COVID-19: Optimal Lockdown

COVID-19：最佳封锁

• 批准号: EP/V025899/1
• 负责人: Ritabrata Dutta
• 金额: $ 20.92万
• 依托单位: University of Warwick
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV025899%2F1
• 关键词: COVID; 19; Optimal; Lockdown

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.

当前的 COVID-19 大流行已导致整个国家陷入封锁，对人们的生活和经济产生了巨大影响。当然，人们对这种封锁的效率有多高提出疑问，并且越来越关注我们的国家将如何减少社交距离措施并最终恢复正常。我们建议通过使用最先进的流行病学模型来回答其中一些问题，方法是使用人口普查数据来准确模拟英国人口的典型行为，然后将其与来自 NHS 的日益可用的数据结合起来，以了解 COVID-19 在英国的传播， PHE 和 ONS，这将帮助我们模拟 COVID-19 在社区中的传播。然后将探索这些模型，以设计基于关闭公共和商业场所或关闭交通线路的最佳缓解策略，以及通过重新开放此类场所或逐步恢复公共交通来实现的退出策略。这些策略将根据每日数据频繁调整。我们得出的模型和控制策略将在专门网站上公开发布，该网站将随着新数据的出现而经常更新。

项目成果

Controlling Swarms toward Flocks and Mills

控制蜂群走向羊群和磨坊

• DOI: 10.1137/21m1404314
• 发表时间: 2022
• 期刊: SIAM Journal on Control and Optimization
• 影响因子: 2.2
• 作者: Carrillo J

Moment-Driven Predictive Control of Mean-Field Collective Dynamics

• DOI: 10.1137/21m1391559
• 发表时间: 2021-01
• 期刊: SIAM J. Control. Optim.
• 作者: G. Albi;M. Herty;D. Kalise;C. Segala

Tensor Decomposition Methods for High-dimensional Hamilton--Jacobi--Bellman Equations

高维Hamilton--Jacobi--Bellman方程的张量分解方法

• DOI: 10.1137/19m1305136
• 发表时间: 2021
• 期刊: SIAM Journal on Scientific Computing
• 影响因子: 3.1
• 作者: Dolgov S

State-dependent Riccati equation feedback stabilization for nonlinear PDEs

• DOI: 10.1007/s10444-022-09998-4
• 发表时间: 2021-06
• 期刊: Advances in Computational Mathematics
• 影响因子: 1.7
• 作者: A. Alla;D. Kalise;V. Simoncini

Gradient-augmented Supervised Learning of Optimal Feedback Laws Using State-Dependent Riccati Equations

使用状态相关 Riccati 方程的最优反馈定律的梯度增强监督学习

• DOI: 10.1109/lcsys.2021.3086697
• 发表时间: 2022
• 期刊: IEEE Control Systems Letters
• 影响因子: 3
• 作者: Albi G