Abstract

BackgroundThe 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.MethodsGeneralized 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.ResultsOur 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.ConclusionsThe 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.

Highlights

  • 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

  • As of September 15, 2021, more than 226 million people worldwide have been infected, and more than 4.1 million people have died since the first case was detected in December 2019 in China, according to data gathered by the Centre for Systems Science and Engineering [CSSE] at Johns Hopkins University [5, 6]

  • Some projects are being undertaken to monitor the evolution of COVID-19 across countries [5, 16, 17], which naturally leads to comparisons made by those who use the data. These “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

Read more

Summary

Introduction

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. Pandemics and major epidemic outbreaks are not unlikely events, contrary to what common sense may imply. A pandemic might have a catastrophic impact if it is not taken seriously, due to the non-linearity of its transmission in a world that is highly interconnected through long-range transportation [3, 4]. This is an ideal setting for the widespread transmission of COVID-19. As of September 15, 2021, more than 226 million people worldwide have been infected, and more than 4.1 million people have died since the first case was detected in December 2019 in China, according to data gathered by the Centre for Systems Science and Engineering [CSSE] at Johns Hopkins University [5, 6]

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call