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，即使新数据改善了科学见解，许多不确定性仍然涉及传播的关键方面。在整个大流行中，Covid-19的数学和统计模型在流行病学数据，预测发生率趋势以及评估不同干预策略的潜在影响方面具有重要作用。由帝国学院共同19号响应团队开发的模型特别有影响力，但是缺少有关传输模式的详细数据需要限制其预测性能的重要假设。该项目将（a）扩展传输趋势的预测模型，以包括复杂的时空相关性，以更好地捕获新的播种事件并改善对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种推断的限制是可能的，（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）的传输数据，以得出特定的传输参数估计值，以提高这些设置，以提高模型的能力预测目标的影响非药物干预措施，（d）了解流行病学参数随着时间而变化的重要性以及驱动这些变化的原因。这项工作将直接支持帝国团队通过SPI-M，神经和SAGE委员会以及我们与PHE和PHE和联合生物安全中心（JBC）的合作伙伴关系的帝国团队的意见。

项目成果

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

• DOI: 10.1371/journal.pcbi.1011191
• 发表时间: 2023-06
• 期刊: PLoS computational biology
• 影响因子: 4.3

Risk of hospitalisation associated with infection with SARS-CoV-2 omicron variant versus delta variant in Denmark: an observational cohort study.

• DOI: 10.1016/s1473-3099(22)00154-2
• 发表时间: 2022-07
• 期刊: LANCET INFECTIOUS DISEASES
• 影响因子: 56.3
• 作者: Bager, Peter;Wohlfahrt, Jan;Bhatt, Samir;Stegger, Marc;Legarth, Rebecca;Moller, Camilla Holten;Skov, Robert Leo;Valentiner-Branth, Palle;Voldstedlund, Marianne;Fischer, Thea K.;Simonsen, Lone;Kirkby, Nikolai Soren;Thomsen, Marianne Kragh;Spiess, Katja;Marving, Ellinor;Larsen, Nicolai Balle;Lillebaek, Troels;Ullum, Henrik;Molbak, Kare;Krause, Tyra Grove
• 通讯作者: Krause, Tyra Grove

Spatial and temporal fluctuations in COVID-19 fatality rates in Brazilian hospitals.

• DOI: 10.1038/s41591-022-01807-1
• 发表时间: 2022-07
• 期刊: NATURE MEDICINE
• 影响因子: 82.9
• 作者: Brizzi, Andrea;Whittaker, Charles;Servo, Luciana M. S.;Hawryluk, Iwona;Prete Jr, Carlos A.;de Souza, William M.;Aguiar, Renato S.;Araujo, Leonardo J. T.;Bastos, Leonardo S.;Blenkinsop, Alexandra;Buss, Lewis F.;Candido, Darlan;Castro, Marcia C.;Costa, Silvia F.;Croda, Julio;de Souza Santos, Andreza Aruska;Dye, Christopher;Flaxman, Seth;Fonseca, Paula L. C.;Geddes, Victor E. V.;Gutierrez, Bernardo;Lemey, Philippe;Levin, Anna S.;Mellan, Thomas;Bonfim, Diego M.;Miscouridou, Xenia;Mishra, Swapnil;Monod, Melodie;Moreira, Filipe R. R.;Nelson, Bruce;Pereira, Rafael H. M.;Ranzani, Otavio;Schnekenberg, Ricardo P.;Semenova, Elizaveta;Sonnabend, Raphael;Souza, Renan P.;Xi, Xiaoyue;Sabino, Ester C.;Faria, Nuno R.;Bhatt, Samir;Ratmann, Oliver
• 通讯作者: Ratmann, Oliver

Quantifying Changes in Vaccine Coverage in Mainstream Media as a Result of the COVID-19 Outbreak: Text Mining Study.

• DOI: 10.2196/35121
• 发表时间: 2022-07
• 期刊: JMIR infodemiology

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

• DOI: 10.1038/s41597-022-01175-y
• 发表时间: 2022-04-01
• 期刊: Scientific data
• 影响因子: 9.8
• 作者: Altman G;Ahuja J;Monrad JT;Dhaliwal G;Rogers-Smith C;Leech G;Snodin B;Sandbrink JB;Finnveden L;Norman AJ;Oehm SB;Sandkühler JF;Kulveit J;Flaxman S;Gal Y;Mishra S;Bhatt S;Sharma M;Mindermann S;Brauner JM
• 通讯作者: Brauner JM