Uncertainty Quantification for Expensive COVID-19 Simulation Models (UQ4Covid)
Lead Research Organisation:
UNIVERSITY OF EXETER
Department Name: Mathematics
Abstract
Accurate mathematical models of transmission are crucial for targeting successful interventions to combat the spread of SARS-Cov2. In the UK, established models are used to provide real time policy support to Government through the Scientific Pandemic Influenza group - Modelling (SPI-M). Modellers in SPI-M have a proven track record, and models are continually adapted to respond to the evolving pandemic. When using models to inform decision making, it is crucial that all sources of uncertainty are properly accounted for when calibrating and predicting. For 30 years the UK has been a world-leader in developing Uncertainty Quantification (UQ); delivering methods for formal treatments of uncertainty when using models to understand the world, allowing efficient and robust calibration and prediction. Despite this, these techniques are not currently in place for COVID-19 simulation models, leading to slower-than-necessary adaptive model development-UQ allows for fast re-calibration-and an under-representation of uncertainty in predictions delivered to policymakers.
This project will adapt and deliver UQ techniques, code and tutorials for models of COVID-19 in the UK, providing SPI-M modellers with tools to facilitate rapid re-calibration of their models when changes are made in response to the evolving pandemic, and to more accurately represent uncertainty in their predictions. We will work closely with MetaWards, a spatial meta-population transmission framework (Danon et al. 2009, 2020) that contributes to SPI-M, to develop and apply these tools as we move into the winter; enabling fast evaluation of interventions responding to localised outbreaks, efficacy of vaccine rollout strategies, duration of immunity and more.
This project will adapt and deliver UQ techniques, code and tutorials for models of COVID-19 in the UK, providing SPI-M modellers with tools to facilitate rapid re-calibration of their models when changes are made in response to the evolving pandemic, and to more accurately represent uncertainty in their predictions. We will work closely with MetaWards, a spatial meta-population transmission framework (Danon et al. 2009, 2020) that contributes to SPI-M, to develop and apply these tools as we move into the winter; enabling fast evaluation of interventions responding to localised outbreaks, efficacy of vaccine rollout strategies, duration of immunity and more.
Publications
Woods C
(2022)
MetaWards: A flexible metapopulation framework for modelling disease spread
in Journal of Open Source Software
Trickey A
(2021)
University students and staff able to maintain low daily contact numbers during various COVID-19 guideline periods
in Epidemiology and Infection
Thomas A
(2021)
Limits of lockdown: characterising essential contacts during strict physical distancing
in Wellcome Open Research
Swallow B
(2022)
Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling.
in Epidemics
Pellis L
(2022)
Estimation of reproduction numbers in real time: Conceptual and statistical challenges.
in Journal of the Royal Statistical Society. Series A, (Statistics in Society)
Nixon E
(2021)
Contacts and behaviours of university students during the COVID-19 pandemic at the start of the 2020/2021 academic year.
in Scientific reports
Morvan M
(2022)
An analysis of 45 large-scale wastewater sites in England to estimate SARS-CoV-2 community prevalence.
in Nature communications
Hyams C
(2021)
Effectiveness of BNT162b2 and ChAdOx1 nCoV-19 COVID-19 vaccination at preventing hospitalisations in people aged at least 80 years: a test-negative, case-control study.
in The Lancet. Infectious diseases
Hyams C
(2022)
Incidence of community acquired lower respiratory tract disease in Bristol, UK during the COVID-19 pandemic: A prospective cohort study.
in The Lancet regional health. Europe
Hyams C
(2023)
Severity of Omicron (B.1.1.529) and Delta (B.1.617.2) SARS-CoV-2 infection among hospitalised adults: A prospective cohort study in Bristol, United Kingdom.
in The Lancet regional health. Europe
Gibbs H
(2022)
Population disruption: observational study of changes in the population distribution of the UK during the COVID-19 pandemic
in Wellcome Open Research
Danon L
(2021)
A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Chatzilena A
(2023)
Relative vaccine effectiveness of mRNA COVID-19 boosters in people aged at least 75 years during the spring-summer (monovalent vaccine) and autumn-winter (bivalent vaccine) booster campaigns: a prospective test negative case-control study, United Kingdom, 2022
in Eurosurveillance
Chatzilena A
(2023)
Effectiveness of BNT162b2 COVID-19 vaccination in prevention of hospitalisations and severe disease in adults with SARS-CoV-2 Delta (B.1.617.2) and Omicron (B.1.1.529) variant between June 2021 and July 2022: A prospective test negative case-control study.
in The Lancet regional health. Europe
Challen R
(2021)
Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study.
in BMJ (Clinical research ed.)
