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).

Publications

10 25 50
 
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

 
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 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