COVID-19 has created an unprecedented demand for trusted information. We – the Health Data Research UK Early Career Researcher Committee – are immensely proud to be part of a community that is delivering high quality, timely and transparent research (please see https://www.hdruk.ac.uk/covid-19/ for further detail of the Health Data Research UK response).
The winning publication
In this case study, we highlight our top picks of Health Data Research UK COVID-19 pre-publications, with our evaluation focusing on evidence of: 1) research quality, 2) team science, 3) scale, 4) open science, 5) patient and public involvement, and 6) equality, diversity and inclusion. In this month’s Publication of the Month competition, which has been dedicated to COVID-19-focussed papers, our top scoring publication was: “Rapid implementation of mobile technology for real-time epidemiology of COVID-19”. This mobile technology was developed by a research team spanning Harvard, King’s College London and the digital healthcare company Zoe Global Limited. The team includes HDR UK’s Professor Tim Spector, and was led by David Drew from Massachusetts General Hospital in the US – it brought together data scientists, epidemiologists and developers including the COronavirus Pandemic Epidemiology (COPE) consortium, to create and rapidly deploy a COVID-19 Symptom Tracker in the UK and United States.
The smartphone app is now used by over 2.6 million people and collects detailed data on daily symptoms, which can be used to estimate the burden of COVID-19 infection in the community. The app also asks participants to report information on geography, characteristics (such as underlying health conditions, being healthcare worker) clinical testing uptake and results, and illness progression and treatment. This information is vital for identifying infection hotspots and who is at greatest risk of severe infection. Critically, the capability for participants to consent for their data to be linked to other clinical trials and studies that they are enrolled in is built into the app, and is fully compliant with information governance requirements such as General Data Protection Regulation. The team have developed and published a toolkit, including the questionnaires staged on the app, thereby promoting open science at this critical time.
Early insights into COVID from using this app
We are already reaping the benefits of this work with Cristina Menni, Tim Spector and colleagues across King’s College London, the University of Nottingham and Zoe Global Limited using data from the app to examine the diagnostic value of loss of smell and taste as markers of laboratory-confirmed COVID-19 infection. The identification of patterns of symptoms such as loss of smell, which do not require bio-specimen testing is particularly important whilst there is a gap between testing demand and availability. This is particularly relevant to healthcare and other key workers in constant contact with the public who have not yet been tested for COVID-19.
Highly commended HDR UK publications
We also wish to highlight two other recent HDR UK publications which were highly commended this month by the Early Career Researcher Committee:
Age-dependent effects in transmission and control of COVID-19 epidemics
The ECR committee also wanted to highlight Davies et al’s open access pre-print on Age-dependent effects in transmission and control of COVID-19 epidemics and appreciate the effort of the research communities all around the world as well as our HDR UK researchers and fellows in producing valuable insights to inform decisions and measure the impact of current interventions.
Nicholas G. Davies led a valuable institutional collaboration from London School of Hygiene & Tropical Medicine, including Dr Rosalind Eggo, HDR UK (UKRI Innovation) fellow, at London School of Hygiene and Tropical Medicine (LSHTM), part of HDR UK London. This study unveiled some of the unknown facts about the role of age in transmission and severity of COVID-19. Davies et al have looked at possible reasons for lower number of reported COVID-19 cases in children compared to adults. They have addressed three important age-related scenarios which may account for lower case rate amongst children: i-decreased mixing between children after school closure, ii-lower susceptibility to infection in children iii-children are more likely to not show symptoms.
Using age-structured models for six countries, Davies et al. have shown that older adults are more likely to develop clinical symptoms with higher rate of infection considering raise of other
Estimating the infection and case fatality ratio for coronavirus disease (COVID-19) using age-adjusted data from the outbreak on the Diamond Princess cruise ship, February 2020
Roz Eggo was also involved in our second highly-commended study, led by Timothy Russell, also from, LSHTM – analysing the fatality ratio for coronavirus from data from the Diamond Princess cruise ship. The analysis demonstrated the importance of adjusting for delays from confirmation to outcome in real-time estimates of fatality risk, and the benefits of combining datasets alongside appropriate age adjustments to provide early insights into COVID-19 severity.
Many congratulations to the winning and highly commended teams for this month!
Open Access Publication of the Month – November 2019
27 November 2019
Using health data to understand risks of antibiotics in tuberculosis and after caesarean section
Open Access Publication of the Month – January 2020
30 January 2020
PhenoScanner v2: Improving the usability of human genomic data to improve health