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用于传染病的动态微观模拟模型。

A dynamic microsimulation model for epidemics.

机构信息

Our World in Data at the Global Change Lab, London, UK.

Institute for Data Science and Artificial Intelligence \& Global Systems Institute, University of Exeter, UK; Joint Centre for Excellence in Environmental Intelligence, Exeter, UK.

出版信息

Soc Sci Med. 2021 Dec;291:114461. doi: 10.1016/j.socscimed.2021.114461. Epub 2021 Oct 18.

Abstract

A large evidence base demonstrates that the outcomes of COVID-19 and national and local interventions are not distributed equally across different communities. The need to inform policies and mitigation measures aimed at reducing the spread of COVID-19 highlights the need to understand the complex links between our daily activities and COVID-19 transmission that reflect the characteristics of British society. As a result of a partnership between academic and private sector researchers, we introduce a novel data driven modelling framework together with a computationally efficient approach to running complex simulation models of this type. We demonstrate the power and spatial flexibility of the framework to assess the effects of different interventions in a case study where the effects of the first UK national lockdown are estimated for the county of Devon. Here we find that an earlier lockdown is estimated to result in a lower peak in COVID-19 cases and 47% fewer infections overall during the initial COVID-19 outbreak. The framework we outline here will be crucial in gaining a greater understanding of the effects of policy interventions in different areas and within different populations.

摘要

大量证据表明,COVID-19 的结果以及国家和地方干预措施在不同社区的分布并不均衡。为了告知旨在减少 COVID-19 传播的政策和缓解措施,有必要了解我们的日常活动与 COVID-19 传播之间的复杂联系,这些联系反映了英国社会的特点。作为学术和私营部门研究人员之间合作的结果,我们引入了一种新颖的数据驱动建模框架以及一种计算效率高的方法来运行这种类型的复杂模拟模型。我们展示了该框架在评估不同干预措施效果方面的强大功能和空间灵活性,在对德文郡的第一个英国全国封锁的效果进行的案例研究中证明了这一点。在这里,我们发现,据估计,早期的封锁将导致 COVID-19 病例的峰值降低,并且在最初的 COVID-19 爆发期间,感染人数总体减少 47%。我们在这里概述的框架将对于在不同地区和不同人群中更好地了解政策干预措施的效果至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/75d2/8520832/b1c2563e5e96/gr1_lrg.jpg

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