COVID-19 Modelling Consortium: quantitative epidemiological predictions in response to an evolving pandemic
Lead Research Organisation:
University of Warwick
Department Name: Mathematics
Abstract
Since the beginning of the COVID-19 pandemic in early 2020, mathematical and statistical modelling have been used to provide estimates of the epidemic in the UK, and to make short- and long-term predictions about the impact of interventions. The teams of epidemiological modellers and statisticians in our JUNIPER (Joint UNIversity Pandemic Epidemiological Research) consortium represent a core of committed and experienced university research groups that have dedicated themselves since February 2020 to generating predictions, forecasts and insights. These findings feed directly into the Scientific Pandemic Influenza Group on Modelling (SPI-M) and the Scientific Advisory Group for Emergencies (SAGE), both of whom advise the UK government on scientific matters relating to the UK's response to the pandemic. As part of SPI-M this group has brought together a range of analyses to underpin diverse policy decisions including early estimates of the scale of an uncontrolled epidemic, reasonable worst-case scenarios and the impact of reopening schools.
Moving forward, critical research gaps remain unaddressed, and further translational work must be conducted to generate the necessary insights. The requested funding will ensure these key groups, with their extensive experience of delivering science for policy and deep understanding of this outbreak, will be able to continue and expand their activities. The Juniper consortium members will continue to respond to rapid requests from the UK government via SPI-M and SAGE, including providing weekly forecasts of the reproductive number R and growth rate in the UK and predictions of the likely impact of policy decisions and interventions. The research teams will be flexible and adaptive to the changing phases of the epidemic, and will proactively consider novel methodology, analysis or modelling that is required, as well as horizon scan the impact of new scientific findings and how this will impact on current and future modelling.
The programme of work will address a core set of eight overarching questions that the consortium has identified as being important over the next 12-18 months: 1. How to best address issues around the storage, curation, and processing of the growing number of COVID-related data streams 2. Improving statistical and computational fundamentals for outbreaks 3. Refining methodology for the detection of hotspots or regions in need of greater control 4. Developing bespoke methods to analyse and model Surveillance, Test and Trace 5. Refining methodologies to determine risks posed by structured environments such as workplaces, care homes, hospitals, schools, universities 6. Producing realistic individual-scale modelling of contemporary social interactions 7. Implication of finer-scale individual-level characteristics and impacts of short- and long-term immunity in models. 8. Detailed retrospective analysis of the first wave.
Our consortium will embed these scientific activities within an open and collaborative framework, including considerable public outreach so that scientific assumptions and findings are effectively communicated. Our consortium will be outward-facing and inclusive, helping to add value to a range of existing and new COVID-19 activities. We aim to build national capacity and the proposed programme will also contribute to training the next generation of applied epidemiological modellers.
Moving forward, critical research gaps remain unaddressed, and further translational work must be conducted to generate the necessary insights. The requested funding will ensure these key groups, with their extensive experience of delivering science for policy and deep understanding of this outbreak, will be able to continue and expand their activities. The Juniper consortium members will continue to respond to rapid requests from the UK government via SPI-M and SAGE, including providing weekly forecasts of the reproductive number R and growth rate in the UK and predictions of the likely impact of policy decisions and interventions. The research teams will be flexible and adaptive to the changing phases of the epidemic, and will proactively consider novel methodology, analysis or modelling that is required, as well as horizon scan the impact of new scientific findings and how this will impact on current and future modelling.
The programme of work will address a core set of eight overarching questions that the consortium has identified as being important over the next 12-18 months: 1. How to best address issues around the storage, curation, and processing of the growing number of COVID-related data streams 2. Improving statistical and computational fundamentals for outbreaks 3. Refining methodology for the detection of hotspots or regions in need of greater control 4. Developing bespoke methods to analyse and model Surveillance, Test and Trace 5. Refining methodologies to determine risks posed by structured environments such as workplaces, care homes, hospitals, schools, universities 6. Producing realistic individual-scale modelling of contemporary social interactions 7. Implication of finer-scale individual-level characteristics and impacts of short- and long-term immunity in models. 8. Detailed retrospective analysis of the first wave.
Our consortium will embed these scientific activities within an open and collaborative framework, including considerable public outreach so that scientific assumptions and findings are effectively communicated. Our consortium will be outward-facing and inclusive, helping to add value to a range of existing and new COVID-19 activities. We aim to build national capacity and the proposed programme will also contribute to training the next generation of applied epidemiological modellers.
Technical Summary
Mathematical and statistical modelling has been hugely influential providing rigorous estimates of the COVID-19 epidemic in the UK and making short-term and long-term predictions for decisions on interventions.
We are leaving the first phase of this epidemic, cases are slowly declining but there are local outbreaks and variation between regions is of increasing importance. Although standard epidemiological modelling tools have worked well so far, a suite of new tools are now needed that can deal with spatially- and socially-structured stochastic dynamics and population heterogeneities.
The teams of epidemiological modellers and statisticians in this consortium represent a core of committed and experienced research groups that have dedicated the last six months to generating predictions, forecasts and insights feeding into SPI-M and SAGE. To tackle the challenges of the next 18 months, these teams require investment in staff time and personnel. The proposed consortium will support these established and collaborating research teams, build national capacity and help train the next generation of applied epidemiological modellers.
