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

数学建模和自适应控制为地方、区域和国家范围内的 COVID-19 大流行的实时决策提供信息

• 批准号: MR/V009761/1
• 负责人: Michael Tildesley
• 金额: $ 30.98万
• 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=MR%2FV009761%2F1
• 关键词: Mathematical; modeling; adaptive; control; inform

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.

目前，世界受到冠状病毒病（Covid-19）的大流行的破坏，该病毒在撰写本文时，已导致近100万个确认的感染病例，在全球范围内约有50,000例死亡。全球人口的大约三分之一处于某种形式的限制下 - 造成了巨大的经济负担 - 对于许多国家而言，重点转向了如何从世界上看到过的一些最严重的社会疏远措施中计划“退出策略”。该项目将在英国Covid -19的实时数据中使用实时数据，以提供强大的预测，以提供强大的预测，以指导模型的未来水平范围。我们将研究在爆发期间的短期和长期预测如何随着更多信息提供，这可能会影响应引入应引入的适当控制措施的预测，以及何时以及如何放松此类政策。最后，考虑到未来感染浪潮的潜力，我们将使用模型来确定应实施的最佳自适应控制政策，以减少COVID-19的疫情导致的死亡人数，并最大程度地减少对卫生服务的影响。

项目成果

Possible future waves of SARS-CoV-2 infection generated by variants of concern with a range of characteristics.

• DOI: 10.1038/s41467-021-25915-7
• 发表时间: 2021-09-30
• 期刊: Nature communications
• 影响因子: 16.6
• 作者: Dyson L;Hill EM;Moore S;Curran-Sebastian J;Tildesley MJ;Lythgoe KA;House T;Pellis L;Keeling MJ
• 通讯作者: Keeling MJ

Risk of mortality in patients infected with SARS-CoV-2 variant of concern 202012/1: matched cohort study.

• DOI: 10.1136/bmj.n579
• 发表时间: 2021-03-09
• 期刊: BMJ (Clinical research ed.)
• 作者: Challen R;Brooks-Pollock E;Read JM;Dyson L;Tsaneva-Atanasova K;Danon L
• 通讯作者: Danon L

SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return

英国大学生中的 SARS-CoV-2 感染：2020 年 9 月至 12 月的经验教训以及未来学生返校的建模见解

• DOI: 10.17863/cam.73941
• 发表时间: 2021
• 作者: Enright J

SARS-CoV-2 infection in UK university students: lessons from September-December 2020 and modelling insights for future student return.

• DOI: 10.1098/rsos.210310
• 发表时间: 2021-08
• 期刊: Royal Society open science
• 影响因子: 3.5
• 作者: Enright J;Hill EM;Stage HB;Bolton KJ;Nixon EJ;Fairbanks EL;Tang ML;Brooks-Pollock E;Dyson L;Budd CJ;Hoyle RB;Schewe L;Gog JR;Tildesley MJ
• 通讯作者: Tildesley MJ

Non-pharmaceutical interventions and their relevance in the COVID-19 vaccine rollout in Saudi Arabia and Arab Gulf countries.

• DOI: 10.1016/j.idm.2022.08.005
• 发表时间: 2022-09
• 期刊: INFECTIOUS DISEASE MODELLING
• 影响因子: 8.8
• 作者: Althobaity, Yehya;Wu, Jianhong;Tildesley, Michael J.
• 通讯作者: Tildesley, Michael J.