Multiresolution predictive dynamics of COVID-19 risk and intervention effects
Lead Research Organisation:
IMPERIAL COLLEGE LONDON
Department Name: School of Public Health
Abstract
SARS-CoV2 is a novel virus, and even as new data improves scientific insight, many uncertainties remain about key aspects of transmission. Throughout the pandemic, mathematical and statistical models of COVID-19 have had an important role in the analysis of epidemiological data, in forecasting incidence trends and in assessing the potential impact of different intervention strategies. Models developed by the Imperial College COVID-19 response team have been particularly influential, but the absence of detailed data on transmission patterns have necessitated important assumptions that limit their predictive performance. This project will (a) extend predictive models of transmission trends to include complex spatiotemporal correlation to better capture new seeding events and improve early identification of hotspots of transmission, (b) understand the causal effect of interventions on transmission and the limits to which this inference is possible, (c) systematically collate and analyse data on transmission in specific contexts (households, schools, workplaces and care homes) to derive specific transmission parameter estimates for those settings to be used to improve the ability of models to predict the impact of targeted non pharmaceutical interventions, (d) Understand how important epidemiological parameters are changing with time and what is driving these changes. This work will directly support the Imperial team's input into the UK COVID-19 response via the SPI-M, NERVTAG and SAGE committees and our partnerships with PHE and the Joint Biosecurity Centre (JBC).
Technical Summary
SARS-CoV2 is a novel virus, and even as new data improves scientific insight, many uncertainties
remain about key aspects of transmission. Throughout the pandemic, mathematical and statistical
models of COVID-19 have had an important role in the analysis of epidemiological data, in
forecasting incidence trends and in assessing the potential impact of different intervention
strategies. Models developed by the Imperial College COVID-19 response team have been
particularly influential, but the absence of detailed data on transmission patterns have
necessitated important assumptions that limit their predictive performance. This project will (a)
extend predictive models of transmission trends to include complex spatiotemporal correlation to
better capture new seeding events and improve early identification of hotspots of transmission,
(b) understand the causal effect of interventions on transmission and the limits to which this
inference is possible, (c) systematically collate and analyse data on transmission in specific
contexts (households, schools, workplaces and care homes) to derive specific transmission
parameter estimates for those settings to be used to improve the ability of models to predict the
impact of targeted non pharmaceutical interventions, (d) Understand how important
epidemiological parameters are changing with time and what is driving these changes. This work
will directly support the Imperial team's input into the UK COVID-19 response via the SPI-M,
NERVTAG and SAGE committees and our partnerships with PHE and the Joint Biosecurity Centre
(JBC).
remain about key aspects of transmission. Throughout the pandemic, mathematical and statistical
models of COVID-19 have had an important role in the analysis of epidemiological data, in
forecasting incidence trends and in assessing the potential impact of different intervention
strategies. Models developed by the Imperial College COVID-19 response team have been
particularly influential, but the absence of detailed data on transmission patterns have
necessitated important assumptions that limit their predictive performance. This project will (a)
extend predictive models of transmission trends to include complex spatiotemporal correlation to
better capture new seeding events and improve early identification of hotspots of transmission,
(b) understand the causal effect of interventions on transmission and the limits to which this
inference is possible, (c) systematically collate and analyse data on transmission in specific
contexts (households, schools, workplaces and care homes) to derive specific transmission
parameter estimates for those settings to be used to improve the ability of models to predict the
impact of targeted non pharmaceutical interventions, (d) Understand how important
epidemiological parameters are changing with time and what is driving these changes. This work
will directly support the Imperial team's input into the UK COVID-19 response via the SPI-M,
NERVTAG and SAGE committees and our partnerships with PHE and the Joint Biosecurity Centre
(JBC).
Publications
Gavenciak T
(2021)
Seasonal variation in SARS-CoV-2 transmission in temperate climates
Ainslie KEC
(2020)
Evidence of initial success for China exiting COVID-19 social distancing policy after achieving containment.
in Wellcome open research
Hakki S
(2022)
Onset and window of SARS-CoV-2 infectiousness and temporal correlation with symptom onset: a prospective, longitudinal, community cohort study.
in The Lancet. Respiratory medicine
Derqui N
(2023)
Risk factors and vectors for SARS-CoV-2 household transmission: a prospective, longitudinal cohort study.
in The Lancet. Microbe
Bager P
(2022)
Risk of hospitalisation associated with infection with SARS-CoV-2 omicron variant versus delta variant in Denmark: an observational cohort study.
in The Lancet. Infectious diseases
Hogan AB
(2020)
Potential impact of the COVID-19 pandemic on HIV, tuberculosis, and malaria in low-income and middle-income countries: a modelling study.
in The Lancet. Global health
Unwin HJT
(2022)
Global, regional, and national minimum estimates of children affected by COVID-19-associated orphanhood and caregiver death, by age and family circumstance up to Oct 31, 2021: an updated modelling study.
in The Lancet. Child & adolescent health
Mishra S
(2021)
Comparing the responses of the UK, Sweden and Denmark to COVID-19 using counterfactual modelling.
in Scientific reports
Altman G
(2022)
A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe.
in Scientific data
Knock ES
(2021)
Key epidemiological drivers and impact of interventions in the 2020 SARS-CoV-2 epidemic in England.
in Science translational medicine
Scott L
(2021)
Track Omicron's spread with molecular data.
in Science (New York, N.Y.)
