Chen Meiqian, Dong Yucheng, Shi Xiaoping, Zhuang Jun
Center for Network Big Data and Decision-Making, Business School Sichuan University Chengdu China.
Xiangjiang Laboratory Changsha China.
Health Sci Rep. 2023 Jun 18;6(6):e1350. doi: 10.1002/hsr2.1350. eCollection 2023 Jun.
Since the beginning of the coronavirus disease 2019 (COVID-19), a large number of government policies have been implemented worldwide in response to the global spread of COVID-19. This paper aims at developing a data-driven analysis to answer the three research questions: (a) Compared to the pandemic development, are the global government COVID-19 policies sufficiently active? (b) What are the differences and characteristics in the policy activity levels at the country level? (c) What types of COVID-19 policy patterns are forming?
Using the Oxford COVID-19 Government Response Tracker data set, we present a global analysis of the COVID-19 policy activity levels and evolution patterns from January 1, 2020 to June 30, 2022, based on the differential expression-sliding window analysis (DE-SWAN) algorithm and the clustering ensemble algorithm.
Within the period under study, the results indicate that (a) the global government policy responses to COVID-19 are very active, and the policy activity levels are significantly higher than those of global pandemic developments; (b) a high activity of policy is positively correlated to pandemic prevention at the country level; and (c) a high human development index (HDI) score is negatively correlated to the country policy activity level. Furthermore, we propose to categorize the global policy evolution patterns into three categories: (i) Mainstream (152 countries); (ii) China; and (iii) Others (34 countries).
This work is one of the few studies that quantitatively explores the evolutionary characteristics of global government policies on COVID-19, and our results provide some new perspectives on global policy activity levels and evolution patterns.
自2019年冠状病毒病（COVID-19）疫情开始以来，全球范围内实施了大量政府政策以应对COVID-19的全球传播。本文旨在开展一项数据驱动分析，以回答三个研究问题：（a）与疫情发展相比，全球政府的COVID-19政策是否足够积极？（b）国家层面的政策活动水平有哪些差异和特点？（c）正在形成哪些类型的COVID-19政策模式？
利用牛津COVID-19政府应对追踪数据集，我们基于差异表达-滑动窗口分析（DE-SWAN）算法和聚类集成算法，对2020年1月1日至2022年6月30日期间的COVID-19政策活动水平和演变模式进行了全球分析。
在研究期间，结果表明：（a）全球政府对COVID-19的政策应对非常积极，政策活动水平显著高于全球疫情发展水平；（b）国家层面的高政策活动与疫情防控呈正相关；（c）高人类发展指数（HDI）得分与国家政策活动水平呈负相关。此外，我们建议将全球政策演变模式分为三类：（i）主流（152个国家）；（ii）中国；（iii）其他（34个国家）。
这项工作是少数定量探索全球政府COVID-19政策演变特征的研究之一，我们的结果为全球政策活动水平和演变模式提供了一些新视角。
