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

TitleA global scale COVID-19 variants time-series analysis across 48 countries
Authors
KeywordsCOVID-19
global
mutation
strategy
time-series
variant of concern (VOC)
Issue Date27-Apr-2023
PublisherFrontiers Media
Citation
Frontiers in Public Health, 2023, v. 11 How to Cite?
Abstract

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.


Persistent Identifierhttp://hdl.handle.net/10722/342131
ISSN
2023 Impact Factor: 3.0
2023 SCImago Journal Rankings: 0.895
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChu, Rachel Yui Ki-
dc.contributor.authorSzeto, Kam Chiu-
dc.contributor.authorWong, Irene Oi Ling-
dc.contributor.authorChung, Pui Hong-
dc.date.accessioned2024-04-09T07:29:58Z-
dc.date.available2024-04-09T07:29:58Z-
dc.date.issued2023-04-27-
dc.identifier.citationFrontiers in Public Health, 2023, v. 11-
dc.identifier.issn2296-2565-
dc.identifier.urihttp://hdl.handle.net/10722/342131-
dc.description.abstract<p><strong>Background:</strong> 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.</p><p><strong>Methods:</strong> 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.</p><p><strong>Results:</strong> The trend series of seasonality-adjusted global COVID-19 new cases was found to be heteroscedastic (<em>p</em> = 0.002), while its rate of change was indeterministic (<em>p</em> = 0.052) and stationary (<em>p</em> = 0.024). Seasonal cointegration relationships between expected new case series by variants were found in 37 out of 48 countries (<em>p</em> < 0.05), reflecting a constant long-term stochastic trend in new case numbers contributed from different variants of concern within most countries.</p><p><strong>Conclusion:</strong> 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.</p>-
dc.languageeng-
dc.publisherFrontiers Media-
dc.relation.ispartofFrontiers in Public Health-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectCOVID-19-
dc.subjectglobal-
dc.subjectmutation-
dc.subjectstrategy-
dc.subjecttime-series-
dc.subjectvariant of concern (VOC)-
dc.titleA global scale COVID-19 variants time-series analysis across 48 countries-
dc.typeArticle-
dc.identifier.doi10.3389/fpubh.2023.1085020-
dc.identifier.scopuseid_2-s2.0-85159100492-
dc.identifier.volume11-
dc.identifier.eissn2296-2565-
dc.identifier.isiWOS:000984784500001-
dc.identifier.issnl2296-2565-

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