Using GAM functions and Markov-Switching models in an evaluation framework to assess countries’ performance in controlling the COVID-19 pandemic

Abdinardo Moreira Barreto de Oliveira*, Jane Binner, Anandadeep Mandal, Logan Kelly, Gabriel J. Power

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusions: 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.
Original languageEnglish
Article number2173
Pages (from-to)1-14
Number of pages14
JournalBMC Public Health
Volume21
Issue number1
Early online date27 Nov 2021
DOIs
Publication statusPublished - Dec 2021

Bibliographical note

Scope/idea of the study, research design and methodology have been designed and estimated by me.
About the Journal: Impact factor is 4.003

Publisher Copyright:
© 2021, The Author(s).

Keywords

  • COVID-19
  • Statistical models
  • Epidemiological Monitoring
  • Epidemiological monitoring
  • Policy
  • SARS-CoV-2
  • Europe
  • Humans
  • Pandemics/prevention & control

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

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