TRACK-COVID: a population-based epidemiological investigation of COVID-19 virus infection

Study code
NBR72

Lead researcher
Professor Emanuele Di Angelantonio

Study type
Participant re-contact

Institution or company
University of Cambridge

Researcher type
Academic

Speciality area
Cross-cutting, COVID

Summary

Coronavirus induced disease (COVID-19) has now been declared a global pandemic by the World Health Organisation (WHO); efforts are in progress to understand its epidemiology and identify treatments and vaccines. As initial disease surveillance efforts in the UK have focused principally on patients with severe disease, however, there is an unmet need for complementary efforts that provide national data on the full spectrum of the disease, including the extent and fraction of mild or asymptomatic infections that do not require medical attention. 


Here we propose to help meet this need by conducting periodic surveys of approximately participants already consented into national blood donors research cohorts (e.g., STRIDES) who have agreed to be re-contacted for invitation to additional biomedical studies.  

The overall concept is to conduct a two-stage study to help monitor the evolution of immunity to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the new coronavirus, and to help define its key epidemiological and serologic characteristics across a wide sample of the population resident across the geographical breadth of England. 

During the first stage of this effort, individuals will be invited via email to participate, and will be sent a participant information booklet via web link. For participants who agree to participate, they will be asked to complete a consent form electronically, and be sent an online questionnaire on a monthly basis. During the second stage, participants expressing an interest in providing biological samples (e.g., finger-prick capillary whole blood sample) will be contacted and asked to provide self-collected samples on a monthly basis over a period of 12 months.  

The impact of this effort will immediate: it will, for example, directly inform public health modelling, feeding into the Scientific Advisory Group for Emergencies (SAGE) in order to help better control and understand the COVID-19 outbreak.