Mathematical modeling and adaptive control to inform real time decision making for the COVID-19 pandemic at the local, regional and national scale
Lead Research Organisation:
University of Warwick
Department Name: Mathematics
Abstract
The world is currently being devastated by a pandemic of coronavirus disease (COVID-19) which, at the time of writing, has resulted in almost 1 million confirmed cases of infection and around 50,000 deaths worldwide. Around one third of the global population are under some form of restriction - causing huge economic burdens - and for many countries, focus has turned to how planning an "exit strategy" from some of the most severe social distancing measures that the world has ever seen.
This project will use real time data on the UK COVID-19 outbreak to provide robust predictions, guaging the ability of a model to predict future epidemic behaviour. We will investigate how our short- and long-term predictions change during an outbreak as more information becomes available, how this may effect forecasts of the appropriate control measures that should be introduced and when and how such policies should be relaxed. Finally, taking into account the potential for future waves of infection, we will use our model to determine optimal adaptive control policies that should be implemented to reduce the number of deaths as a result of the COVID-19 outbreak and to minimise the impact on the health service.
This project will use real time data on the UK COVID-19 outbreak to provide robust predictions, guaging the ability of a model to predict future epidemic behaviour. We will investigate how our short- and long-term predictions change during an outbreak as more information becomes available, how this may effect forecasts of the appropriate control measures that should be introduced and when and how such policies should be relaxed. Finally, taking into account the potential for future waves of infection, we will use our model to determine optimal adaptive control policies that should be implemented to reduce the number of deaths as a result of the COVID-19 outbreak and to minimise the impact on the health service.
Technical Summary
emergence of a novel strain of coronavirus in the city of Wuhan in China resulted in a global pandemic and the implementation of social distancing measures in a significant number of countries around the world in order to reduce the risk to the most vulnerable members of society. The first case of infection in the UK was reported on 31st January 2020 and with cases continuing to rise, the country was put into lockdown on 23rd March in an effort to reduce the spread of disease.
Throughout the epidemic in the UK, mathematical models (including predictions from Warwick) have been used to provide support to the government and to guide decision making. However, these models are typically required to repeatedly produce new outputs as more data emerges on a daily basis on cases and deaths, and there is a need to investigate how the predictions are likely to change as more data become available.
This project will develop methodology that will allow for robust parameter inference of the Warwick model, which is already being used for UK-decision support. We will enhance our real time model fitting, incorporating up to date information on cases and outcomes, and use this framework to determine multi-phase adaptive control policies, with a focus upon optimal timing of relaxation and tightening of social distancing measures, that should be implemented to mitigate future infection waves. Our results will be communicated directly to the scientific pandemic influenza modelling group that advises the UK government.
Throughout the epidemic in the UK, mathematical models (including predictions from Warwick) have been used to provide support to the government and to guide decision making. However, these models are typically required to repeatedly produce new outputs as more data emerges on a daily basis on cases and deaths, and there is a need to investigate how the predictions are likely to change as more data become available.
This project will develop methodology that will allow for robust parameter inference of the Warwick model, which is already being used for UK-decision support. We will enhance our real time model fitting, incorporating up to date information on cases and outcomes, and use this framework to determine multi-phase adaptive control policies, with a focus upon optimal timing of relaxation and tightening of social distancing measures, that should be implemented to mitigate future infection waves. Our results will be communicated directly to the scientific pandemic influenza modelling group that advises the UK government.
Organisations
Publications
Althobaity Y
(2022)
A comparative analysis of epidemiological characteristics of MERS-CoV and SARS-CoV-2 in Saudi Arabia.
in Infectious Disease Modelling
Althobaity Y
(2022)
Non-pharmaceutical interventions and their relevance in the COVID-19 vaccine rollout in Saudi Arabia and Arab Gulf countries.
in Infectious Disease Modelling
Althobaity Y
(2023)
Modelling the impact of non-pharmaceutical interventions on the spread of COVID-19 in Saudi Arabia.
in Scientific reports
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.)
Dyson L
(2021)
Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics
in Nature Communications
Eames KTD
(2023)
Coughs, colds and "freshers' flu" survey in the University of Cambridge, 2007-2008.
in Epidemics
Enright J
(2021)
SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return.
in Royal Society open science
Description | This project focused upon the development of mathematical models that could provide evidence to support the UK government COVID-19 response. The project created a suite a models that provided short term forecasts to simulate the spread of disease in the UK during the pandemic and explore the impact of the introduction of interventions and relaxation of lockdown policies. Research was also carried out upon the impact of COVID-19 in educational settings (schools and universities) and in workplaces, as well as the optimal policy to minimise the overall cost associated with outbreaks. The project was able to establish a set of models that could be utilised in future emerging outbreaks and provide insights into the public health impact of future epidemics. |
Exploitation Route | The models developed during this project could be adapted to inform policy makers regarding the risk associated with future emerging respiratory diseases and the role of intervention policies to reduce public health risk. |
Sectors | Education Healthcare Government Democracy and Justice |
Description | The work that was carried out during this project was presented in meetings of the Scientific Pandemic Influenza Modelling Operational Group (SPI-M-O) and the Scientific Advisory Group for Emergencies (SAGE) and utilised to inform the UK government regarding the future spread of COVID-19 and the potential impact of intervention policies across different settings. |
First Year Of Impact | 2020 |
Sector | Education,Healthcare,Government, Democracy and Justice |
Impact Types | Policy & public services |
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 |
Title | Network model to simulate spread of disease in universities and in the UK workforce |
Description | As a direct result of this research grant, the Warwick team have developed a network model to simulate the spread of disease in UK workers and UK students in a university setting. This model has been used to develop optimal strategies for workers to return to workplaces and for students to return to university campuses. Outputs from these models have been presented at the Scientific Pandemic Influenza Modelling Group meetings and at SAGE and papers are currently under peer review. |
Type Of Material | Computer model/algorithm |
Year Produced | 2020 |
Provided To Others? | No |
Impact | The models have been used to simulate strategies for workers to return to work and for staggered return of students to university campuses and to investigate the impact of lateral flow testing upon transmission and isolation of students. These outputs have been presented to SPI-M/SAGE. |
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 |