Understanding the dynamics and drivers of the COVID-2019 epidemic using real-time outbreak analytics
Lead Research Organisation:
London School of Hygiene & Tropical Medicine
Department Name: UNLISTED
Abstract
Tracking and predicting an epidemic can help tailor public health measures so that we
respond quickly and appropriately to evolving threats. We will fit statistical models to
emerging data to monitor key epidemiological and clinical parameters related to
transmissibility and severity, and their likely impact on health services. Mathematical
models will be fitted to surveillance data to forecast future cases and hospitalisations and
evaluate the impact of different public health policies. Importantly, we will also measure
people’s behaviour over time to detect changes in response to the epidemic, assessing
what measures they take to reduce risk and where they get their evidence from. This
information will be used to refine our models and improve epidemiological forecasting. It
will also allow public health authorities to tailor their messaging to ensure that people
respond appropriately to changes in risk. The information that we generate, either from
our analyses of the emerging data, epidemic forecasts, or from our panel of respondents,
will be rapidly summarised and shared on easy-to-use websites so that scientists, health
workers and the public can be best informed about the threats posed by COVID-19 and
the efficacy of different measures to reduce risk.
respond quickly and appropriately to evolving threats. We will fit statistical models to
emerging data to monitor key epidemiological and clinical parameters related to
transmissibility and severity, and their likely impact on health services. Mathematical
models will be fitted to surveillance data to forecast future cases and hospitalisations and
evaluate the impact of different public health policies. Importantly, we will also measure
people’s behaviour over time to detect changes in response to the epidemic, assessing
what measures they take to reduce risk and where they get their evidence from. This
information will be used to refine our models and improve epidemiological forecasting. It
will also allow public health authorities to tailor their messaging to ensure that people
respond appropriately to changes in risk. The information that we generate, either from
our analyses of the emerging data, epidemic forecasts, or from our panel of respondents,
will be rapidly summarised and shared on easy-to-use websites so that scientists, health
workers and the public can be best informed about the threats posed by COVID-19 and
the efficacy of different measures to reduce risk.
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.
Efficient response to COVID-19 requires an understanding of the epidemiological and
behavioural drivers of disease transmission. Due to the rapidly evolving outbreak and the
mitigation strategies likely to be put in place at different times, analyses of epidemic
drivers and policy evaluation need constant updating to provide relevant data-driven
evidence to inform evolving public health choices.
We will provide rapid, continually updated estimates of key epidemiological features such
as disease severity and transmissibility measures and lengths of stay. Surveillance,
serological and sequence data (where available) will be analysed accounting for censoring
and reporting delays. Mathematical models will be fit to the emerging data streams using
Bayesian methods to provide regular forecasting updates and assess the impact of
current or potential future interventions. Contact and precautionary behaviours will be
monitored in a representative cohort along with information on risk awareness and
perceived efficacy of interventions to refine transmission models, improve forecasting, and
assess the effectiveness of social distancing measures.
Special attention will be given to sharing results in an open and timely manner.
Epidemiological parameter estimates and forecasts will be shared on a public website,
updated daily. User-friendly web interfaces will allow users to generate model outputs and
investigate the impact of specific model assumptions on different policy findings.
Highlights of essential results will be gathered in short policy briefs updated weekly.
Findings will immediately inform UK policy through participation on UK Government
advisory committees.
Efficient response to COVID-19 requires an understanding of the epidemiological and
behavioural drivers of disease transmission. Due to the rapidly evolving outbreak and the
mitigation strategies likely to be put in place at different times, analyses of epidemic
drivers and policy evaluation need constant updating to provide relevant data-driven
evidence to inform evolving public health choices.
We will provide rapid, continually updated estimates of key epidemiological features such
as disease severity and transmissibility measures and lengths of stay. Surveillance,
serological and sequence data (where available) will be analysed accounting for censoring
and reporting delays. Mathematical models will be fit to the emerging data streams using
Bayesian methods to provide regular forecasting updates and assess the impact of
current or potential future interventions. Contact and precautionary behaviours will be
monitored in a representative cohort along with information on risk awareness and
perceived efficacy of interventions to refine transmission models, improve forecasting, and
assess the effectiveness of social distancing measures.
Special attention will be given to sharing results in an open and timely manner.
Epidemiological parameter estimates and forecasts will be shared on a public website,
updated daily. User-friendly web interfaces will allow users to generate model outputs and
investigate the impact of specific model assumptions on different policy findings.
Highlights of essential results will be gathered in short policy briefs updated weekly.
Findings will immediately inform UK policy through participation on UK Government
advisory committees.
