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

基本信息

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

项目摘要

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，世界正遭受冠状病毒病 (COVID-19) 大流行的破坏，截至撰写本文时，全球已导致近 100 万确诊感染病例和约 50,000 人死亡。全球约三分之一的人口受到某种形式的限制，造成巨大的经济负担，对于许多国家来说，焦点已转向如何规划“退出战略”，摆脱世界上有史以来最严厉的一些社会疏远措施该项目将使用英国 COVID-19 疫情的实时数据来提供可靠的预测，评估模型预测未来流行病行为的能力。随着更多信息的出现，我们将调查我们的短期和长期预测在疫情爆发期间如何变化，这可能如何影响对应采取的适当控制措施的预测，以及何时以及如何放松这些政策。最后，考虑到未来感染浪潮的可能性，我们将使用我们的模型来确定应实施的最佳自适应控制政策，以减少因 COVID-19 爆发而导致的死亡人数，并尽量减少对疫情的影响。卫生服务。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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.)
影响因子：
0
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
期刊：
影响因子：
0
作者：
Enright J
通讯作者：
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

A comparative analysis of epidemiological characteristics of MERS-CoV and SARS-CoV-2 in Saudi Arabia.