We have developed a core set of eight overarching questions that we feel underpin the future challenges that will need to be addressed by SPI-M, SAGE & JBC:
1. Data collation, processing and analysis
2. Statistical and computational fundamentals for outbreaks
3. Detection of hotspots or regions in need of greater control
4. Surveillance, Test and Trace
5. Structured environments (workplaces, care homes, hospitals, schools, universities) 6. Realistic individual-scale modelling of contemporary social interactions
7. Implications of finer-scale individual-level characteristics
8. Detailed retrospective analysis of the first wave
We are leaving the first phase of this epidemic, cases are slowly declining but there are local outbreaks and variation between regions is of increasing importance. Although standard epidemiological modelling tools have worked well so far, a suite of new tools are now needed that can deal with spatially- and socially-structured stochastic dynamics and population heterogeneities.
The teams of epidemiological modellers and statisticians in this consortium represent a core of committed and experienced research groups that have dedicated the last six months to generating predictions, forecasts and insights feeding into SPI-M and SAGE. To tackle the challenges of the next 18 months, these teams require investment in staff time and personnel. The proposed consortium will support these established and collaborating research teams, build national capacity and help train the next generation of applied epidemiological modellers.
We have developed a core set of eight overarching questions that we feel underpin the future challenges that will need to be addressed by SPI-M, SAGE & JBC:
1. Data collation, processing and analysis
2. Statistical and computational fundamentals for outbreaks
3. Detection of hotspots or regions in need of greater control
4. Surveillance, Test and Trace
5. Structured environments (workplaces, care homes, hospitals, schools, universities) 6. Realistic individual-scale modelling of contemporary social interactions
7. Implications of finer-scale individual-level characteristics
8. Detailed retrospective analysis of the first wave
Organisations
- University of Warwick (Lead Research Organisation)
- EPSRC (Co-funder)
- OFFICE FOR NATIONAL STATISTICS (Collaboration)
- Isaac Newton Institute for Mathematical Sciences (Collaboration)
- Health and Safety Executive (HSE) (Collaboration)
- MANCHESTER UNIVERSITY NHS FOUNDATION TRUST (Collaboration)
- Royal Statistical Society (Collaboration)
- NHS ENGLAND (Collaboration)
- UNIVERSITY OF CAMBRIDGE (Collaboration)
- UNIVERSITY OF EXETER (Collaboration)
Publications
Ahmad S
(2021)
Early signals of Omicron severity in sentinel UK hospitals
Aylett-Bullock J
(2022)
Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward.
in BMJ global health
Ben S. Cooper
(2023)
The burden and dynamics of hospital-acquired SARS-CoV-2 in England
Ben S. Cooper
(2023)
The burden and dynamics of hospital-acquired SARS-CoV-2 in England
| Description | The JUNIPER award has been transformative in terms of both the fundamental science and its interface with policy. Members of the JUNIPER consortium have been at the forefront of UK researchers in the major event during the COVID-19 outbreak and have been instrumental in the science-policy interface. Specifically: 1) The majority of JUNIPER investigators (and some PDRAs) have been part of the expanded SPI-M-O (Scientific Pandemic Influenza Modelling - Operational) which has provided a stream of quantitative assessments and projections during the pandemic. Gog and Hall have regularly attended SAGE meetings, with other members attending to present individual packages of work; members of JUNIPER also chair or are part of other advisory groups, including SAGE-subgroups (Social Care, Environmental Modelling, Hospital Onset COVID-19, Regional Variation, International Vaccination, Children, Schools and Higher Education), the Vaccine Effectiveness Expert Panel, PHE's Variant Technical Group and JCVI. Three of our team were awarded OBEs for services to the COVID-19 response (Brooks Pollock, Gog and Keeling). 2) JUNIPER statistical analysis and modelling has been key to understanding the behaviour and potential risks of each new variant of concern (Alpha, Delta and Omicron) - members of JUNIPER are part of PHE's variant subgroup. 3) JUNIPER has been one of three groups that has provided input into the six roadmaps to relax controls throughout 2021 - providing six major documents to SAGE (Scientific Advisory Group on Emergencies) 4) JUNIPER has been at the forefront of modelling the interaction between NPI controls and vaccination - members of JUNIPER are part of JCVI (Joint Committee on Vaccination and Immunisation) and SAGE's Vaccine Effectiveness Expert Panel. 5) Every week JUNIPER members have provided several of the independent estimates of the reproductive number R, growth rate in the UK and predictions of the effect on the epidemic of given interventions, such as vaccination and relaxation of social distancing measures. |
| Exploitation Route | There is the huge potential to take this work forwards. This pandemic is not over yet and the UK is likely to be challenged be new variants, waning vaccine protection and a decrease in data quality over the next year(s) - all of which necessitates the use of the statistical analysis, mathematical models and epidemiological knowledge that the JUNIPER consortium has generated over the last 18 months. There is also the potential to extend the methods and insights developed by this consortium to other countries and other pathogens. The last 18 months has revolutionised our understanding of epidemiological analysis and projection, and these ideas have a plethera of applications beyond COVID-19. |
| Sectors | Healthcare |
| URL | https://maths.org/juniper/ |
| Description | The JUNIPER consortium was established to address the multiple quantitative questions that arose during the COVID-19 pandemic, feeding into SPI-MO and SAGE and through these to government planning and policy. Since its creation in November 2020, JUNIPER has made: weekly contributions to the estimation of R (four estimates from different institutions); weekly contributions to Medium Term Forecasts (three projections from different institutions); weekly contributions to the Regional Variation subgroup (two estimates from different institutions); has generated the majority of reports to weekly SPI-MO group meetings; and generated (at least) 25 reports for SAGE (Scientific Advisory Group for Emergencies) often presenting the work. |
