Recording and analysing data on the neurological consequences of Long Covid using a web app

Long COVID is a clinical syndrome characterised by the presence of certain symptoms related to SARS-CoV-2 infection, which occur or persist for weeks or months after recovery from the acute infection. The clinical picture may vary from patient to patient and the symptoms experienced are not always immediately traced back to the previous infection. This study deals with the measurement, management and analysis of data on neurological consequences in individuals with long COVID, within the 'Happy Again' study (https://www.essex.ac.uk/research-projects/happy-again), a project developed by the University of Essex. Within the 'Happy Again' platform (https://happyagain.essex.ac.uk/), participants can take part in a series of cognitive assessments. These assessments take place using questionnaires and a battery of sensory and cognitive tests, all designed to record behavioural markers that correlate with possible neuropsychological deficits. The results of these tests, together with the answers to the questionnaires, are systematically stored in the database for subsequent analysis. My role initially consisted of ensuring the integrity of the database in the face of, for example, computer intrusion attempts or internet connection interruptions that could cause duplicate tests. However, the heart of this thesis was the systematic analysis of the data, with a focus on the questionnaires and one of the tests, the word categorisation test. I was also involved in synthesising new Long Covid severity indices by means of factor analysis. My thesis consists of a comprehensive account of the work that led to the creation of the 'Happy Again' web application, detailing the evolution of the system's development, architectural choices and implementation strategies, highlighting the changes introduced, the data analysis, the implementation methods employed and the results obtained.