Virus Watch: Understanding community incidence, symptom profiles, and transmission of COVID-19 in relation to population movement and behaviour.
Lead Research Organisation:
University College London
Department Name: UNLISTED
Abstract
The novel strain of Coronavirus (COVID-19) has caused large numbers of deaths and
severe societal disruption in China and is predicted to become a global pandemic. To
prepare and respond we need to know how many people become infected, how many of
them become ill, what their symptoms are, how many seek health care, how commonly
they transmit to household contacts, what proportion need hospitalisation and what
proportion die. We need to understand how the population responds e.g. hand washing,
behaviours during and after coughing, sneezing or nose wiping, and whether people
restrict their movements and social contacts. Since many of those infected will have
relatively mild symptoms and not seek medical advice the only way to accurately obtain
this information is to conduct large scale community studies. We will follow up members of
the public and contacts of cases using regular online surveys of symptoms and
behaviours, secure tracking of participant movements, and testing for COVID-19 and other
respiratory infections to build a detailed picture of how the virus spreads and the
population responds. We will share this data with participants, health service and public
health planners and the general public to help minimise the impact of the virus.
severe societal disruption in China and is predicted to become a global pandemic. To
prepare and respond we need to know how many people become infected, how many of
them become ill, what their symptoms are, how many seek health care, how commonly
they transmit to household contacts, what proportion need hospitalisation and what
proportion die. We need to understand how the population responds e.g. hand washing,
behaviours during and after coughing, sneezing or nose wiping, and whether people
restrict their movements and social contacts. Since many of those infected will have
relatively mild symptoms and not seek medical advice the only way to accurately obtain
this information is to conduct large scale community studies. We will follow up members of
the public and contacts of cases using regular online surveys of symptoms and
behaviours, secure tracking of participant movements, and testing for COVID-19 and other
respiratory infections to build a detailed picture of how the virus spreads and the
population responds. We will share this data with participants, health service and public
health planners and the general public to help minimise the impact of the virus.
Technical Summary
This COVID-19 Rapid Response award is jointly funded (50:50) between the Medical Research Council and the National Institute for Health Research. The figure displayed is the total award amount of the two funders combined, with each partner contributing equally towards the project.
COVID-19 is set to become a global pandemic. Near real time information is needed to
inform NHS planning and public health response. This includes community incidence
(regardless of seeking care), risk factors for disease, clinical symptoms, case
hospitalisation and mortality ratios, asymptomatic viral shedding, asymptomatic infection,
household transmission risk and population behaviours during periods of wellness and
illness (including social contact and movement, respiratory hygiene and health seeking
behaviours). This information can only be gathered accurately through large scale
community studies.
Our experience of the MRC/Wellcome FluWatch study and ESRC BugWatch study allows
us to rapidly establish two six-month national household cohorts (April-September 2020)
and (October 2020-March 2021) of 25,000 individuals each (2,500 in each region of
England and Wales) for online symptom and behaviour reporting and optional use of an
app allowing their mobile phones to be used as GPS trackers enabling secure transfer
and analysis of detailed movement patterns. Two sub-cohorts (10,000 individuals each)
will also self-swab when ill for detection of COVID-19 and other circulating viruses. We will
also conduct a London based study (200 households) following household contacts of
COVID-19 cases for 28 days with daily symptom reporting, regular swabbing and baseline
and follow up serology to investigate household transmission and asymptomatic infection
and virus shedding. We will use data from Virus Watch to train whole population prediction
models based on social media and search engine data and develop spatiotemporal
models. Findings will be rapidly disseminated via online dashboards informing the public
and decision makers.
COVID-19 is set to become a global pandemic. Near real time information is needed to
inform NHS planning and public health response. This includes community incidence
(regardless of seeking care), risk factors for disease, clinical symptoms, case
hospitalisation and mortality ratios, asymptomatic viral shedding, asymptomatic infection,
household transmission risk and population behaviours during periods of wellness and
illness (including social contact and movement, respiratory hygiene and health seeking
behaviours). This information can only be gathered accurately through large scale
community studies.
