated people as clinically extremely vulnerable and thus eligible for shielding, and nosocomial transmission. More recently we have studied the efficacy of vaccination in those designated as clinically extremely vulnerable, and the safety of vaccination, focusing on the risk of cerebral venous thrombosis.
Figure from research group homepage
Public Health Scotland
Molecular and genetic epidemiology, clinical prediction, personalised medicine, vaccination efficacy
Statistical modelling, machine learning methods