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Open AccessArticle

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

by Chia-Lin Chang 1,2 and Michael McAleer 2,3,4,5,6,*
1
Department of Applied Economics and Department of Finance, National Chung Hsing University, Taichung 402, Taiwan
2
Department of Finance, Asia University, Taichung 41354, Taiwan
3
Discipline of Business Analytics, University of Sydney Business School, Sydney, NSW 2006, Australia
4
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, 3000 Rotterdam, The Netherlands
5
Department of Economic Analysis and ICAE, Complutense University of Madrid, 28223 Madrid, Spain
6
Institute of Advanced Sciences, Yokohama National University, Yokohama 240-8501, Japan
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2020, 17(9), 3161; https://doi.org/10.3390/ijerph17093161
Received: 6 April 2020 / Revised: 22 April 2020 / Accepted: 28 April 2020 / Published: 1 May 2020

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.
Keywords: global health security risk; pandemic; COVID-19; Pythagorean means; risk management; numerical rankings; ordinal rankings global health security risk; pandemic; COVID-19; Pythagorean means; risk management; numerical rankings; ordinal rankings

1. Introduction

There is no doubt that the COVID-19 disease, and the SARS-CoV-2 virus that causes it, have captured the world’s attention. With the exception of some countries where the leadership has tried to downplay, distort, and seemingly ignore its presence, most countries seem to have taken the coronavirus seriously from a public health and community safety perspective. Under such circumstances, it can be difficult to maintain a semblance of sanity when it is easy to entertain the alternative of panic.
At the time of writing, there is still no safe, reliable, efficient, and timely vaccine for the SARS-CoV coronavirus that caused SARS from 2002 to 2003, and for the MERS-CoV coronavirus that has continued to cause MERS since 2012. Therefore, it is difficult to feel optimistic about the discovery of a vaccine for COVID-19 in the foreseeable future.
For detailed medical studies on COVID-19, and government efforts to deal with the disease, see [1] Paules, Marston, and Fauci (2020), [2] del Rio and Malani (2020), [3] Parodi and Liu (2020), [4] Wang, Ng, and Brook (2020), [5] Wu and McCoogan (2020), [6] Sharfstein, Becker, and Mello (2020), [7] Wu, Chen, Cai et al. (2020), [8] Hoopman, Allegranzi, and Mehtar (2020), [9] Gostin, Hodge Jr., Wiley (2020), [10] Merchant and Lurie (2020), and [11] Yu, Ouyang, Chua et al. (2020), among others.
From a non-medical perspective, recent papers on risk management of COVID-19 include [12,13] McAleer (2020) and [14] Yang, Cheng, and Yue (2020).
Despite the volume of research and discussion on the health, medical, economic, financial, political, and travel advisory aspects of COVID-19, it is essential to enquire if an outbreak of the epidemic, which was belatedly classified as a global pandemic by the World Health Organization on 11 March 2020, might have been anticipated, given the well-documented history of SARS and MERS.
For there to be a foreseeable and predictable outcome based on observable and credible data, rather than on possibly misguided perceptions and “hunches” that do not necessarily rely on provable facts, it is essential to consider a well-documented source of publicly available information about what might have been anticipated about epidemics such as COVID-19. If various issues directly related to health security risk could have been predicted accurately, public health and medical contingency plans might have been prepared and activated well in advance of the onset of a pandemic such as COVID-19.
The purpose of this paper is to critically evaluate an important source of health security, namely the Global Health Security Index (2019). The data in the 2019 Report were available before the discovery of COVID-19 as pneumonia of unknown form in December 2019. Therefore, it is possible to evaluate how countries might have been prepared for a global epidemic, or pandemic, and acted accordingly.
The GHS Index numerical score rankings are obtained from [15] Global Health Security Index (2019), and are presented in Appendix A, while the GHS Index ordinal rankings are presented in Appendix B.
The remainder of the paper is as follows. Section 2 presents the Global Health Security (GHS) Index that is based on six broad categories. Section 3 provides an empirical evaluation of the numerical GHS scores and their respective rankings, as well as the corresponding ordinal rankings. Two regression models are estimated by least squares using both the numerical score and ordinal rankings, and optimal weights are assigned to each of the six categories in calculating the GHS Index. A conclusion and discussion of relevance are given in Section 4.

