COVID-19: Optimal Lockdown
Lead Research Organisation:
University of Warwick
Department Name: Statistics
Abstract
The current COVID-19 pandemic has caused whole countries to lockdown, with a huge effect on people's lives and in the economy. Naturally, there are questions about how efficient this lockdown is, and increasing interest in how our country will reduce social distancing measures and eventually go back to normal. We propose to answer some of these questions by using cutting edge epidemiological models for the spread of COVID-19 in the UK using census data to model the typical behaviour of the UK population accurately and then combining this with the increasingly available data from the NHS, PHE and the ONS, which will help us model the spread of COVID-19 in our communities. These models will then be explored in order to design an optimal mitigation strategy based on closing public and commercial venues, or shutting down transport links, and an exit strategy from our lockdown, which will be achieved by reopening such venues or gradually restoring public transports. These strategies will be adapted frequently in response to daily data. Our resulting models and control strategy will be publicly available on a dedicated website, which will be updated frequently as new data becomes available.
Publications
Albi G
(2022)
Moment-Driven Predictive Control of Mean-Field Collective Dynamics
in SIAM Journal on Control and Optimization
Albi G
(2022)
Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-Dependent Riccati Equations
in IEEE Control Systems Letters
Alla A
(2023)
State-dependent Riccati equation feedback stabilization for nonlinear PDEs
in Advances in Computational Mathematics
Carrillo J
(2022)
Controlling Swarms toward Flocks and Mills
in SIAM Journal on Control and Optimization
Cavallaro M
(2023)
Bayesian inference of polymerase dynamics over the exclusion process
in Royal Society Open Science
Dolgov S
(2021)
Tensor Decomposition Methods for High-dimensional Hamilton--Jacobi--Bellman Equations
in SIAM Journal on Scientific Computing
Dutta R
(2021)
Using mobility data in the design of optimal lockdown strategies for the COVID-19 pandemic.
in PLoS computational biology
Dutta R
(2021)
ABCpy : A High-Performance Computing Perspective to Approximate Bayesian Computation
in Journal of Statistical Software
Dutta R
(2022)
Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning.
in PLoS computational biology
Ebert A
(2021)
Likelihood-Free Parameter Estimation for Dynamic Queueing Networks: Case Study of Passenger Flow in an International Airport Terminal
in Journal of the Royal Statistical Society Series C: Applied Statistics
Description | Our main goal in this project is to develop an epidemiological model, which (a) can utilize mobility information available from tracking devices (e.g.. Google mobility, telephone locations etc.), (b) can provide a data-driven way to decide what should be optimal lock down strategy for a country to combat an on-going pandemic and finally (c) can be updated dynamically with the availability of more up-to-date data. At the first stage of our work, we have achieved all of these goals by developing an epidemiological model for a nation as a whole which uses Google Mobility data, illustrated its excellent performance in prediction of ongoing COVID-19 pandemic in England and France, provided a dynamic way of deciding optimal lock down strategies. This work is presently under review in PLOS Computational Biology. Further a talk titled "Optimal lock down with Google mobility" illustrating the main findings was given in "Mathematics of Big Data: Lessons from COVID-19" seminar of The Institute of Mathematics and its Applications (IMA), UK on December 15, 2020. This can be found here: https://www.youtube.com/watch?v=vmjy33VkObQ On the second stage of our work, we are presently working on developing a model which further utilizes commuting information between local authorities in the UK and can provide a dynamic data-driven means to decide optimal lock down strategies for each local authorities specifically. |
Exploitation Route | The present work provides a way to provide optimal lock down strategy for a country using publicly available dataset. Further development using new sources of datasets can be achieved (e.g.. using data from tracking locations of telephones). Any government decision making bodies can utilize the methodology and results which can help them making decisions in a data-driven manner. |
Sectors | Communities and Social Services/Policy,Healthcare,Government, Democracy and Justice,Security and Diplomacy |
URL | https://optimallockdown.github.io/Covid19inEngland/ |
Title | Omptimal Lockdown |
Description | A Github organization encompassing all the optimal lockdown related works done under this funding. |
Type Of Material | Computer model/algorithm |
Year Produced | 2021 |
Provided To Others? | Yes |
Impact | Have been used for modelling optimal lockdown. |
URL | https://github.com/OptimalLockdown/ |