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The first 100 days of COVID-19 coronavirus – How efficient did country health systems perform to flatten the curve in the first wave?

Author

Listed:
  • Breitenbach, Marthinus C
  • Ngobeni, Victor
  • Ayte, Goodness

Abstract

In this novel paper, we make use of a non-parametric method known as Data Envelopment Analysis (DEA) to analyse the 31 most infected countries during the first 100 days since the outbreak of the COVID-19 coronavirus for the efficiency in containing the spread of the virus – a question yet to be answered in the literature. Our model showed 12 of the 31 countries in our sample were efficient and 19 inefficient in the use of resources to manage the flattening of their COVID-19 contagion curves. Among the worst performers were some of the richest countries in the world, Germany, Canada, the USA and Austria, with efficiency between 50 and 60 per cent - more inefficient than Italy, France and Belgium, who were some of those hardest hit by the spread of the virus.

Suggested Citation

  • Breitenbach, Marthinus C & Ngobeni, Victor & Ayte, Goodness, 2020. "The first 100 days of COVID-19 coronavirus – How efficient did country health systems perform to flatten the curve in the first wave?," MPRA Paper 8872, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:8872
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    File URL: https://mpra.ub.uni-muenchen.de/101440/1/MPRA_paper_8872.pdf
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    References listed on IDEAS

    as
    1. Sharon Hadad & Yossi Hadad & Tzahit Simon-Tuval, 2013. "Determinants of healthcare system’s efficiency in OECD countries," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 14(2), pages 253-265, April.
    2. Victor Ngobeni & Marthinus C. Breitenbach & Goodness C. Aye, 2020. "Efficiency of provincial public healthcare in South Africa," Working Papers 810, Economic Research Southern Africa.
    3. Pablo Cos & Enrique Moral-Benito, 2014. "Determinants of health-system efficiency: evidence from OECD countries," International Journal of Health Economics and Management, Springer, vol. 14(1), pages 69-93, March.
    4. Thomas A. Garrett, 2008. "Pandemic economics: the 1918 influenza and its modern-day implications," Review, Federal Reserve Bank of St. Louis, vol. 90(Mar), pages 74-94.
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    6. Avkiran, Necmi K., 2001. "Investigating technical and scale efficiencies of Australian Universities through data envelopment analysis," Socio-Economic Planning Sciences, Elsevier, vol. 35(1), pages 57-80, March.
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    14. Eric Wang & Eskander Alvi, 2011. "Relative Efficiency of Government Spending and Its Determinants: Evidence from East Asian Countries," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, vol. 1(1), pages 3-28, June.
    15. Andrés Ramírez Hassan, 2008. "Consequences of omitting relevant inputs on the quality of the data envelopment analysis under different input correlation structures," Documentos de Trabajo CIEF 010627, Universidad EAFIT.
    16. Anton Sorin Gabriel, 2013. "Technical Efficiency in the Use of Health Care Resources: A Cross-Country Analysis," Scientific Annals of Economics and Business, Sciendo, vol. 60(1), pages 1-12, July.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Pandemic; COVID-19; Flattening the Curve; Data Envelopment Analysis; Non-Pharmaceutical Interventions; Healthcare; Technical Efficiency; Healthcare system efficiency barometer.;

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • I10 - Health, Education, and Welfare - - Health - - - General

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