2. The Global Health Security (GHS) Index

Among the 140 questions, the GHS Index “prioritizes not only countries’ capacities, but also the existence of functional, tested, proven capabilities for stopping outbreaks at the source” (https://www.ghsindex.org/about/#About-the-Index-Project-Team).
The questions are organized across the following six categories:
  • Prevention: Prevention of the emergence or release of pathogens;
  • Detection and Reporting: Early detection and reporting for epidemics of potential international concern;
  • Rapid Response: Rapid response to and mitigation of the spread of an epidemic;
  • Health System: Sufficient and robust health system to treat the sick and protect health workers;
  • Compliance with International Norms: Commitments to improving national capacity, financing plans to address gaps, and adhering to global norms;
  • Risk Environment: Overall risk environment and country vulnerability to biological threats.
The GHS Index is a comprehensive assessment, developed as a collaboration between the Nuclear Threat Initiative, Johns Hopkins Center for Health Security, and the Economist Intelligence Unit, covering global health security capabilities in 195 countries. The GHS Index lists the countries that are best prepared for an epidemic or pandemic. “The average overall GHS Index score is 40.2 out of a possible 100. While high-income countries report an average score of 51.9, the Index shows that collectively, international preparedness for epidemics and pandemics remains very weak. Overall, the GHS Index finds severe weaknesses in a country’s abilities to prevent, detect, and respond to health emergencies; severe gaps in health systems; vulnerabilities to political, socioeconomic, and environmental risks that can confound outbreak preparedness and response; and a lack of adherence to international norms.” (https://www.ghsindex.org/report-model/). As part of China, Hong Kong was not included in the GHS Index as a country, while Taiwan was not included undoubtedly for political reasons. The data for the 195 countries are reported on pages 20–29 at: https://www.ghsindex.org/wp-content/uploads/2019/10/2019-Global-Health-Security-Index.pdf, which provides a numerical Average Overall score and separate numerical scores for each of the six categories. The seven numerical score rankings are obtained from Global Health Security Index (2019), and are reported in Appendix A, while the seven ordinal rankings are presented in Appendix B.