| First Year Of Impact | 2020 |
| Sector | Healthcare |
| Impact Types | Societal Policy & public services |
| Description | Advice to JBC and PHE around Christmas 2020 and lockdown 3 |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Advice supported heavy reduction in mixing around Christmas 2020 and ultimately contributed to triggering the lockdown in January 2021 |
| Description | Chairing and contributions to the SPI-M subgroup on Regional Variation |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Early sight of the finer scale dynamics of this unfolding pandemic has been essential to SPI-M's contributions to scientific advice to government. This subgroup of SPI-M has regularly been the first group to look in detail at early dynamics of key moments of this pandemic, including the establishment of new variants of concern. |
| Description | Early recommendations for effective contact tracing from the INI Infectious Dynamics of Pandemics programme |
| Geographic Reach | National |
| Policy Influence Type | Contribution to new or Improved professional practice |
| Impact | Shaped understanding of effective implementation of contact tracing |
| URL | http://www.newton.ac.uk/files/preprints/ni20001.pdf |
| Description | Limited impact of age-based NPIs in response to Omicron wave |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Age-based lockdown was not implemented |
| URL | https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/1044... |
| Description | Membership of the Scientific Pandemic Influenza Modelling Group, a subgroup of SAGE, during the COVID-19 pandemic. |
| Geographic Reach | National |
| Policy Influence Type | Membership of a guideline committee |
| Impact | PI TIldesley, co-I Dyson, co-I Keeling and PDRA Hill are members of SPI-M, the modelling subgroup of SAGE during the COVID-19 pandemic. In this capacity, they have produced multiple modelling documents affiliated to the work carried out on this grant that have been presented at SPI-M, several of which have gone on to SAGE. These have included calculations of the weekly R number, medium and long term epidemic forecasts, predictions of the impact of school re-openings and circuit breaker lockdowns, strategies for students returning to university campuses and strategies for deployment of vaccination. There have been a significant number of SAGE papers in the last 9 months that have included work that has specifically come directly from the work on this grant. A list of papers published by SAGE is given in the URL below, several of which have been produced by the Warwick team. |
| URL | https://www.gov.uk/government/organisations/scientific-advisory-group-for-emergencies |
| Description | Modelling support to Her Majesty's Prison and Probation Services |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | 1) Improved understanding of high degree of heterogeneity in risk of COVID-19 introduction across prisons, supporting the introduction of a national four-level classification system, and the development of a national framework to determine prison regimes and COVID-secure operational delivery throughout the pandemic. 2) Shaping of the practical implementation of the "reverse cohort unit" (quarantine at entry), regarding duration, timing of testing at entry and release, and risks associated. 3) Better understanding of the risk of introduction associated with staff, why this is difficult to mitigate, and how to reduce it via compartmentalisation strategies, optimal use of new technologies and synchronisation of testing with work shifts. Understanding has been integral to developing a Testing strategy across the Ministry of Justice, with conclusions translated into social care settings and, in particular, care homes. 4) Informed response to the Omicron wave, in particular quantifying the costs of relaxation of interventions given evidence of reduced severity compared to previous variants. In summary, this work helped HMPPS make informed decisions concerning when, where and how to intervene in monitoring and controlling COVID-19 transmission in prisons, thus making efforts more targeted and cost-effective, and equally minimising the risk of outbreaks, ultimately saving lives. |
| URL | https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/9798... |
| Description | National and international analysis of COVID-19 cases and deaths |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | UK Public Health situational awareness reports, instrumental to guide the COVID-19 pandemic response. |
| Description | Projected impact of school reopening in March 2021 |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Projections of epidemic dynamics following school reopening in March 2021, as a first step in the Roadmap out of the third lockdown in the UK has allowed the UK Government to improve planning of resources and gauge the trade-offs between societal costs of health and education. |
| Description | R estimates and medium-term projections |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | The reproduction number (R) and the epidemic growth rate are commonly used metrics for monitoring the spread of an infection and the impact of control measures. During the COVID-19 pandemic in the UK, estimates of R and growth rates have been generated by combining around 15 estimates (the precise number of estimates varies week-by-week) from different SPI-M groups's models. Medium Term Projections are a useful planning tool across government, translating recent behaviour into projections for the next 4-6 weeks. |
| URL | https://www.gov.uk/guidance/the-r-value-and-growth-rate |
| Description | SAGE contributions and participation |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Contributions from SAGE have been highly significant in shaping the UK's response the COVID-19 pandemic. Our work has contributed to decisions about national and local lockdowns, healthcare provision planning, vaccination strategy, travel restrictions, university educational provision, school reopening, and care home provision and response. |