Faria NR
(2021)
Genomics and epidemiology of the P.1 SARS-CoV-2 lineage in Manaus, Brazil.
in Science (New York, N.Y.)
Walker PGT
(2020)
The impact of COVID-19 and strategies for mitigation and suppression in low- and middle-income countries.
in Science (New York, N.Y.)
Dhar MS
(2021)
Genomic characterization and epidemiology of an emerging SARS-CoV-2 variant in Delhi, India.
in Science (New York, N.Y.)
Suel E
(2021)
Multimodal deep learning from satellite and street-level imagery for measuring income, overcrowding, and environmental deprivation in urban areas.
in Remote sensing of environment
Leech G
(2022)
Mask wearing in community settings reduces SARS-CoV-2 transmission.
in Proceedings of the National Academy of Sciences of the United States of America
Hawryluk I.
(2021)
Gaussian Process Nowcasting: Application to COVID-19 Mortality Reporting
in Proceedings of Machine Learning Research
Dan S
(2023)
Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model.
in PLoS computational biology
Mehrjou A
(2023)
Pyfectious: An individual-level simulator to discover optimal containment policies for epidemic diseases.
in PLoS computational biology
Gavenciak T
(2022)
Seasonal variation in SARS-CoV-2 transmission in temperate climates: A Bayesian modelling study in 143 European regions.
in PLoS computational biology
Brizzi A
(2022)
Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals.
in Nature medicine
Sharma M
(2021)
Understanding the effectiveness of government interventions against the resurgence of COVID-19 in Europe.
in Nature communications
Unwin HJT
(2020)
State-level tracking of COVID-19 in the United States.
in Nature communications
Mlcochova P
(2021)
SARS-CoV-2 B.1.617.2 Delta variant replication and immune evasion.
in Nature
Lavezzo E
(2020)
Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'.
in Nature
Volz E
(2021)
Assessing transmissibility of SARS-CoV-2 lineage B.1.1.7 in England.
in Nature
Brito AF
(2021)
Global disparities in SARS-CoV-2 genomic surveillance.
in medRxiv : the preprint server for health sciences
Faria NR
(2021)
Genomics and epidemiology of a novel SARS-CoV-2 lineage in Manaus, Brazil.
in medRxiv : the preprint server for health sciences
Sonabend R
(2021)
Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: a mathematical modelling study.
in Lancet (London, England)
Nyberg T
(2022)
Misclassification bias in estimating clinical severity of SARS-CoV-2 variants - Authors' reply.
in Lancet (London, England)
Nyberg T
(2022)
Comparative analysis of the risks of hospitalisation and death associated with SARS-CoV-2 omicron (B.1.1.529) and delta (B.1.617.2) variants in England: a cohort study.
in Lancet (London, England)
Thompson HA
(2020)
SARS-CoV-2 infection prevalence on repatriation flights from Wuhan City, China.
in Journal of travel medicine
Verity R
(2026)
Robert Verity, Samir Bhatt, Anne Cori, Seth Flaxman, and Swapnil Mishra's contribution to the Discussion of 'Some statistical aspects of the Covid-19 response' by Wood et al.
in Journal of the Royal Statistical Society Series A: Statistics in Society
Gurdasani D
(2021)
Vaccinating adolescents against SARS-CoV-2 in England: a risk-benefit analysis.
in Journal of the Royal Society of Medicine
Krawczyk K
(2021)
Quantifying Online News Media Coverage of the COVID-19 Pandemic: Text Mining Study and Resource.
in Journal of medical Internet research
Pakkanen MS
(2023)
Unifying incidence and prevalence under a time-varying general branching process.
in Journal of mathematical biology
Christensen B
(2022)
Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.
