A global scale COVID-19 variants time-series analysis across 48 countries - Chu, Rachel Yui Ki; Szeto, Kam Chiu; Wong, Irene Oi Ling; Chung, Pui Hong (Frontiers in Public Health, 2023)
Background: COVID-19 pandemic is slowing down, and countries are discussing whether preventive measures remained effective. This study aimed to investigate a particular property of 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 2020-05-02 to 2022-08-29 were acquired. 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 inter-variant 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 cases series by variants were found among 37 out of 48 countries (p<0.05), reflecting a constant long-term stochastic trend in new cases number contributed from different variants of concern within most countries. Conclusion: Our result indicated that the new case long-term trends were random on a global scale, and stable within most countries, so the virus was unlikely to be eliminated but containable. Policymakers shall adapt to the transformation of the pandemic into an endemic.