| URL | https://www.gov.uk/government/organisations/scientific-advisory-group-for-emergencies |
| Description | SPI-M scenarios at times of major policy changes |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Generating scenarios under a range of assumptions is one of the key outputs only models can produce. Such scenarios have been essential to gauge worst-case numbers of admissions and hospital beds occupied and retrospectively compare realised data with past scenarios to assess the quality of initial guesses on policy impact and informal estimation of changes in transmission following control implementation or relaxation |
| Description | Social Care Working Group (SCWG) subgroup of SAGE |
| Geographic Reach | National |
| Policy Influence Type | Implementation circular/rapid advice/letter to e.g. Ministry of Health |
| Impact | COVID19 Policy on care home shielding, testing and subsequent interventions were informed by the outputs of the working group. |
| Description | Surge vaccination in Delta hotspots |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Surge vaccination in Delta hotspots (e.g. Bolton) was never advertised but de-facto implemented. |
| Description | Use of LFTs for border control and contact tracing with Omicron |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Work heavily influenced the policy change from self-isolation to a 7-day course of LFTs when traced as contact of an Omicron case. Established discussions with the border control policy makers resulted in a request to work together to generate a protocol for automatic ramp-up of interventions given specific criteria are met in the case of arrival of future variants of concern |
| Description | Vaccine priority Phase 1 and 2. |
| Geographic Reach | National |
| Policy Influence Type | Participation in a guidance/advisory committee |
| Impact | Work from the JUNIPER consortium was pivotal in providing quantitative information on the likely impact of vaccination, and the ordering of priority groups for both Phase 1 and Phase 2 of the UK vaccine role out. This was directly presented to the Joint Committee on Vaccination and Immunisation (of which Keeling is a member). Our initial work clearly showed that an age-prioritised ordering had considerable advantages over other schemes in terms of reducing mortality and morbidity over short time scales. Our second piece of work considering the optimal timing of vaccinating the Phase 1 group, and helped to support the recommendation that there should be a 12-week interval between first and second doses. Our third piece of work considered the optimisation of Phase 2, and showed relatively little difference between any ordering of groups - speed and hence simplicity was key; this helped to support the roll-out of Phase 2 in age-order. Work in 2021/2022 has provided quantiative projections to JCVI on booster vaccination, helping to underpin the decision to implement booster (in 2021) and second boosters (in 2022), and to underpin the vaccination of 5-11 and 12-17 year olds. |
| Description | An analytical framework for Test, Trace and Isolate in the UK: optimising and targeting deployment alongside other measures. |
| Amount | £416,025 (GBP) |
| Funding ID | MR/V028618/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2020 |
| End | 08/2021 |
| Description | Gig workers: unsung heroes and a strategic role in the UK national response to the COVID-19 pandemic |
| Amount | £100,000 (GBP) |
| Funding ID | MC_PC_19083 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 03/2020 |
| End | 10/2021 |
| Description | JUNIPER Partnership |
| Amount | £1,329,372 (GBP) |
| Funding ID | MR/X018598/1 |
| Organisation | Medical Research Council (MRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2023 |
| End | 04/2028 |
| Description | OPerational research for Emergency Response and strategic planning Analysis (OPERA) |
| Amount | £373,826 (GBP) |
| Funding ID | PR-R17-0916-21001 |
| Organisation | National Institute for Health Research |
| Sector | Public |
| Country | United Kingdom |
| Start | 04/2018 |
| End | 03/2022 |
| Title | Case data with referenced sources for cities within China from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Case_data_with_referenced_sources_for_cities_within_China_f... |
| Title | Case data with referenced sources for cities within China from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Case_data_with_referenced_sources_for_cities_within_China_f... |
| Title | Case data with referenced sources for other countries/regions from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Case_data_with_referenced_sources_for_other_countries_regio... |
| Title | Case data with referenced sources for other countries/regions from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Case_data_with_referenced_sources_for_other_countries_regio... |
| Title | CoCoNet manuscript data |
| Description | CoCoNet survey data for the manuscript: ' Social mixing patterns in the UK following the relaxation of COVID-19 pandemic restrictions: a cross-sectional online survey'Description |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | None to report |
| URL | http://www.research.lancs.ac.uk/portal/en/datasets/coconet-manuscript-data(52d69555-0092-4757-808b-9... |
| Title | Estimation of Length of Stay from hospital line-list data |
| Description | Statistical method to estimate Length of Stay, and if data is complete also probability of outcome, from data routinely collected by each hospital |
| Type Of Material | Data analysis technique |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | Correction of early estimates of hospital Length of Stay, which helped the NHS to plan capacity better. Method currently deployed to all Hospital Trusts in the North West of England |
| URL | https://bmcinfectdis.biomedcentral.com/articles/10.1186/s12879-021-06371-6 |
| Title | Estimation of the impact of individual characteristics on transmission from household-stratified epidemic data |
| Description | Cutting-edge method used throughout the COVID-19 pandemic to extract information about the impact of individual characteristics on susceptibility and infectivity from household-stratified epidemic data, in particular the ONS COVID-19 Infection Survey |
