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Alternative Global Health Security Indexes for Risk Analysis of COVID-19

Author

Listed:
  • Chia-Lin Chang

    (Department of Applied Economics and Department of Finance, National Chung Hsing University, Taichung 402, Taiwan
    Department of Finance, Asia University, Taichung 41354, Taiwan)

  • Michael McAleer

    (Department of Finance, Asia University, Taichung 41354, Taiwan
    Discipline of Business Analytics, University of Sydney Business School, Sydney, NSW 2006, Australia
    Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3000 Rotterdam, The Netherlands
    Department of Economic Analysis and ICAE, Complutense University of Madrid, 28223 Madrid, Spain)

Abstract

Given the volume of research and discussion on the health, medical, economic, financial, political, and travel advisory aspects of the SARS-CoV-2 virus that causes the COVID-19 disease, it is essential to enquire if an outbreak of the epidemic might have been anticipated, given the well-documented history of SARS and MERS, among other infectious diseases. If various issues directly related to health security risks could have been predicted accurately, public health and medical contingency plans might have been prepared and activated in advance of an epidemic such as COVID-19. This paper evaluates an important source of health security, the Global Health Security Index (2019), which provided data before the discovery of COVID-19 in December 2019. Therefore, it is possible to evaluate how countries might have been prepared for a global epidemic, or pandemic, and acted accordingly in an effective and timely manner. The GHS index numerical scores are calculated as the arithmetic (AM), geometric (GM), and harmonic (HM) means of six categories, where AM uses equal weights for each category. The GHS Index scores are regressed on the numerical score rankings of the six categories to check if the use of equal weights of 0.167 in the calculation of the GHS Index using AM is justified, with GM and HM providing a check of the robustness of the arithmetic mean. The highest weights are determined to be around 0.244–0.246, while the lowest weights are around 0.186–0.187 for AM. The ordinal GHS Index is regressed on the ordinal rankings of the six categories to check for the optimal weights in the calculation of the ordinal Global Health Security (GHS) Index, where the highest weight is 0.368, while the lowest is 0.142, so the estimated results are wider apart than for the numerical score rankings. Overall, Rapid Response and Detection and Reporting have the largest impacts on the GHS Index score, whereas Risk Environment and Prevention have the smallest effects. The quantitative and qualitative results are different when GM and HM are used.

Suggested Citation

  • Chia-Lin Chang & Michael McAleer, 2020. "Alternative Global Health Security Indexes for Risk Analysis of COVID-19," IJERPH, MDPI, vol. 17(9), pages 1-17, May.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3161-:d:353114
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    References listed on IDEAS

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    1. Chuanyi Wang & Zhe Cheng & Xiao-Guang Yue & Michael McAleer, 2020. "Risk Management of COVID-19 by Universities in China," JRFM, MDPI, vol. 13(2), pages 1-6, February.
    2. Michael McAleer, 2020. "Prevention Is Better Than the Cure: Risk Management of COVID-19," JRFM, MDPI, vol. 13(3), pages 1-5, March.
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    Cited by:

    1. Bing Wang & Yiwei Lyu, 2023. "Research on the Compilation of a Composite Index from the Perspective of Public Value—The Case of the Global Health Security Index," Sustainability, MDPI, vol. 15(19), pages 1-16, October.
    2. Christian M. Hafner, 2020. "The Spread of the Covid-19 Pandemic in Time and Space," IJERPH, MDPI, vol. 17(11), pages 1-13, May.
    3. Ștefan Cristian Gherghina & Daniel Ștefan Armeanu & Camelia Cătălina Joldeș, 2020. "Stock Market Reactions to COVID-19 Pandemic Outbreak: Quantitative Evidence from ARDL Bounds Tests and Granger Causality Analysis," IJERPH, MDPI, vol. 17(18), pages 1-35, September.
    4. Mete, Suleyman & Yucesan, Melih & Gul, Muhammet & Ozceylan, Eren, 2023. "An integrated hybrid MCDM approach to evaluate countries’ COVID-19 risks," Socio-Economic Planning Sciences, Elsevier, vol. 90(C).

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