Challen R
(2022)
Meta-analysis of the severe acute respiratory syndrome coronavirus 2 serial intervals and the impact of parameter uncertainty on the coronavirus disease 2019 reproduction number.
in Statistical methods in medical research
Challen R
(2023)
Clinical decision-making and algorithmic inequality.
in BMJ quality & safety
Brooks-Pollock E
(2021)
The population attributable fraction of cases due to gatherings and groups with relevance to COVID-19 mitigation strategies
in Philosophical Transactions of the Royal Society B: Biological Sciences
Brooks-Pollock E
(2021)
Modelling that shaped the early COVID-19 pandemic response in the UK.
in Philosophical transactions of the Royal Society of London. Series B, Biological sciences
Brooks-Pollock E
(2023)
Voluntary risk mitigation behaviour can reduce impact of SARS-CoV-2: a real-time modelling study of the January 2022 Omicron wave in England.
in BMC medicine
Brooks-Pollock E
(2021)
High COVID-19 transmission potential associated with re-opening universities can be mitigated with layered interventions.
in Nature communications
Description | That traditional UQ methods cannot work in real time for decision support in pandemics. We have developed and tested methodology based on a UQ-Data Assimilation hybrid that seems effective and would meet the need to real-time UQ in decision support scenarios. |
Exploitation Route | Once our papers are published, we want to work with specific infectious disease modelling groups to embed the UQ systems we have worked on into the models they develop. Such embedding will mean that the next pandemic can be faced with real time UQ already in place. |
Sectors | Aerospace, Defence and Marine,Healthcare,Government, Democracy and Justice |
Description | Our findings on variant spread were presented to SPI-M-O during the emergence of the Delta and Omicron variants, with models, uncertainties and figures we prepared being passed (along with similar from other groups) to SAGE to advise policy makers. The PI was recognised officially for these contributions by the government. |
First Year Of Impact | 2021 |
Sector | Healthcare,Government, Democracy and Justice |
Impact Types | Policy & public services |
Description | Spatial tracking of Covid-19 variant spread during spring 2021 for Spi-M/SAGE during the emergence of the Delta variant |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Spatial tracking of spread of Omicron Variant of Covid-19, November 2021-January 2022 |
Geographic Reach | National |
Policy Influence Type | Participation in a guidance/advisory committee |
Description | Joint Biosecurity Centre, Department for Health and Social Care Secondment 118311 |
Amount | £25,703 (GBP) |
Organisation | UK Health Security Agency |
Sector | Public |
Country | United Kingdom |
Start | 11/2021 |
End | 09/2022 |
Title | UQ4Covid Ensemble Data |
Description | Data from multiple ensembles of the UQ4Covid spatial version of Metawards. Can be used for developing UQ or methods for plotting high-resolution outbreaks and their response to parameter perturbation. |
Type Of Material | Database/Collection of data |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Unknown, though multiple papers in preparation. |
URL | https://gws-access.jasmin.ac.uk/public/covid19/ |
Title | UQ4Covid Tutorials |
Description | This is a set of methods, code, data and tutorials for fitting basic UQ methods to Covid-19 models. |
Type Of Material | Computer model/algorithm |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Unknown |
URL | https://uq4covid.github.io |
Description | Stefan and UKHSA |
Organisation | UK Health Security Agency |
Country | United Kingdom |
Sector | Public |
PI Contribution | Stefan Siegert, a project Co-I began consulting with them taking some of our methods and some of his own to help them with their covid prevalence model. |
Collaborator Contribution | Development of a Covid Prevalence model using waste water data. |
Impact | Paper in review in AIMS Mathematics |
Start Year | 2021 |
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 | 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 |