Evidence map›Paper›PMID 37342293›Full record

ArticleHealth science reports2023

Global analysis of the COVID-19 policy activity levels and evolution patterns: A cross-sectional study.

Meiqian Chen, Yucheng Dong, Xiaoping Shi, Jun Zhuang

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Article in Health science reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Meiqian ChenCenter for Network Big Data and Decision-Making, Business School Sichuan University Chengdu China.ORCID https://orcid.org/0000-0002-1122-3512
Yucheng DongCenter for Network Big Data and Decision-Making, Business School Sichuan University Chengdu China.ORCID 0000-0002-0028-6796
Xiaoping ShiIrving K. Barber School of Arts and Sciences University of British Columbia Kelowna British Columbia Canada.
Jun ZhuangDepartment of Industrial and Systems Engineering University at Buffalo Buffalo New York USA.ORCID 0000-0003-4830-6570

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Aims: 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? Methods: 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. Results: 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). Conclusion: 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.

Indexed as

activity levelCOVID‐19government policypolicy pattern

Identifiers

PMID37342293
PMCPMC10277603

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