Our experience of the MRC/Wellcome FluWatch study and ESRC BugWatch study allows
us to rapidly establish two six-month national household cohorts (April-September 2020)
and (October 2020-March 2021) of 25,000 individuals each (2,500 in each region of
England and Wales) for online symptom and behaviour reporting and optional use of an
app allowing their mobile phones to be used as GPS trackers enabling secure transfer
and analysis of detailed movement patterns. Two sub-cohorts (10,000 individuals each)
will also self-swab when ill for detection of COVID-19 and other circulating viruses. We will
also conduct a London based study (200 households) following household contacts of
COVID-19 cases for 28 days with daily symptom reporting, regular swabbing and baseline
and follow up serology to investigate household transmission and asymptomatic infection
and virus shedding. We will use data from Virus Watch to train whole population prediction
models based on social media and search engine data and develop spatiotemporal
models. Findings will be rapidly disseminated via online dashboards informing the public
and decision makers.
Organisations
People |
ORCID iD |
Andrew Hayward (Principal Investigator) |
Publications
Aldridge R
(2021)
Household overcrowding and risk of SARS-CoV-2: analysis of the Virus Watch prospective community cohort study in England and Wales
in Wellcome Open Research
Aldridge RW
(2022)
SARS-CoV-2 antibodies and breakthrough infections in the Virus Watch cohort.
in Nature communications
Beale S
(2022)
Workplace contact patterns in England during the COVID-19 pandemic: Analysis of the Virus Watch prospective cohort study
in The Lancet Regional Health - Europe
Beale S
(2022)
Deprivation and exposure to public activities during the COVID-19 pandemic in England and Wales.
in Journal of epidemiology and community health
Beale S
(2022)
Occupation, work-related contact and SARS-CoV-2 anti-nucleocapsid serological status: findings from the Virus Watch prospective cohort study
in Occupational and Environmental Medicine
Beale S
(2023)
Differential Risk of SARS-CoV-2 Infection by Occupation: Evidence from the Virus Watch prospective cohort study in England and Wales.
in Journal of occupational medicine and toxicology (London, England)
Budd J
(2020)
Digital technologies in the public-health response to COVID-19.
in Nature medicine
Description | Analysis of the risk of public activities on acquisition of respiratory tract infections contributing to UK COVID-19 lockdown and control measures |
Geographic Reach | National |
Policy Influence Type | Implementation circular/rapid advice/letter to e.g. Ministry of Health |
Impact | An analysis of Flu Watch data showed marked increases in risk of respiratory infection following public activities such as use of shops, cinemas, places of worship, restaurants, public transport. This was shared with the Chief Medical And Chief Scientific Officers prior to publication and supported national decisions to lockdown. It was also cited in lated SAGE documents on transmission informing continuation control measures and lifting of lockdown measures. |
Description | Contribution to national COVID strategy on coming out of lockdown and plan B measures |
Geographic Reach | National |
Policy Influence Type | Implementation circular/rapid advice/letter to e.g. Ministry of Health |
Impact | Delineation of the relative importance of different settings fed into plans for the road map out of lockdown allowing greater societal reopening in a staged way and proportionate reintroduction of measures during triggering of plan B when Omicron emerged. As such it has had a wide cross sector influence |
Description | Evidence of seasonality of coronavirus supporting winter planning |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Citation in other policy documents |
Impact | This analysis demonstrated a high degree of seasonality of coronavirus - it contributed to an Academy of Medical Science Winter planning report for COVID |
URL | https://www.gov.uk/government/publications/covid-19-preparing-for-a-challenging-winter-202021-7-july... |
Description | Impact on COVID-19 policy and practice |
Geographic Reach | Multiple continents/international |
Policy Influence Type | Membership of a guideline committee |
Impact | Data on symptom profiles was critical to operation of Test and Trace - a cornerstone of control Data on non household transmission helped design route out of lockdown and prioritise measures for plan B allowing proportionate response with impact across all societal sectors Data informing booster programme made UK amongst first in world to introduce booster doses providing significant protection against waning immunity over the winter 2021/22 and allowing less restrictive measures - many countries followed suite. |
Description | Influence on testing capacity for national test and trace programme |
Geographic Reach | National |
Policy Influence Type | Citation in other policy documents |
Impact | We used data from Flu Watch and Bug Watch to predict the likely capacity requirements for NHS Test and Trace in summer and winter periods based on the frequency of symptoms. These estimates were included in an Academy of Medical Sciences report on preparing for the winter and communicated directly to Test and Trace |