| Type Of Material | Data analysis technique |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | Estimation of the impact of individual characteristics on transmission from household-stratified epidemic data |
| URL | https://arxiv.org/abs/2104.04605 |
| Title | Evaluation and deployment of isotype-specific salivary antibody assays for detecting previous SARS-CoV-2 infection in children and adults |
| Description | These data are results relating to SARS-CoV-2 antibody testing conducted on saliva using ELISA. Saliva samples were collected from individuals before SARS-CoV-2 emergence and from PCR confirmed COVID-19 cases, suspected cases and healthy donors. Samples were tested as part of assay development, optimisation and evaluation as described in the paper titled 'Evaluation and deployment of isotype-specific salivary antibody assays for detecting previous SARS-CoV-2 infection in children and adults' by Amy C Thomas, Elizabeth Oliver, Holly E Baum, Kapil Gupta, Kathryn L Shelley, Anna E Long, Hayley E Jones, Joyce Smith, Benjamin Hitchings, Natalie di Bartolo, Kate Vasileiou, Fruzsina Rabi, Hanin Alamir, Malak Eghleilib, Ore Francis, Jennifer Oliver, Begonia Morales-Aza, Ulrike Obst, Debbie Shattock, Rachael Barr, Lucy Collingwood, Kaltun Duale, Niall Grace, Guillaume Gonnage Livera, Lindsay Bishop, Harriet Downing, Fernanda Rodrigues, Nicholas Timpson, Caroline L Relton, Ashley Toye, Derek N Woolfson, Imre Berger, Anu Goenka, Andrew D Davidson, Kathleen M Gillespie, Alistair JK Williams, Mick Bailey, Ellen Brooks-Pollock, Adam Finn, Alice Halliday & the CoMMinS Study Team. There is a single data file named 'ThresholdValidation_alldataFV_260722.csv' within the folder '2. Data'. Corresponding ethical approvals and consent information is in the folder '1. Ethics and consent'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://data.bris.ac.uk/data/dataset/1urnu8sfg88322u2a9qh004zh6/ |
| Title | Metawards mathematical modelling framework for infectious disease |
| Description | We developed a modelling framework for the spatial spread of infectious diseases. The package is developed in python with an R interface for widespread use. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | It has fed into SPI-M subgroup of SAGE. |
| URL | http://metawards.org |
| Title | R code file sourced by figures_main_paper.R from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/R_code_file_sourced_by_figures_main_paper_R_from_Novel_coro... |
| Title | R code file sourced by figures_main_paper.R from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/R_code_file_sourced_by_figures_main_paper_R_from_Novel_coro... |
| Title | R code for generating Figures 2, 3 and 4 and Table 1 and numbers used in main text. from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/R_code_for_generating_Figures_2_3_and_4_and_Table_1_and_num... |
| Title | R code for generating Figures 2, 3 and 4 and Table 1 and numbers used in main text. from Novel coronavirus 2019-nCoV (COVID-19): early estimation of epidemiological parameters and epidemic size estimates |
| Description | Since it was first identified, the epidemic scale of the recently emerged novel coronavirus (2019-nCoV) in Wuhan, China, has increased rapidly, with cases arising across China and other countries and regions. Using a transmission model, we estimate a basic reproductive number of 3.11 (95% CI, 2.39-4.13), indicating that 58-76% of transmissions must be prevented to stop increasing. We also estimate a case ascertainment rate in Wuhan of 5.0% (95% CI, 3.6-7.4). The true size of the epidemic may be significantly greater than the published case counts suggest, with our model estimating 21 022 (prediction interval, 11 090-33 490) total infections in Wuhan between 1 and 22 January. We discuss our findings in the light of more recent information.This article is part of the theme issue 'Modelling that shaped the early COVID-19 pandemic response in the UK'. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/R_code_for_generating_Figures_2_3_and_4_and_Table_1_and_num... |
| Title | Supplement 2 Sparks et al. from A novel approach for evaluating contact patterns and risk mitigation strategies for COVID-19 in English primary schools with application of structured expert judgement |
| Description | Eliciation data in excel spreadsheet |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Supplement_2_Sparks_et_al_from_A_novel_approach_for_evaluat... |
| Title | Supplement 2 Sparks et al. from A novel approach for evaluating contact patterns and risk mitigation strategies for COVID-19 in English primary schools with application of structured expert judgement |
| Description | Eliciation data in excel spreadsheet |
| Type Of Material | Database/Collection of data |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| URL | https://rs.figshare.com/articles/dataset/Supplement_2_Sparks_et_al_from_A_novel_approach_for_evaluat... |
| Title | sj-R-2-smm-10.1177_09622802221107105 - Supplemental material for Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic |
| Description | Supplemental material, sj-R-2-smm-10.1177_09622802221107105 for Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic by Shaun R Seaman, Tommy Nyberg, Christopher E Overton, David J Pascall, Anne M Presanis and Daniela De Angelis in Statistical Methods in Medical Research |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://sage.figshare.com/articles/dataset/sj-R-2-smm-10_1177_09622802221107105_-_Supplemental_mater... |
| Title | sj-R-2-smm-10.1177_09622802221107105 - Supplemental material for Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic |
| Description | Supplemental material, sj-R-2-smm-10.1177_09622802221107105 for Adjusting for time of infection or positive test when estimating the risk of a post-infection outcome in an epidemic by Shaun R Seaman, Tommy Nyberg, Christopher E Overton, David J Pascall, Anne M Presanis and Daniela De Angelis in Statistical Methods in Medical Research |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| URL | https://sage.figshare.com/articles/dataset/sj-R-2-smm-10_1177_09622802221107105_-_Supplemental_mater... |
