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
Abbas K
(2020)
Routine childhood immunisation during the COVID-19 pandemic in Africa: a benefit-risk analysis of health benefits versus excess risk of SARS-CoV-2 infection.
in The Lancet. Global health
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
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
Barnard R
(2022)
Modelling the medium-term dynamics of SARS-CoV-2 transmission in England in the Omicron era
in Nature Communications
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
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
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
(2021)
Strategies to reduce the risk of SARS-CoV-2 importation from international travellers: modelling estimations for the United Kingdom, July 2020.
in Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin
Clifford S
(2023)
Effectiveness of BNT162b2 and ChAdOx1 against SARS-CoV-2 household transmission: a prospective cohort study in England
in Wellcome Open Research
Davies NG
(2020)
Age-dependent effects in the transmission and control of COVID-19 epidemics.
in Nature medicine
Davies NG
(2021)
Increased mortality in community-tested cases of SARS-CoV-2 lineage B.1.1.7.
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
Davies NG
(2021)
Estimated transmissibility and impact of SARS-CoV-2 lineage B.1.1.7 in England.
in Science (New York, N.Y.)
| 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-... |
| Title | CoMix - Age structured contact matrices for 9 key periods of the COVID-19 epidemic in England |
| Description | Contact matrices from 9 distinct periods of the UK COVID-19 epidemic: Lockdown 1 = 23rd March - 3rd June 2020 Lockdown 1 easing = 4th June - 29th July 2020 Reduced restrictions = 30th July - 3rd Sep 2020 Schools open = 4th Sept - 26th October 2020 Lockdown 2 = 5th November - 2nd December 2020 Lockdown 2 easing = 3rd December - 19th December 2020 Christmas = 20 December 2020 - 2nd January 2021 Lockdown 3 = 5th January - 8th March 2021 Lockdown 3 with schools open = 8th March - 16th March 2021 1. The file: contact_matrices_9_periods.csv contains the mean contact matrices. 2. The nine 'qs' files for the individual periods contain 1000 bootstrap samples of the contact matrix for the relevant period. each column is a different sample. The age-groups are not explicitly detailed, but follow the same order as in the contact_matrices_9_periods.csv file. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://zenodo.org/record/4677017 |
| Title | SARS-CoV-2 antibodies protect against reinfection for at least 6 months in a multicentre seroepidemiological workplace cohort |
| Description | To identify the potential for SARS-CoV-2 reinfection |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://www.immport.org/shared/study/SDY2035 |
| Title | Effectiveness of BNT162b2 and ChAdOx1 against SARS-CoV-2 household transmission |
| Description | Code (no data) used for Effectiveness of BNT162b2 and ChAdOx1 against SARS-CoV-2 household transmission: a prospective cohort study in England |
| Type Of Technology | Software |
| Year Produced | 2023 |
| Open Source License? | Yes |
| URL | https://zenodo.org/record/7618847 |
| 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-... |