基于统计学和基于主体的建模分析不同 SARS-CoV-2 变异株在英格兰的传播能力及不同干预措施的影响。

Statistical and agent-based modelling of the transmissibility of different SARS-CoV-2 variants in England and impact of different interventions.

机构信息

The Big Data Institute and the Pandemic Sciences Institute, Nuffield Department of Medicine, University of Oxford, Oxford.

The Queen's College, University of Oxford, Oxford.

出版信息

Philos Trans A Math Phys Eng Sci. 2022 Oct 3;380(2233):20210315. doi: 10.1098/rsta.2021.0315. Epub 2022 Aug 15.

The English SARS-CoV-2 epidemic has been affected by the emergence of new viral variants such as B.1.177, Alpha and Delta, and changing restrictions. We used statistical models and the agent-based model Covasim, in June 2021, to estimate B.1.177 to be 20% more transmissible than the wild type, Alpha to be 50-80% more transmissible than B.1.177 and Delta to be 65-90% more transmissible than Alpha. Using these estimates in Covasim (calibrated 1 September 2020 to 20 June 2021), in June 2021, we found that due to the high transmissibility of Delta, resurgence in infections driven by the Delta variant would not be prevented, but would be strongly reduced by delaying the relaxation of restrictions by one month and with continued vaccination. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.

英国 SARS-CoV-2 疫情受到了新病毒变体的出现（如 B.1.177、Alpha 和 Delta）和变化限制的影响。我们使用统计模型和基于代理的 Covasim 模型，于 2021 年 6 月估计，B.1.177 的传染性比野生型高 20%，Alpha 的传染性比 B.1.177 高 50-80%，Delta 的传染性比 Alpha 高 65-90%。在 Covasim 中使用这些估计值（2020 年 9 月 1 日校准至 2021 年 6 月 20 日），我们发现，由于 Delta 的高传染性，Delta 变体驱动的感染再次爆发将无法预防，但如果将放宽限制的时间推迟一个月，并继续接种疫苗，感染的再次爆发将会得到极大的抑制。本文是“模拟现实生活中的传染病的技术挑战及其克服这些挑战的实例”主题的一部分。