Waves, Lock-Downs, and Vaccines - Decision Support and Model with Superb Geographical and Sociological Resolution

浪潮、封锁和疫苗 - 具有卓越地理和社会学分辨率的决策支持和模型

• 批准号: EP/W011956/1
• 负责人: Frank Krauss
• 金额: $ 45.62万
• 依托单位: Durham University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FW011956%2F1
• 关键词: Waves; Lock; Downs; Vaccines; Decision

This proposal aims at further developing, validating, and deploying the JUNE model for the simulation of the spread of COVID-19 in the United Kingdom and the impact of medical and non-medical mitigation strategies. Recently constructed, JUNE combines superb geographical and sociological resolution in its virtual population model with a detailed, flexible and adjustable simulation of daily activities and the ability to include mitigation strategies and other interventions, to asses their effect. JUNE is already being used by NHS England to gain insights into the nature of the disease and the dynamics of its spreading, thereby informing operational planning and supporting decisions.In this proposal we will1. continue to provide valuable insights for the NHS, PHE, and the UK government, for example the impact of further complete or partial lock-downs, of the tier system and possible extensions and modifications, and of different vaccination protocols. This includes, in particular, the application of cutting-edge Bayesian methods for uncertainty quantification in projections of possible futures, and the development of robust data assimilation protocols to improve short-term predictions.2. further refine the population model, with special emphasis on the transmission dynamics and conditions in minorities and on the effect of socio-economic factors on infection and fatality rates. We will also include models for multiple infectious diseases affecting the population concurrently, to quantify the impact of seasonal flu the resulting demands on the health care system.As a by-product we will fully document the code and produce a convenient user-interface, to facilitate its use in future epidemics, thereby turning it into an enduring national asset.

该提案旨在进一步开发、验证和部署JUNE模型，以模拟COVID-19在英国的传播以及医疗和非医疗缓解策略的影响。JUNE最近建成，在其虚拟人口模型中结合了高超的地理和社会学分辨率，对日常活动进行了详细，灵活和可调整的模拟，并能够包括缓解策略和其他干预措施，以评估其效果。JUNE已经被英国国民保健署用于深入了解疾病的性质及其传播的动态，从而为运营规划和支持决策提供信息。继续为NHS，PHE和英国政府提供有价值的见解，例如进一步完全或部分封锁的影响，分层系统和可能的扩展和修改，以及不同的疫苗接种方案。这尤其包括在对可能的未来进行预测时应用最先进的贝叶斯方法对不确定性进行量化，以及制定强有力的数据同化协议以改进短期预测。进一步完善人口模型，特别强调少数民族的传播动态和条件，以及社会经济因素对感染率和死亡率的影响。我们还将包括同时影响人口的多种传染病的模型，以量化季节性流感对卫生保健系统的影响及其对卫生保健系统的需求。作为一个副产品，我们将完整地记录代码，并制作一个方便的用户界面，以便于在未来的流行病中使用，从而将其转化为持久的国家资产。

项目成果

June: open-source individual-based epidemiology simulation.

• DOI: 10.1098/rsos.210506
• 发表时间: 2021-07
• 期刊: Royal Society open science
• 影响因子: 3.5
• 作者: Aylett-Bullock J;Cuesta-Lazaro C;Quera-Bofarull A;Icaza-Lizaola M;Sedgewick A;Truong H;Curran A;Elliott E;Caulfield T;Fong K;Vernon I;Williams J;Bower R;Krauss F
• 通讯作者: Krauss F

Bayesian Emulation and History Matching of JUNE

六月的贝叶斯仿真和历史匹配

• DOI: 10.1101/2022.02.21.22271249
• 发表时间: 2022
• 作者: Vernon I

Epidemiological modelling in refugee and internally displaced people settlements: challenges and ways forward.

• DOI: 10.1136/bmjgh-2021-007822
• 发表时间: 2022-03
• 期刊: BMJ global health
• 影响因子: 8.1
• 作者: Aylett-Bullock J;Gilman RT;Hall I;Kennedy D;Evers ES;Katta A;Ahmed H;Fong K;Adib K;Al Ariqi L;Ardalan A;Nabeth P;von Harbou K;Hoffmann Pham K;Cuesta-Lazaro C;Quera-Bofarull A;Gidraf Kahindo Maina A;Valentijn T;Harlass S;Krauss F;Huang C;Moreno Jimenez R;Comes T;Gaanderse M;Milano L;Luengo-Oroz M
• 通讯作者: Luengo-Oroz M

A Mixed-Method Approach to Determining Contact Matrices in the Cox's Bazar Refugee Settlement

确定考克斯巴扎尔难民安置区接触矩阵的混合方法

• DOI: 10.1101/2022.11.22.22282641
• 发表时间: 2022
• 作者: Walker J