People |
ORCID iD |
John Edmunds (Principal Investigator) |
Publications
Yang B
(2022)
Accuracy for key parameters in modelling study.
in The Lancet. Public health
Davies NG
(2020)
Age-dependent effects in the transmission and control of COVID-19 epidemics.
in Nature medicine
Gibbs H
(2022)
Association between mobility, non-pharmaceutical interventions, and COVID-19 transmission in Ghana: A modelling study using mobile phone data.
in PLOS global public health
Gibbs H
(2021)
Association between mobility, non-pharmaceutical interventions, and COVID-19 transmission in Ghana: a modelling study using mobile phone data.
in medRxiv : the preprint server for health sciences
Davies NG
(2021)
Association of tiered restrictions and a second lockdown with COVID-19 deaths and hospital admissions in England: a modelling study.
in The Lancet. Infectious diseases
Gibbs H
(2023)
Call detail record aggregation methodology impacts infectious disease models informed by human mobility.
in PLoS computational biology
Grint DJ
(2021)
Case fatality risk of the SARS-CoV-2 variant of concern B.1.1.7 in England, 16 November to 5 February.
in Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin
Gibbs H
(2020)
Changing travel patterns in China during the early stages of the COVID-19 pandemic.
in Nature communications
Meakin S
(2022)
Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level.
in medRxiv : the preprint server for health sciences
Meakin S
(2022)
Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level.
in BMC medicine
Pearson CAB
(2021)
COVID-19 vaccination in Sindh Province, Pakistan: A modelling study of health impact and cost-effectiveness.
in PLoS medicine
Gibbs H
(2021)
Detecting behavioural changes in human movement to inform the spatial scale of interventions against COVID-19.
in PLoS computational biology
Liu Y
(2022)
Dosing interval strategies for two-dose COVID-19 vaccination in 13 middle-income countries of Europe: Health impact modelling and benefit-risk analysis.
in The Lancet regional health. Europe
Russell TW
(2021)
Effect of internationally imported cases on internal spread of COVID-19: a mathematical modelling study.
in The Lancet. Public health
Clifford S
(2023)
Effectiveness of BNT162b2 and ChAdOx1 against SARS-CoV-2 household transmission: a prospective cohort study in England
in Wellcome Open Research
Clifford S
(2023)
Effectiveness of BNT162b2 and ChAdOx1 against SARS-CoV-2 household transmission: a prospective cohort study in England
in Wellcome Open Research
Kucharski AJ
(2020)
Effectiveness of isolation, testing, contact tracing, and physical distancing on reducing transmission of SARS-CoV-2 in different settings: a mathematical modelling study.
in The Lancet. Infectious diseases
Davies NG
(2020)
Effects of non-pharmaceutical interventions on COVID-19 cases, deaths, and demand for hospital services in the UK: a modelling study.
in The Lancet. Public health
Davies NG
(2021)
Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England.
in Science (New York, N.Y.)
Hellewell J
(2021)
Estimating the effectiveness of routine asymptomatic PCR testing at different frequencies for the detection of SARS-CoV-2 infections.
in BMC medicine
Munday JD
(2021)
Estimating the impact of reopening schools on the reproduction number of SARS-CoV-2 in England, using weekly contact survey data.
in BMC medicine
Sherratt K
(2021)
Exploring surveillance data biases when estimating the reproduction number: with insights into subpopulation transmission of COVID-19 in England.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Abbas M
(2021)
Explosive Nosocomial Outbreak of SARS-CoV-2 in a Rehabilitation Clinic: The Limits of Genomics for Outbreak Reconstruction
in SSRN Electronic Journal
Abbas M
(2021)
Explosive nosocomial outbreak of SARS-CoV-2 in a rehabilitation clinic: the limits of genomics for outbreak reconstruction.
in The Journal of hospital infection
Bhaskaran K
(2021)
Factors associated with deaths due to COVID-19 versus other causes: population-based cohort analysis of UK primary care data and linked national death registrations within the OpenSAFELY platform.
in The Lancet regional health. Europe
Edmunds WJ
(2020)
Finding a path to reopen schools during the COVID-19 pandemic.
in The Lancet. Child & adolescent health
McCarthy CV
(2021)
Global and national estimates of the number of healthcare workers at high risk of SARS-CoV-2 infection.
in The Journal of hospital infection
Clark A
(2020)
Global, regional, and national estimates of the population at increased risk of severe COVID-19 due to underlying health conditions in 2020: a modelling study.
in The Lancet. Global health
Description | The work covered in this award has been critical in influencing UK and other policy decisions related to COVID-19. I do not have the time to list all of them, but I and various other members of the team are members of SPI-M (the modelling subgroup that feeds into SAGE). The work contained here has been presented at numerous SPI-M meetings over the last year. I am also a member of SAGE and much of the work contained here has directly informed SAGE. |
Geographic Reach | National |
Policy Influence Type | Implementation circular/rapid advice/letter to e.g. Ministry of Health |
Impact | As above, the work generated by this award has had a direct influence on SAGE (Strategic Advisory Group for Emergencies) through my presentation of the work there, or through SPI-M (the modelling subgroup of SAGE). I and/or my co-investigators present at SPI-M every week. |
URL | https://www.gov.uk/government/collections/scientific-evidence-supporting-the-government-response-to-... |
Description | Presentation and attendance of key COVID-19 scientific advisory bodies, including SAGE, SPI-M, and NERVTAG |
Form Of Engagement Activity | A formal working group, expert panel or dialogue |
Part Of Official Scheme? | No |
Geographic Reach | National |
Primary Audience | Professional Practitioners |
Results and Impact | There have been over 100 SAGE meetings, over 100 SPI-M meetings and over 60 NERVTAG meetings over the course of the pandemic. Professor Edmunds has attended almost all of these meetings, and has been joined at SPI-M by the other co-applicants on this grant. In doing this, the work conducted as a consquence of this grant has directly helped inform government policy over the 2 years of the pandemic. |
Year(s) Of Engagement Activity | 2020,2021,2022 |
URL | https://www.gov.uk/government/collections/scientific-evidence-supporting-the-government-response-to-... |