Multiresolution predictive dynamics of COVID-19 risk and intervention effects

COVID-19 风险和干预效果的多分辨率动态预测

• 批准号: MR/V038109/1
• 负责人: Samir Bhatt
• 金额: $ 70.93万
• 依托单位: Imperial College London
• 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%2FV038109%2F1
• 关键词: Multiresolution; predictive; dynamics; COVID; 19

SARS-CoV2 is a novel virus, and even as new data improves scientific insight, many uncertainties remain about key aspects of transmission. Throughout the pandemic, mathematical and statistical models of COVID-19 have had an important role in the analysis of epidemiological data, in forecasting incidence trends and in assessing the potential impact of different intervention strategies. Models developed by the Imperial College COVID-19 response team have been particularly influential, but the absence of detailed data on transmission patterns have necessitated important assumptions that limit their predictive performance. This project will (a) extend predictive models of transmission trends to include complex spatiotemporal correlation to better capture new seeding events and improve early identification of hotspots of transmission, (b) understand the causal effect of interventions on transmission and the limits to which this inference is possible, (c) systematically collate and analyse data on transmission in specific contexts (households, schools, workplaces and care homes) to derive specific transmission parameter estimates for those settings to be used to improve the ability of models to predict the impact of targeted non pharmaceutical interventions, (d) Understand how important epidemiological parameters are changing with time and what is driving these changes. This work will directly support the Imperial team's input into the UK COVID-19 response via the SPI-M, NERVTAG and SAGE committees and our partnerships with PHE and the Joint Biosecurity Centre (JBC).

SARS-CoV2 是一种新型病毒，尽管新数据提高了科学洞察力，但传播的关键方面仍然存在许多不确定性。在整个大流行期间，COVID-19 的数学和统计模型在流行病学数据分析、预测发病趋势和评估不同干预策略的潜在影响方面发挥了重要作用。帝国理工学院 COVID-19 应对团队开发的模型特别有影响力，但由于缺乏有关传播模式的详细数据，因此需要做出重要的假设，从而限制了其预测性能。该项目将（a）扩展传播趋势的预测模型，以包括复杂的时空相关性，以更好地捕捉新的播种事件并改进传播热点的早期识别，（b）了解干预措施对传播的因果影响以及这一推论的局限性(c) 系统地整理和分析特定环境（家庭、学校、工作场所和护理院）的传播数据，得出这些环境的具体传播参数估计值，用于提高模型预测传播的能力有针对性的非药物干预措施的影响，(d) 了解重要的流行病学参数随时间变化的程度以及驱动这些变化的因素。这项工作将直接支持帝国理工学院团队通过 SPI-M、NERVTAG 和 SAGE 委员会以及我们与 PHE 和联合生物安全中心 (JBC) 的合作伙伴关系对英国 COVID-19 应对工作的投入。

项目成果

A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe.

欧洲针对 SARS-CoV-2 的非药物干预数据集。

• DOI: http://dx.10.17863/cam.83113
• 发表时间: 2022
• 作者: Altman G

A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe.

欧洲针对 SARS-CoV-2 的非药物干预数据集。

• DOI: http://dx.10.1038/s41597-022-01175-y
• 发表时间: 2022
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Altman G

Estimating fine age structure and time trends in human contact patterns from coarse contact data: The Bayesian rate consistency model.

从粗略接触数据估计人类接触模式的精细年龄结构和时间趋势：贝叶斯速率一致性模型。

• DOI: http://dx.10.1371/journal.pcbi.1011191
• 发表时间: 2023
• 期刊: PLoS computational biology
• 影响因子: 4.3
• 作者: Dan S

Global disparities in SARS-CoV-2 genomic surveillance.

SARS-CoV-2 基因组监测的全球差异。

• DOI: http://dx.10.1101/2021.08.21.21262393
• 发表时间: 2021
• 期刊: the preprint server for health sciences
• 作者: Brito AF

A dataset of non-pharmaceutical interventions on SARS-CoV-2 in Europe.

欧洲针对 SARS-CoV-2 的非药物干预数据集。

• DOI: http://dx.10.17863/cam.84257
• 发表时间: 2022
• 作者: Altman G