| Description | CO at Manchester University NHS Foundation Trust |
| Organisation | Manchester University NHS Foundation Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Full time 3-month and part-time (3-months at 40%, 3-months and 30% and 3-months at 20%) secondments of PDRA CO to the Clinical Data Science Unit (CDSU) at the Manchester University NHS Foundation Trust (MFT), to develop and operationalise a stochastic model to estimate duration in various hospital states, probability of hospital outcome, and ultimately to predict hospital bed demand. |
| Collaborator Contribution | Data access and model validation |
| Impact | MFT able to plan and manage resources better, in particular keeping running electives for as long as possible before stopping for the second COVID-19 pandemic wave (Autumn 2020), unlike during the first wave when everything stopped immediately. Model to predict hospital bed demand deployed to all Hospital Trusts in the North West. |
| Start Year | 2020 |
| Description | Clinical Data Science Unit senior analyst |
| Organisation | Manchester University NHS Foundation Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | Support to model development to predict hospital bed demand Advice on nosocomial transmission model |
| Collaborator Contribution | data access and model validation |
| Impact | EpiBeds modelling tools Bed surge forecasting methods. |
| Start Year | 2020 |
| Description | HL at Manchester University NHS Foundation Trust |
| Organisation | Manchester University NHS Foundation Trust |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | PhD student HL quantified of the risks associated with sharing staff between COVID and non-COVID wards, and developed a model to nosocomial infection that helped identifying wards more at risk of nosocomial outbreaks. |
| Collaborator Contribution | Data access and model validation |
| Impact | Quantification of the risks associated with sharing staff between COVID and non-COVID wards provided scientific evidence and robust justification of hospital staff compartmentalisation strategies already implemented based on intuitive arguments, increasing awareness and compliance. Faster detection of in-hospital outbreaks, and wards at greater risk of an outbreak can be flagged ahead of time, providing information on which patients may have been exposed, and therefore should be monitored. Nosoco - a user-friendly tool to track nosocomial infections and identify wards at high risk of outbreak. |
| Start Year | 2020 |
| Description | Infection Dynamics of Pandemics |
| Organisation | Isaac Newton Institute for Mathematical Sciences |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Contributed to 2 papers and 1 report, and to 4 publications as part of a Special Issue for the journal Epidemics as one of the key outputs of the Infectious Dynamics of Pandemics (IDP) programme in 2020 |
| Collaborator Contribution | The INI hosted the IDP programme, though virtually. |
| Impact | Multiple talks and seminars, 1 report, 4 papers and an upcoming Special Issue in the journal Epidemics. Multi-disciplinary collaboration involving mathematics, modelling, statistics, data science, epidemiology, public health, economics, policy. |
| Start Year | 2020 |
| Description | Isaac Newton Institute (INI) |
| Organisation | Isaac Newton Institute for Mathematical Sciences |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Interactions with the wider Mathematical research community on current COVID-19 modelling work that is being used to advise government policy regarding COVID-19 |
| Collaborator Contribution | Support in running monthly seminars an regular research meetings to disseminate work of the grant to the wider mathematical research community, |
| Impact | Monthly seminars, and three research meetings. |
| Start Year | 2021 |
| Description | National Core Study (PROTECT) theme 2 |
| Organisation | Health and Safety Executive (HSE) |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | I am a work package leader developing within host models of COVID and considering dose response. |
| Collaborator Contribution | research is by design multidisciplinary. |
| Impact | Presentation to PROTECT main meeting (Feb 2022). |
| Start Year | 2021 |
| Description | North West COVID-19 Modelling Collaborative |
| Organisation | NHS England |
| Department | NHS North West England |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | CO participating in advisory meetings and presenting model results supporting the coordination of the COVID-19 pandemic response in the North West |
| Collaborator Contribution | Data access and shaping connections with relevant partners |
| Impact | Coordination of the COVID-19 pandemic response in the North West |
| Start Year | 2020 |
| Description | ONS COVID-19 Infection Survey |
| Organisation | Office for National Statistics |
| Country | United Kingdom |
| Sector | Private |
| PI Contribution | Estimation of impact of individuals' characteristics (age, patient-facing role, etc.) and vaccination status on the risk of infection and transmission via household-based analysis of the ONS COVID-19 Infection Survey |
| Collaborator Contribution | Data access and training in statistical disclosure control |
| Impact | Multi-disciplinary collaboration between mathematical modellers, statisticians, epidemiologists, data scientists and data holders. Estimation of impact of individuals' characteristics (age, patient-facing role, etc.) and vaccination status on the risk of infection and transmission, and the role of children with and without schools open. Publications: https://arxiv.org/abs/2104.04605 https://arxiv.org/abs/2107.06545 |
| Start Year | 2020 |
| Description | RSS meeting on R |
| Organisation | Royal Statistical Society |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | Royal Statistical Society paper submission and presentation at the "RSS Special Topic Meeting on Covid-19 Transmission", with written reply to the 4 formal discussants, and with written discussion towards others' submissions. |
| Collaborator Contribution | Organisation of meeting to discuss and clarify the meaning and role of reproduction numbers, and the challenges in their real-time estimation |
