对 48 个国家的全球 COVID-19 变体时间序列分析。

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

机构信息

School of Public Health, LKS Faculty of Medicine, The University of Hong Kong, Hong Kong, Hong Kong SAR, China.

Department of Finance, Business School, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China.

出版信息

Front Public Health. 2023 Apr 27;11:1085020. doi: 10.3389/fpubh.2023.1085020. eCollection 2023.

DOI:10.3389/fpubh.2023.1085020

PMID:37181700

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10173412/

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 ( = 0.002), while its rate of change was indeterministic ( = 0.052) and stationary ( = 0.024). Seasonal cointegration relationships between expected new case series by variants were found in 37 out of 48 countries ( < 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.

摘要

背景

冠状病毒病（COVID-19）大流行正在放缓，各国正在讨论预防措施是否仍然有效。本研究旨在调查 COVID-19 趋势中存在的一个特定特性，如果其关注变体是否协整，确定其可能转变为地方病。

方法

从 GISAID 数据库中获取 2020 年 5 月 2 日至 2022 年 8 月 29 日期间 48 个国家的每两周预期 COVID-19 变体新病例。虽然使用 Breusch-Pagan 检验检验病例系列的同方差性，但使用季节性分解获取两周全球新病例系列的趋势分量。然后使用单样本 Wilcoxon 符号秩检验检验趋势的百分比变化是否具有零均值对称性，使用增广 Dickey-Fuller 检验检验零均值平稳性，以确认全球 COVID 趋势的随机性。使用相同季节性调整的向量误差校正模型回归以获得每个国家的变体协整系列。使用增广 Dickey-Fuller 检验检验它们的平稳性，以确认国家内变体之间长期随机相互作用的常数。

结果

发现季节调整后的全球 COVID-19 新病例趋势系列存在异方差（ = 0.002），而其变化率不确定（ = 0.052）且稳定（ = 0.024）。在 48 个国家中的 37 个国家（ < 0.05）发现了变体预期新病例系列之间的季节协整关系，反映了大多数国家中不同关注变体引起的新病例数量的长期随机趋势。