in JMIR infodemiology
Flaxman S
(2023)
Assessment of COVID-19 as the Underlying Cause of Death Among Children and Young People Aged 0 to 19 Years in the US
in JAMA Network Open
| Description | The work on this grant has been used to directly inform UK COVID-19 policy via SPI-M, Nervtag and SAGE. The outputs which have not been published have been performed to answer specific scientific questions relevant to the UK government. Outputs from this grant have directly informed the UK COVID-19 response. |
| First Year Of Impact | 2021 |
| Sector | Healthcare,Government, Democracy and Justice |
| Impact Types | Cultural Societal Economic Policy & public services |
| Title | Data from: SARS-CoV-2 antibody dynamics in blood donors and COVID-19 epidemiology in eight Brazilian state capitals |
| Description | The COVID-19 situation in Brazil is complex due to large differences in the shape and size of regional epidemics. Here we tested monthly blood donation samples for IgG antibodies from March 2020 to March 2021 in eight of Brazil's most populous cities. The inferred attack rate of SARS-CoV-2 adjusted for seroreversion in December 2020, before the Gamma VOC was dominant, ranged from 19.3% (95% CrI 17.5% - 21.2%) in Curitiba to 75.0% (95% CrI 70.8% - 80.3%) in Manaus. Seroprevalence was consistently smaller in women and donors older than 55 years. The age-specific infection fatality rate (IFR) differed between cities and consistently increased with age. The infection hospitalisation rate (IHR) increased significantly during the Gamma-dominated second wave in Manaus, suggesting increased morbidity of the Gamma VOC compared to previous variants circulating in Manaus. The higher disease penetrance associated with the health system's collapse increased the overall IFR by a minimum factor of 2.91 (95% CrI 2.43 - 3.53). These results highlight the utility of blood donor serosurveillance to track epidemic maturity and demonstrate demographic and spatial heterogeneity in SARS-CoV-2 spread. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | http://datadryad.org/stash/dataset/doi:10.5061/dryad.dz08kps08 |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January 2020 - June 2021 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Response2covid19") and the reference: Porcher, Simon "Response2covid19, a dataset of governments' responses to COVID-19 all around the world", Scientific Data, 7, 423, 2020. https://doi.org/10.1038/s41597-020-00757-y |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V7/view |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January 2020 - June 2021 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Response2covid19") and the reference: Porcher, Simon "Response2covid19, a dataset of governments' responses to COVID-19 all around the world", Scientific Data, 7, 423, 2020. https://doi.org/10.1038/s41597-020-00757-y |
| Type Of Material | Database/Collection of data |
| Year Produced | 2022 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061 |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-July 2020 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Governments' Responses to COVID-19 (Response2covid19)") and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V5/view?path=/openicpsr/119061/fcr:versio... |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-July 2020 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Governments' Responses to COVID-19 (Response2covid19)") and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V5/view?path=/openicpsr/119061/fcr:versio... |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-July 2020 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Governments' Responses to COVID-19 (Response2covid19)") and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V5/view |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Governments' Responses to COVID-19 (Response2covid19)") and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V6/view?path=/openicpsr/119061/fcr:versio... |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Governments' Responses to COVID-19 (Response2covid19)") and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V6/view?path=/openicpsr/119061/fcr:versio... |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Governments' Responses to COVID-19 (Response2covid19)") and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V6/view |
| Title | Governments' Responses to COVID-19 (Response2covid19) |
| Description | The Response2covid19 dataset tracks governments' responses to COVID-19 all around the world. The dataset is at the country-level and covers the January-October 2020 period; it is updated on a monthly basis. It tracks 20 measures - 13 public health measures and 7 economic measures - taken by 228 governments. The tracking of the measures allows creating an index of the rigidity of public health measures and an index of economic response to the pandemic. The objective of the dataset is both to inform citizens and to help researchers and governments in fighting the pandemic.The dataset can be downloaded and used freely. Please properly cite the name of the dataset ("Governments' Responses to COVID-19 (Response2covid19)") and the reference: Porcher, Simon "A novel dataset of governments' responses to COVID-19 all around the world", Chaire EPPP 2020-03 discussion paper, 2020. |
| Type Of Material | Database/Collection of data |
| Year Produced | 2020 |
| Provided To Others? | Yes |
| Impact | n/a |
| URL | https://www.openicpsr.org/openicpsr/project/119061/version/V6/view?path=/openicpsr/119061/fcr:versio... |
| Description | Collaboration with the Statens Serum Institut |
| Organisation | The Statens Serum Institute (SSI) |
| Country | Denmark |
| Sector | Public |
