Modeling COVID-19 cases statistically and evaluating their effect on the economy of countries

HANNS ANIBAL DE LA FUENTE MELLA, Rolando Rubilar, Karime Chahuán-Jiménez, VICTOR ELISEO LEIVA SANCHEZ

Research output: Contribution to journalArticlepeer-review

19 Scopus citations

Abstract

COVID-19 infections have plagued the world and led to deaths with a heavy pneumonia manifestation. The main objective of this investigation is to evaluate the performance of certain economies during the crisis derived from the COVID-19 pandemic. The gross domestic product (GDP) and global health security index (GHSI) of the countries belonging–or not–to the Organization for Economic Cooperation and Development (OECD) are considered. In this paper, statistical models are formulated to study this performance. The models’ specifications include, as the response variable, the GDP variation/growth percentage in 2020, and as the covariates: the COVID-19 disease rate from its start in March 2020 until 31 December 2020; the GHSI of 2019; the countries’ risk by default spreads from July 2019 to May 2020; belongingness or not to the OECD; and the GDP per capita in 2020. We test the heteroscedasticity phenomenon present in the modeling. The variable “COVID-19 cases per million inhabitants” is statistically significant, showing its impact on each country’s economy through the GDP variation. Therefore, we report that COVID-19 cases affect domestic economies, but that OECD membership and other risk factors are also relevant.

Original languageEnglish
Article number1558
JournalMathematics
Volume9
Issue number13
DOIs
StatePublished - 1 Jul 2021
Externally publishedYes

Keywords

  • Data science
  • Econometric modeling
  • Economic crisis
  • Global health security index
  • Gross domestic product
  • OECD
  • SARS-CoV-2

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