B4077 - Using metabolomic data statistics and machine learning to predict severe COVID-19 and long COVID - 30/05/2022

B number: 
Principal applicant name: 
Milla Kibble | University of Bristol (United Kingdom)
Dr Francisco Perez-Reche, Dr Joshua Bell, Prof. Nicholas Timpson
Title of project: 
Using metabolomic data, statistics and machine learning to predict severe COVID-19 and long COVID
Proposal summary: 

The central idea of the proposed research is to use metabolic biomarkers to predict the severity of COVID-19 and the likelihood of long COVID for individuals that have not necessarily been diagnosed with a pre-existing health condition. To this end, we will use pre-pandemic data from several cohort studies which, in addition to basic information on age, sex, ethnicity, etc, contain hundreds of metabolic biomarkers for thousands of individuals. To understand the link between these characteristics and the impact of COVID-19, we will use symptoms data for those individuals in the cohort studies that had COVID-19. The data will be analysed with statistical methods to identify associations between the characteristics of individuals before the pandemic and the severity of the disease. This analysis will be complemented with computer programs developed to predict if the infection of an individual will have serious effects based on his/her characteristics before the pandemic. Machine learning techniques will be used to train computer programs to automatically recognise metabolic features that represent a risk for severe COVID-19.

Impact of research: 
Date proposal received: 
Tuesday, 24 May, 2022
Date proposal approved: 
Monday, 30 May, 2022
metabolomics, Infection, Computer simulations/modelling/algorithms, Metabolic - metabolism