| Impact | Scientific discussion and clarification of the meaning and role of reproduction numbers, and the challenges in their real-time estimation. This was important given the huge public confusion despite the unprecedented presence of the concept in the media. This was a multi-disciplinary collaboration with statisticians, mathematical modellers and epidemiologists. The main output is a publication, replies to commentators and comments on another publication, all part of a Special Issue in the Journal of the Royal Statistical Society A |
| Start Year | 2021 |
| Description | Rapid Assistance in Modelling the Pandemic (RAMP) |
| Organisation | University of Cambridge |
| Department | Department of Applied Mathematics and Theoretical Physics (DAMTP) |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | Joint organization of workshops held at Isaac Newton Institute |
| Collaborator Contribution | Joint organization of workshops held at Isaac Newton Institute |
| Impact | Outputs are a series of workshops on COVID-19 research topics of current priority: Genomic, Evolutionary and Epidemiological Approaches for Pandemics, 15th March 2022 Behaviour and Policy During Pandemics: Models and Methods, 22nd February 2022 The Role of Uncertainty in Mathematical Modelling of Pandemics, 8th - 10th February 2022 Optimal Vaccination Strategies, 14th December 2021 Modelling Behaviour to Inform Policy for Pandemics, 2nd, 4th & 5th November 2021 Understanding the Generation Time for COVID-19, 28th - 30th July 2021 Evolutionary Implications of the COVID-19 Vaccination Programme, 19th & 20th April 2021 |
| Start Year | 2021 |
| Description | Uncertainty Quantification for Covid UQ4Covid |
| Organisation | University of Exeter |
| Country | United Kingdom |
| Sector | Academic/University |
| PI Contribution | I am CO-PI on the UQ4Covid grant with PI Daniel Williamson (EPSRC), based on the modelling framework (metawards) developed as funded by the MRC grant. I wrote the original spatial covid model, and the PI and other COIs are developing the statistical framework for fitting this model. Continuing work is a collaborative effort. |
| Collaborator Contribution | The PI and CO-I have contributed to the continued development of the metawards modelling framework, especially the model fitting, but also the importation of data. |
| Impact | N/A |
| Start Year | 2020 |
| Title | MetaWards: A flexible metapopulation framework for modelling disease spread |
| Description | Understanding how disease spreads through populations is important when designing and implementing control measures. MetaWards implements a stochastic metapopulation model of disease transmission that enables geographical modelling of disease spread that can scale all the way from modelling local transmission up to full national-or international-scale outbreaks. It is built in Python and has a flexible plugin architecture to support complex scenario modelling. This enables the code to be adapted to model new situations and new control measures as they arise, e.g. emergence of new variants of disease, enaction of different types of movement restrictions, availability of different types of vaccines etc. It implements a userdefinable compartmental transmission model, such as an SIR model, that can be extended multi-dimensionally via multiple demographics or sub-populations, and multiple geographical regions. Models can be constructed from the various sources of movement and demographic data that are available, and are accelerated via Cython (Behnel et al., 2020), OpenMP, Scoop (Hold & Gagnon, 2019) and MPI4Py (Dalcin & Fang, 2021) to scale efficiently from running on personal laptops to large supercomputers. Python, R and command line interfaces and a complete set of tutorials empower researchers to adapt their models to a variety of scenarios. |
| Type Of Technology | Software |
| Year Produced | 2020 |
| Open Source License? | Yes |
| Impact | MetaWards has been downloaded over 100,000 times and forked 15 times. It is the main model being used for the EPSRC funded project 'UQ4Covid', EP/V051555/1. |
| URL | https://gtr.ukri.org/projects?ref=EP%2FV051555%2F1 |
| Description | Articles with +Plus Magazine |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | We have worked closely with the +Plus magazine to bring a direct link between the rapidly unfolding research on COVID-19 and science writing for a public audience. At present, we have 42 articles written in collaboration with the JUNIPER consortium and plus: https://plus.maths.org/content/juniper Some of these have had very wide reach, including being cited weekly on the UK Government's weekly update of R during the pandemic. |
| Year(s) Of Engagement Activity | 2020,2021,2022 |
| URL | https://plus.maths.org/content/juniper |
| Description | BBC News Channel interviews |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | BBC News Channel Interview on three separate occasions, commenting on COVID-19: Omicron variant, hospitalisations and testing. |
| Year(s) Of Engagement Activity | 2022 |
| Description | BBC News article |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | BBC News article on household bubbles |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.bbc.co.uk/news/uk-55372743 |
| Description | BBC2 programme "Lockdown 1.0 - Following the Science?" |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Featured prominently in BBC2 programme "Lockdown 1.0 - Following the Science?" about the 3-day doubling time estimate (half as long as SAGE estimates at the time), a game changer in the UK COVID response |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.bbc.co.uk/programmes/m000pjr1 |
| Description | British Infection Association Trainees Day -- Keynote talk |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | Presentation explaining to trainee doctors & members of the British Infection Association the modelling behind SPI-M and SAGE government advisory groups. |
| Year(s) Of Engagement Activity | 2020 |
| Description | Co-presenting a Royal Institution Christmas Lecture |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Julia Gog was a co-presenter on the Royal Institution's Christmas Lectures 2021. This is a prestigious and unique opportunity for very wide-reaching public engagement. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.rigb.org/explore-science/explore/video/going-viral-how-covid-changed-science-forever-per... |
