COVID-19 Modelling Consortium: quantitative epidemiological predictions in response to an evolving pandemic

COVID-19 建模联盟：针对不断演变的流行病的定量流行病学预测

• 批准号: MR/V038613/1
• 负责人: Matthew Keeling
• 金额: $ 392.73万
• 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%2FV038613%2F1
• 关键词: COVID; 19; Modelling; Consortium; quantitative

Since the beginning of the COVID-19 pandemic in early 2020, mathematical and statistical modelling have been used to provide estimates of the epidemic in the UK, and to make short- and long-term predictions about the impact of interventions. The teams of epidemiological modellers and statisticians in our JUNIPER (Joint UNIversity Pandemic Epidemiological Research) consortium represent a core of committed and experienced university research groups that have dedicated themselves since February 2020 to generating predictions, forecasts and insights. These findings feed directly into the Scientific Pandemic Influenza Group on Modelling (SPI-M) and the Scientific Advisory Group for Emergencies (SAGE), both of whom advise the UK government on scientific matters relating to the UK's response to the pandemic. As part of SPI-M this group has brought together a range of analyses to underpin diverse policy decisions including early estimates of the scale of an uncontrolled epidemic, reasonable worst-case scenarios and the impact of reopening schools.Moving forward, critical research gaps remain unaddressed, and further translational work must be conducted to generate the necessary insights. The requested funding will ensure these key groups, with their extensive experience of delivering science for policy and deep understanding of this outbreak, will be able to continue and expand their activities. The Juniper consortium members will continue to respond to rapid requests from the UK government via SPI-M and SAGE, including providing weekly forecasts of the reproductive number R and growth rate in the UK and predictions of the likely impact of policy decisions and interventions. The research teams will be flexible and adaptive to the changing phases of the epidemic, and will proactively consider novel methodology, analysis or modelling that is required, as well as horizon scan the impact of new scientific findings and how this will impact on current and future modelling.The programme of work will address a core set of eight overarching questions that the consortium has identified as being important over the next 12-18 months: 1. How to best address issues around the storage, curation, and processing of the growing number of COVID-related data streams 2. Improving statistical and computational fundamentals for outbreaks 3. Refining methodology for the detection of hotspots or regions in need of greater control 4. Developing bespoke methods to analyse and model Surveillance, Test and Trace 5. Refining methodologies to determine risks posed by structured environments such as workplaces, care homes, hospitals, schools, universities 6. Producing realistic individual-scale modelling of contemporary social interactions 7. Implication of finer-scale individual-level characteristics and impacts of short- and long-term immunity in models. 8. Detailed retrospective analysis of the first wave.Our consortium will embed these scientific activities within an open and collaborative framework, including considerable public outreach so that scientific assumptions and findings are effectively communicated. Our consortium will be outward-facing and inclusive, helping to add value to a range of existing and new COVID-19 activities. We aim to build national capacity and the proposed programme will also contribute to training the next generation of applied epidemiological modellers.

自2020年初COVID-19疫情开始以来，数学及统计模型已被用于提供英国疫情的估计，并对干预措施的影响作出短期及长期预测。我们的JUNIPER（联合大学流行病流行病学研究）联盟中的流行病学建模师和统计学家团队代表了自2020年2月以来致力于生成预测，预测和见解的承诺和经验丰富的大学研究小组的核心。这些发现直接反馈给科学大流行性流感建模小组（SPI-M）和紧急情况科学咨询小组（SAGE），这两个小组就与英国应对大流行有关的科学问题向英国政府提供建议。作为SPI-M的一部分，该小组汇集了一系列分析，以支持各种政策决策，包括对不受控制的流行病规模的早期估计，合理的最坏情况以及重新开放学校的影响。展望未来，关键的研究差距仍然没有解决，必须进行进一步的转化工作，以产生必要的见解。所要求的资金将确保这些关键小组凭借其在为政策提供科学方面的丰富经验和对此次疫情的深入了解，能够继续并扩大其活动。Juniper联盟成员将继续通过SPI-M和SAGE响应英国政府的快速请求，包括每周提供英国生殖数量R和增长率的预测以及政策决策和干预措施可能产生的影响的预测。研究小组将灵活适应疫情的变化阶段，并将积极考虑所需的新方法、分析或建模，以及对新科学发现的影响及其对当前和未来建模的影响进行横向扫描。该工作方案将解决该联盟确定为未来12年重要的八个核心问题，18个月：1.如何最好地解决存储、管理和处理越来越多的COVID相关数据流的问题2.改进疾病暴发的统计和计算基础3.改进用于检测需要更大控制的热点或区域的方法4.开发定制的方法来分析和建模监视，测试和跟踪5。改进方法以确定工作场所、养老院、医院、学校、大学等结构化环境带来的风险。制作当代社会互动的现实个人规模模型7。更精细尺度的个体水平特征的含义以及模型中短期和长期免疫的影响。8.对第一波浪潮进行详细的回顾性分析。我们的联盟将把这些科学活动嵌入到一个开放和协作的框架中，包括大量的公众宣传，以便有效地传达科学假设和发现。我们的联盟将面向外部并具有包容性，有助于为一系列现有和新的COVID-19活动增加价值。我们的目标是建设国家能力，拟议的方案也将有助于培训下一代应用流行病学建模人员。

项目成果

Bivariate collocation for computing R0 in epidemic models with two structures

• DOI: 10.1016/j.camwa.2021.10.026
• 发表时间: 2021-09
• 期刊: Comput. Math. Appl.
• 作者: D. Breda;Simone De Reggi;F. Scarabel;R. Vermiglio;Jianhong Wu

Mid 2021 assessment of the Covid-19 pandemic - remaining epidemiological uncertainties. from The SARS-CoV-2 pandemic: remaining uncertainties in our understanding of the epidemiology and transmission dynamics of the virus, and challenges to be overcome

2021 年中期 Covid-19 大流行评估 - 仍存在流行病学不确定性。

• DOI: 10.6084/m9.figshare.16665959
• 发表时间: 2021
• 作者: Anderson R

COVID-19 transmission dynamics underlying epidemic waves in Kenya.

• DOI: 10.1126/science.abk0414
• 发表时间: 2021-11-19
• 期刊: Science (New York, N.Y.)
• 作者: Brand SPC;Ojal J;Aziza R;Were V;Okiro EA;Kombe IK;Mburu C;Ogero M;Agweyu A;Warimwe GM;Nyagwange J;Karanja H;Gitonga JN;Mugo D;Uyoga S;Adetifa IMO;Scott JAG;Otieno E;Murunga N;Otiende M;Ochola-Oyier LI;Agoti CN;Githinji G;Kasera K;Amoth P;Mwangangi M;Aman R;Ng'ang'a W;Tsofa B;Bejon P;Keeling MJ;Nokes DJ;Barasa E
• 通讯作者: Barasa E

The role of vaccination and public awareness in medium-term forecasts of monkeypox incidence in the United Kingdom

疫苗接种和公众意识在英国猴痘发病率中期预测中的作用

• DOI: 10.1101/2022.08.15.22278788
• 发表时间: 2022
• 作者: Brand S

Early signals of Omicron severity in sentinel UK hospitals

英国哨点医院 Omicron 严重程度的早期信号

• DOI: 10.21203/rs.3.rs-1203019/v1
• 发表时间: 2021
• 作者: Ahmad S