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新冠病毒(COVID-19)传播建模:风险评估与针对性缓解政策

Modelling COVID-19 contagion: risk assessment and targeted mitigation policies.

作者信息

Cont Rama, Kotlicki Artur, Xu Renyuan

机构信息

Oxford University, Mathematical Institute, Oxford, UK.

出版信息

R Soc Open Sci. 2021 Mar 31;8(3):201535. doi: 10.1098/rsos.201535.

Abstract

We use a spatial epidemic model with demographic and geographical heterogeneity to study the regional dynamics of COVID-19 across 133 regions in England. Our model emphasizes the role of variability of regional outcomes and heterogeneity across age groups and geographical locations, and provides a framework for assessing the impact of policies targeted towards subpopulations or regions. We define a concept of efficiency for comparative analysis of epidemic control policies and show targeted mitigation policies based on local monitoring to be more efficient than country-level or non-targeted measures. In particular, our results emphasize the importance of shielding vulnerable subpopulations and show that targeted policies based on local monitoring can considerably lower fatality forecasts and, in many cases, prevent the emergence of second waves which may occur under centralized policies.

摘要

我们使用一个具有人口和地理异质性的空间流行病模型,来研究新冠疫情在英格兰133个地区的区域动态。我们的模型强调了区域结果的变异性以及不同年龄组和地理位置之间的异质性所起的作用,并提供了一个框架,用于评估针对亚人群或地区的政策的影响。我们定义了一个用于疫情防控政策比较分析的效率概念,并表明基于本地监测的针对性缓解政策比国家层面或非针对性措施更有效。特别是,我们的结果强调了保护弱势群体的重要性,并表明基于本地监测的针对性政策可以大幅降低死亡预测,而且在许多情况下,可以防止在集中政策下可能出现的第二波疫情。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3bdb/8101016/b9e204983866/rsos201535f01.jpg

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