| Description | Hollingsworth media activity |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Hollingsworth and Davis (post-doc) gave several interviews to BBC TV news channel regarding the pandemic. |
| Year(s) Of Engagement Activity | 2020,2021,2022 |
| Description | Interview for The Buzz, University of Manchester podcast |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Public/other audiences |
| Results and Impact | Interview for The Buzz, podcast of the Faculty of Science and Engineering at the University of Manchester, for a special episode on the work done at the university to advice policy for the COVID-19 pandemic response in the UK |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.mub.eps.manchester.ac.uk/science-engineering/2020/06/05/covid0-19-fse-report/ |
| Description | Invited guest at "Homecoming", University of Trieste |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Invited to "Homecoming", a meeting presenting 4 stories-of-success for my home University in Trieste, Italy |
| Year(s) Of Engagement Activity | 2020 |
| URL | https://www.youtube.com/watch?v=77HrReu2gyg |
| Description | Joint Gresham College and London Mathematical Society lecture |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Public/other audiences |
| Results and Impact | Lecture title "Maths vs COVID-19". Julia Gog was invited to given the annual joint lecture between Gresham College and the London Mathematical Society. This was given online in May 2021. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.gresham.ac.uk/lectures-and-events/maths-covid |
| Description | Lancaster University Health-Covid Showcase |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Postgraduate students |
| Results and Impact | Presented work on estimating nosocomial infection rates for covid to Lancaster University staff and students, local public health department, and general public |
| Year(s) Of Engagement Activity | 2022 |
| Description | Media Interviews by Warwick team |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | During the COVID-19 pandemic, the Warwick team have engaged in over 600 media interviews, most of which have been carried out by Mike Tildesley, focusing upon the spread of disease and the modelling work carried out by the Warwick team and collaborators. These interviews have included appearances on the Radio Four Today Programme, Radio Five Live, BBC Breakfast News, Sky News, Good Morning Britain and several international, national and local radio stations. |
| Year(s) Of Engagement Activity | 2020,2021,2022 |
| Description | Member of EPSRC healthcare technology SAT |
| 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 | The EPSRC healthcare tech SAT supports EPSRC strategic vision |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://epsrc.ukri.org/about/governance/sats/ |
| Description | On the mathematical frontline |
| Form Of Engagement Activity | A broadcast e.g. TV/radio/film/podcast (other than news/press) |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | Interview for a podcast series to epidemic modellers involved in the JUNIPER consortium and in advising the COVID-19 pandemic response in the UK |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://plus.maths.org/content/index.php/mathematical-frontline-francesca-scarabel |
| Description | Podcast with +Plus Magazine |
| Form Of Engagement Activity | Engagement focused website, blog or social media channel |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | +Plus magazine podcast |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://plus.maths.org/content/mathematical-frontline-ellen-brooks-pollock-and-leon-danon |
| Description | REF output advisor - public health panel |
| Form Of Engagement Activity | A formal working group, expert panel or dialogue |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Policymakers/politicians |
| Results and Impact | About 400 research outputs assessed and scored for the REF Sub-panel 2: Public Health, Health Services and Primary Care |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.ref.ac.uk/ |
| Description | RSS Special Topic Meeting on Covid-19 Transmission |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Professional Practitioners |
| Results and Impact | The scope of the event was to clarify the meaning and role of reproduction numbers, and the challenges in their real-time estimation, given the huge public confusion despite the unprecedented presence of the concept in the media. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://rss.org.uk/news-publication/publications/journals/special-topic-meeting-on-r/ |
| Description | Radio 4 Today interview. |
| Form Of Engagement Activity | A press release, press conference or response to a media enquiry/interview |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Expert scientist, commenting on various aspects of the COVID-19 pandemic on three separate occasions. |
| Year(s) Of Engagement Activity | 2020,2021 |
| Description | Science Media Centre Briefing |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | Briefing attended by all major media organizations on nosocomial infection work. |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://www.sciencemediacentre.org/hospital-acquired-sars-cov-2-infection-in-the-uks-first-covid-19-... |
| Description | Science Media Centre briefings |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Media (as a channel to the public) |
| Results and Impact | SMC press briefings following the release of "roadmap" documents showing projections of the potential impacts of the relaxation steps planned throughout 2021. |
| Year(s) Of Engagement Activity | 2021 |
| Description | Understanding waning immunity |
| Form Of Engagement Activity | A magazine, newsletter or online publication |
| Part Of Official Scheme? | No |
| Geographic Reach | International |
| Primary Audience | Public/other audiences |
| Results and Impact | General audience article on the usefulness of simple models to understand the impact of waning immunity on the long-term dynamics of COVID-19 |
| Year(s) Of Engagement Activity | 2021 |
| URL | https://plus.maths.org/content/so-whats-waning |