Mathematical modeling and adaptive control to inform real time decision making for the COVID-19 pandemic at the local, regional and national scale

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.

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.