This study focuses on the results of the G7 countries from the analysis of daily data from 184 countries of the world during the COVID-19 epidemic. After an increase in restrictions, there is an increase in new COVID-19 deaths. To understand the influences on number of deaths by country, the analysis reveals that per capita income is significantly positively correlated with mortality from COVID-19. This suggests that the epidemic first hit rich countries the hardest through the correlation to the human development index. This finding was contrary to what was predicted by the Global Health Security Index on pre-pandemic preparedness. Within affluent countries, deaths and cases were higher among socio-economic challenged populations. This was supported by the number of deaths that are positively influenced by the GINI index that is an indicator of disparity of income and wealth. The research indicates that after an increase in restrictions, there is an increase in new COVID-19 deaths and cases. This along with the finding on the stringency index, correlated with the stringency lag, point to the effectiveness of policies being negatively correlated due to a lag in implementation and partial application. Moreover, the uncertainty or the variability of the stringency index has a negative impact on mortality. The “Power Distance” by was used to understand individual’s reaction to restrictions indicated by the stringency index and the stringency lag, COVID-19 death numbers were also found to be positively influenced by a countries “Power Distance”. These findings are key to the improve policy management of the virus. The Delta plus and Lambda variant’s increased transmissibility and potential vaccine resistance increases the urgency for policy makers to understand and immediately enforce the stringency of regulations in consideration of their countries Power Balance index, and to reduce the stringency lag of their policies to increase the effectiveness in reducing the transmission of COVID-19.

COVID-19 Deaths Linked to Restrictions Stringency Lag: A G7 and Global Analysis, Implications for Public Policy

Marcella Lucchetta
Project Administration
;
2021-01-01

Abstract

This study focuses on the results of the G7 countries from the analysis of daily data from 184 countries of the world during the COVID-19 epidemic. After an increase in restrictions, there is an increase in new COVID-19 deaths. To understand the influences on number of deaths by country, the analysis reveals that per capita income is significantly positively correlated with mortality from COVID-19. This suggests that the epidemic first hit rich countries the hardest through the correlation to the human development index. This finding was contrary to what was predicted by the Global Health Security Index on pre-pandemic preparedness. Within affluent countries, deaths and cases were higher among socio-economic challenged populations. This was supported by the number of deaths that are positively influenced by the GINI index that is an indicator of disparity of income and wealth. The research indicates that after an increase in restrictions, there is an increase in new COVID-19 deaths and cases. This along with the finding on the stringency index, correlated with the stringency lag, point to the effectiveness of policies being negatively correlated due to a lag in implementation and partial application. Moreover, the uncertainty or the variability of the stringency index has a negative impact on mortality. The “Power Distance” by was used to understand individual’s reaction to restrictions indicated by the stringency index and the stringency lag, COVID-19 death numbers were also found to be positively influenced by a countries “Power Distance”. These findings are key to the improve policy management of the virus. The Delta plus and Lambda variant’s increased transmissibility and potential vaccine resistance increases the urgency for policy makers to understand and immediately enforce the stringency of regulations in consideration of their countries Power Balance index, and to reduce the stringency lag of their policies to increase the effectiveness in reducing the transmission of COVID-19.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3742016
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