Analysis of Policies Based on the Multi-Fuzzy Regression Discontinuity, in Terms of the Number of Deaths in the Coronavirus Epidemic

Wang, Xianghui and Chen, Chang and Du, Yan and Zhang, Yang and Wu, Chengliang (2021) Analysis of Policies Based on the Multi-Fuzzy Regression Discontinuity, in Terms of the Number of Deaths in the Coronavirus Epidemic. Healthcare, 9 (2). p. 116. ISSN 2227-9032

[thumbnail of healthcare-09-00116-v2.pdf] Text
healthcare-09-00116-v2.pdf - Published Version

Download (2MB)

Abstract

It has been a year since the outbreak of the coronavirus epidemic 2019 (COVID-19). In the face of the global epidemic, governments in all countries have taken different prevention measures, such as social isolation, mandatory health protection, and the closure of schools and workplaces. The situation of the epidemic has clearly varied from country to country. In this context, research on the impact of policies for the control of the spread of the global epidemic is of great significance. In this paper, we examined data from a sample of 212 countries between 31 December 2019, and 21 May 2020, using multi-fuzzy regression discontinuity. We found that developed countries had relatively low sensitivity to the policy stringency index; however, policy control measures had a significant effect on epidemic control. In addition, the trend analysis showed that the corresponding management and control came into play only after the policy stringency index reached 50 or the policy management reached level II, and the robustness was optimal at this time. Therefore, the governments in all countries should realize that epidemic prevention and control are of great importance. They can strengthen policy stringency to control the spread of the epidemic, considering their national conditions in terms of the economy and health system.

Item Type: Article
Subjects: Journal Eprints > Medical Science
Depositing User: Managing Editor
Date Deposited: 09 Feb 2023 07:14
Last Modified: 02 May 2024 09:12
URI: http://repository.journal4submission.com/id/eprint/815

Actions (login required)

View Item
View Item