Department of Business, Federal University of Technology of Paraná, Paraná, Brazil.
Birmingham Business School, University of Birmingham, Birmingham, UK.
BMC Public Health. 2021 Nov 27;21(1):2173. doi: 10.1186/s12889-021-11891-6.
The COVID-19 pandemic has initiated several initiatives to better understand its behavior, and some projects are monitoring its evolution across countries, which naturally leads to comparisons made by those using the data. However, most "at a glance" comparisons may be misleading because the curve that should explain the evolution of COVID-19 is different across countries, as a result of the underlying geopolitical or socio-economic characteristics. Therefore, this paper contributes to the scientific endeavour by creating a new evaluation framework to help stakeholders adequately monitor and assess the evolution of COVID-19 in countries, considering the occurrence of spikes, "secondary waves" and structural breaks in the time series.
Generalized Additive Models were used to model cumulative and daily curves for confirmed cases and deaths. The Root Relative Squared Error and the Percentage Deviance Explained measured how well the models fit the data. A local min-max function was used to identify all local maxima in the fitted values. The pure Markov-Switching and the family of Markov-Switching GARCH models were used to identify structural breaks in the COVID-19 time series. Finally, a quadrants system to identify countries that are more/less efficient in the short/long term in controlling the spread of the virus and the number of deaths was developed. Such methods were applied in the time series of 189 countries, collected from the Centre for Systems Science and Engineering at Johns Hopkins University.
Our methodology proves more effective in explaining the evolution of COVID-19 than growth functions worldwide, in addition to standardizing the entire estimation process in a single type of function. Besides, it highlights several inflection points and regime-switching moments, as a consequence of people's diminished commitment to fighting the pandemic. Although Europe is the most developed continent in the world, it is home to most countries with an upward trend and considered inefficient, for confirmed cases and deaths.
The new outcomes presented in this research will allow key stakeholders to check whether or not public policies and interventions in the fight against COVID-19 are having an effect, easily identifying examples of best practices and promote such policies more widely around the world.
COVID-19 大流行促使人们采取了多项举措来深入了解其传播特点，其中一些项目正在监测各国的演变情况，这自然导致了数据分析人员的比较。然而，大多数“一目了然”的比较可能具有误导性，因为各国 COVID-19 演变的曲线因潜在的地缘政治或社会经济特征而有所不同。因此，本文通过创建一个新的评估框架来帮助利益相关者充分监测和评估各国 COVID-19 的演变，从而为科学研究做出了贡献，该框架考虑了时间序列中的尖峰、“二次波”和结构断点。
使用广义加性模型对确诊病例和死亡人数的累积和每日曲线进行建模。根相对平方误差和解释方差百分比衡量模型对数据的拟合程度。使用局部最小-最大函数识别拟合值中的所有局部最大值。纯马尔可夫转换和马尔可夫转换 GARCH 模型家族用于识别 COVID-19 时间序列中的结构断点。最后，开发了一个象限系统来识别在短期/长期内控制病毒传播和死亡人数方面更有效/无效的国家。该方法应用于约翰霍普金斯大学系统科学与工程中心收集的 189 个国家的时间序列。
与全球增长函数相比，我们的方法在解释 COVID-19 的演变方面更有效，此外还将整个估计过程标准化为单一类型的函数。此外，它突出了几个拐点和体制转变时刻，这是由于人们对抗击大流行的承诺减弱所致。尽管欧洲是世界上最发达的大陆，但它却是大多数呈上升趋势的国家的所在地，这些国家被认为在确诊病例和死亡人数方面效率低下。
本研究提出的新结果将使主要利益相关者能够检查对抗 COVID-19 的公共政策和干预措施是否有效，轻松识别最佳实践的示例，并在全球范围内更广泛地推广这些政策。