| PI Contribution | In collaboration with the SSI my group worked on evaluating Omicron severity in Denmark. Since this initial collaboration with the SSI this partnership has matured into a more formal collaboration. The key individuals at the SSI running this partnership are Henrik Ullam and Tyra Grove Krause. Currently, we are studying the longitudinal speak of SARS-COV2 in Denmark, and the heterogenities in this spread. The SSI has given us access to all genetic records, and these are currently being moved to Statistics Denmark (under Laust Mortensens Data Science Lab) for analysis. The large scale publication from this collaboration will deliver on key aspects including, models for longitudinal spread as well as genetic linkages to risk factors |
| Collaborator Contribution | The SSI has allowed us access to their genetic data pipe lines, and provided staff time in terms of a project manager Christian Anders Wathne Bruhn. The SSI will also provide scientific input throughout the collaboration |
| Impact | This work has resulted in a publication in Lancet Infectious diseases and a commentary in the lancet. |
| Start Year | 2022 |
| Description | Fellow of the Academy of Medical Sciences UK |
| Organisation | Academy of Medical Sciences (AMS) |
| Country | United Kingdom |
| Sector | Charity/Non Profit |
| PI Contribution | As a fellow I am responsible for guiding medical policy globally, and in particular with a focus to European policy |
| Collaborator Contribution | The academy provides me with meeting space that can be used for conferences etc, but more broadly is a marker of prestige. |
| Impact | none |
| Start Year | 2023 |
| Description | NOVONORDISK Young Investigator Award |
| Organisation | University of Copenhagen |
| Country | Denmark |
| Sector | Academic/University |
| PI Contribution | Established collaboration with the section of Epidemiology reevnvisioning life course theory. |
| Collaborator Contribution | 5% funded Helen Coupland PhD student |
| Impact | Not yet recognised |
| Start Year | 2020 |
| Description | Omicron severity |
| Organisation | The Statens Serum Institute (SSI) |
| Country | Denmark |
| Sector | Public |
| PI Contribution | Evaluation of data relating to Omicron severity |
| Collaborator Contribution | Provision of data |
| Impact | Paper under review in Lancet Infectious Disease |
| Start Year | 2021 |
| Description | Phylo2Vec: Accelerating Phylogenetic Research - eScience partnership |
| Organisation | University of Washington |
| Department | eScience Institute |
| Country | United States |
| Sector | Academic/University |
| PI Contribution | In a publication developed under this grant, we invented a new algorithm to represent phylogenetic trees - an object to summarise genetic data and time. We applied to the eScience institute and were awarded with developer time to write our code professionally and perform unit testing. |
| Collaborator Contribution | eScience has provided 3 professional programmers time to developing our software in the language RUST |
| Impact | The output is a GitHub repository and a paper in progress |
| Start Year | 2024 |
| Description | Scientific Advisory Group for Emergencies (SAGE) |
| Organisation | Government of the UK |
| Department | Scientific Advisory Group for Emergencies (SAGE) |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | As part of SAGE, this grant was instrumental in the UK unlocking roadmap https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/963440/S1129__Unlocking__Roadmap_Scenarios_for_England_.pdf As part of SAGE, documents were published on the UK strategy for vaccinations and the removal of NPIs https://www.gov.uk/government/publications/imperial-college-london-strategies-for-gradually-lifting-npis-in-parallel-to-covid-19-vaccine-roll-out-in-the-uk-4-february-2021 https://www.gov.uk/government/publications/imperial-college-london-potential-profile-of-the-covid-19-epidemic-in-the-uk-under-different-vaccination-roll-out-strategies-13-january-2021 |
| Collaborator Contribution | Data and advisory expertise |
| Impact | https://www.gov.uk/government/publications/imperial-college-london-potential-profile-of-the-covid-19-epidemic-in-the-uk-under-different-vaccination-roll-out-strategies-13-january-2021 https://www.gov.uk/government/publications/imperial-college-london-strategies-for-gradually-lifting-npis-in-parallel-to-covid-19-vaccine-roll-out-in-the-uk-4-february-2021 https://www.gov.uk/government/publications/imperial-college-london-unlocking-roadmap-scenarios-for-england-18-february-2021 |
| Start Year | 2021 |
| Description | Branching processes for infectious diseases |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Regional |
| Primary Audience | Professional Practitioners |
| Results and Impact | This was a talk in collaboration with Oxford Big data institute, statistics and computer science. |
| Year(s) Of Engagement Activity | 2022 |
| URL | https://www.stats.ox.ac.uk/events/joint-statistics-computer-science-bdi-talk-24th-feb-2022/ |
| Description | EPQ Centre Supervisor - Talk to A-Level Students |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Schools |
| Results and Impact | I was invited to give a talk about research experience and the joys of working in academia. I gave a talk around advice for a career in academia |
| Year(s) Of Engagement Activity | 2021 |
| Description | NNF Data Science Talk about Understanding cause and effect through data science and novel biomedical data sources |
| Form Of Engagement Activity | A talk or presentation |
| Part Of Official Scheme? | No |
| Geographic Reach | Local |
| Primary Audience | Professional Practitioners |
| Results and Impact | This was a key note speech on the topic of causality in biomedical research |
| Year(s) Of Engagement Activity | 2021 |