A global scale COVID-19 variants time-series analysis across 48 countries.

Background: The coronavirus disease (COVID-19) pandemic is slowing down, and countries are discussing whether preventive measures have remained effective or not. This study aimed to investigate a particular property of the trend of COVID-19 that existed and if its variants of concern were cointegrated, determining its possible transformation into an endemic. Methods: Biweekly expected new cases by variants of COVID-19 for 48 countries from 02 May 2020 to 29 August 2022 were acquired from the GISAID database. While the case series was tested for homoscedasticity with the Breusch-Pagan test, seasonal decomposition was used to obtain a trend component of the biweekly global new case series. The percentage change of trend was then tested for zero-mean symmetry with the one-sample Wilcoxon signed rank test and zero-mean stationarity with the augmented Dickey-Fuller test to confirm a random COVID trend globally. Vector error correction models with the same seasonal adjustment were regressed to obtain a variant-cointegrated series for each country. They were tested by the augmented Dickey-Fuller test for stationarity to confirm a constant long-term stochastic intervariant interaction within the country. Results: The trend series of seasonality-adjusted global COVID-19 new cases was found to be heteroscedastic (p = 0.002), while its rate of change was indeterministic (p = 0.052) and stationary (p = 0.024). Seasonal cointegration relationships between expected new case series by variants were found in 37 out of 48 countries (p < 0.05), reflecting a constant long-term stochastic trend in new case numbers contributed from different variants of concern within most countries. Conclusion: Our results indicated that the new case long-term trends were random on a global scale and stable within most countries; therefore, the virus was unlikely to be eliminated but containable. Policymakers are currently in the process of adapting to the transformation of the pandemic into an endemic.

Analysis of the Effectiveness of Measures on the COVID-19 Vaccination Rate in Hong Kong.

BACKGROUND: The World Health Organization has set a target of at least 70% of the global population being vaccinated by the middle of 2022. There are only 17 countries that achieved a 70% vaccination rate (VR). This study aims to analyze the effectiveness of public policies to increase the COVID-19 VR. METHODS: vaccination figures of all eligible population groups in Hong Kong from 22 February 2021 to 23 January 2022, were extracted for analysis. Weekly acceleration in the VR (AVR) was calculated as a measure of policy effectiveness. A total of 13 identified measures were classified into four policy categories: eligibility, accessibility, incentives, and restrictions. Age-weighted AVR (AWAVR) was compared by age group and policy presence vs. absence using Mann-Whitney U tests. RESULTS: the AWAVR means across age groups ranged from -1.26% to +0.23% (p = 0.12) for eligibility; accessibility ranged from +0.18% to +1.51% (p < 0.0001); incentives ranged from +0.11% to +0.68% (p < 0.0001); and restrictions ranged from +0.02% to +1.25% (p < 0.0001). CONCLUSIONS: policies targeting accessibility, incentives, and restrictions are effective at increasing the VR. These results may serve as a policy reference.