3. Empirical Evaluation

This section provides an empirical evaluation of the numerical GHS scores according to seven data series, namely the numerical scores for Average Overall and 6 categories, and the respective numerical score rankings, as well as the corresponding ordinal rankings for the Average Overall and six categories. Two empirical models are estimated using the numerical score rankings and ordinal rankings, with the GHS Index regressed on the respective numerical score rankings and ordinal rankings of each of the six categories.
The GHS Average Overall Index is the arithmetic mean numerical value that is calculated from the six numerical scores categories. The equal weight that is used for each category is 0.167. The abbreviations used are as follows: AO = Average Overall, PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, and RE = Risk Environment.
The descriptive statistics for the numerical score rankings are given in Table 1, which reports the mean, standard deviation, minimum and maximum values, and the range. The highest mean score is RE, and the lowest is HS. The highest standard deviation is DR, and the lowest is CO. The highest minimum is CO, and the lowest is HS. The highest maximum is DR, and the lowest HS. The largest range is DR and the lowest is CO.
It is instructive to present the 10 leading countries according to the AO numerical scores, together with the associated 6 category scores, namely:
  • (AO = 1) USA: PR = 1, DR = 1, RR = 1, HS = 1, CO = 1, RE = 19;
  • (AO = 2) UK: PR = 10, DR = 6, RR = 1, HS = 11, CO = 2, RE = 26;
  • (AO = 3) Netherlands: PR = 4, DR = 7, RR = 4, HS = 3, CO = 32, RE = 12;
  • (AO = 4) Australia: PR = 8, DR = 2, RR = 10, HS = 6, CO = 3, RE = 18;
  • (AO = 5) Canada: PR = 7, DR = 4, RR = 17, HS = 4, CO = 5, RE = 10;
  • (AO = 6) Thailand: PR = 3, DR = 15, RR = 5, HS = 2, CO = 12, RE = 93;
  • (AO = 7) Sweden: PR = 1, DR = 7, RR = 14, HS = 20, CO = 11, RE = 6;
  • (AO = 8) Denmark: PR = 5, DR = 7, RR = 19, HS = 5, CO = 28, RE = 17;
  • (AO = 9) South Korea: PR = 19, DR = 5, RR = 6, HS = 13, CO = 23, RE = 27;
  • (AO = 10) Finland: PR = 9, DR = 45, RR = 7, HS = 6, CO = 4, RE = 14.
The USA has the highest scores in five categories, but has an outlying score at 19 in Risk Environment (RE). The UK and Thailand also have apparent outliers in RE, with scores of 26 and 93, respectively. The Netherlands and Denmark have what seem to be outliers in Compliance (CO), at 32 and 28, respectively. Australia, Canada, and Sweden have relatively uniform scores in all six categories. South Korea has two outlying scores in CO and RE at 23 and 27, respectively. Finland has an outlier in Detection and Reporting (DR) at 45.
In the presence of outliers, the arithmetic mean can give a distorted measure of the central tendency of the individual components. Consequently, it is worth calculating the arithmetic mean (AM), geometric mean (GM), and harmonic mean (HM) using the numerical scores and ordinal rankings of each of the six categories for the 195 countries’ data for purposes of comparison. The GHS Index reported in the Global Health Security Index (2019) is calculated using the arithmetic mean, and is called AO.
The Pythagorean means are special cases of the generalized, power, or Hölder means, which can extend the three means discussed above to weighted power means, such as the quadratic and cubic means. In the interest of keeping the empirical analysis manageable, only the three Pythagorean means will be used in the paper.
The three classical Pythagorean means satisfy the inequality.
HM     GM     AM
The AM (=AO) of the numerical scores of the six categories is defined as:
AO = 1 6 i = 1 6 GHS i
where the subscript i = 1, 2, …, 6 represents PR, DR, RR, HS, CO and RE, respectively. The AM score might be referred to as GHS(AM), but we will continue to use AO, as given in the Global Health Security Index (2019).
Two new alternative GHS mean scores are as follows. The geometric mean of the GHS scores, which is an arithmetic mean of the logarithms of the six GHS scores when all the observations are positive, is defined as:
GM = ( i = 1 6 GHS i ) 1 / 6
where the subscript i = 1, 2, …, 6 represents PR, DR, RR, HS, CO and RE, respectively.
The harmonic mean, which measures the reciprocal of the arithmetic mean of the reciprocals of the six GHS scores, is defined as:
HM = 6 / ( i = 1 6 1 GHS i )
where the subscript i = 1, 2, …, 6 represents PR, DR, RR, HS, CO and RE, respectively.
In the empirical analysis, the new GHS average scores, GM and HM will be analyzed together with AO. According to the inequality in Equation (1), the three means satisfy.
HM     GM     AO
If the rankings of all three means in Equation (5) are similar, according to the pairwise correlation coefficients, the use of AO would seem to be reasonable, although arbitrary. However, if the pairwise correlations are dissimilar, then the use of AO would be questionable, especially given the outliers among the six GHS rankings. This is especially the case when the chosen rankings would depend on an arbitrary selection of a Pythagorean mean.
Returning to Table 1, the means satisfy the condition in Equation (5), as do the minimum values of the numerical scores. The standard deviations are in reverse order to the respective means, as is the range. The maximum values of the numerical scores are similar.
The correlations of the numerical score rankings are given in Table 2. The correlations among AO, GM, and HM are high in the range (0.982, 0.997), with GM and HM having the highest correlation at 0.997. The correlation between DR and RR is very high at 0.987. The next highest correlations are between AO and PR, HS, DR and RR, with all values above 0.89. The correlations of GM and HM with these categories are similar to those of AO. The lowest correlations are between RE and CO, DR and RR, with all values below 0.44.
The correlations of the ordinal rankings are given in Table 3, which qualitatively match the results in Table 2. The correlations among AO, GM, and HM are high in the range (0.950, 0.987), with GM and HM having the highest correlation at 0.987. The correlation between DR and RR is 0.999, which means that the two categories are virtually identical. The next highest correlations are between AO and HS, DR, RR and PR, with all values above 0.88. The correlations of GM and HM with these categories mirror those of AO. The lowest correlations are between RE and CO, RR and DR, with all values below 0.39.
The numerical score GHS Index is regressed on the numerical score rankings of the six categories in Table 4 to check if equal weights in the calculation of the GHS Index are justified. Given the high correlation between DR and RR in Table 2, it is not surprising that RR is statistically insignificant in the first column in Table 4. Each DR and RR are deleted in the second and third columns in Table 4, where the other variable is found to be statistically significant. The highest weights in each case are determined to be RR at 0.325, while the lowest weights are for PR at 0.186 and RE at 0.128. Therefore, Rapid Response has a large impact on the GHS Index numerical score.
The quantitative and qualitative results for GM and HM in Table 5 and Table 6 are quite different from those of AO in Table 4. Both DR and RR are significant for GM, whereas RR is insignificant for HM. The highest weight for GM is RR at 0.42, while the lowest weights are RE at 0.109 and CO at 0.13, which are markedly different from the weights for AO. The highest weights for HM is RR at 0.398 and HS at 0.366, while the lowest weights are RE at 0.076 and CO at 0.096, which are substantially lower than the corresponding weights for AO, as well as lower than for GM.
Overall, the range in the weights is much greater for both GM and HM than they are for AO, although RR has the highest weights for each of the three means.
The ordinal GHS Index is regressed on the ordinal rankings of the six categories in Table 7 to check for the optimal weights in the calculation of the ordinal GHS Index. Given the correlation of 0.999 between DR and RR in Table 3, it is not surprising that both categories are insignificant for AO in the first column when they appear simultaneously, while RR is only marginally significant. Deleting DR and RR in turn leads to the estimates in the second and third columns in Table 7, respectively, which show that the estimates for AO are identical, a result that is mirrored for GM and HM. With AO as the dependent variable, the highest weights are for DR and RR at 0.368, while the lowest is for RE at 0.142.
Broadly similar results hold for GM and HM in Table 8 and Table 9, respectively. The categories DR and RR also have the highest weights for GM and HM, but with higher numerical values of 0.382–0.383 for GM, and a lower numerical value of 0.341 for HM. However, unlike the case for AO where the lowest weight was for RE at 0.142, the lowest weight for GM is PR at 0.175. The lowest weight for HM is also PR, but at much lower weights of 0.118–0.119. It is clear that the ordinal rankings differ more widely across AO, GM and HM than they did for the GHS numerical score rankings.
Overall, Rapid Response and Detection and Reporting have strong impacts on the GHS Index ordinal ranking, regardless of whether the mean is AO, GM, or HM. While Risk Environment has the smallest impact on the GHS Index ordinal score for AO, Prevention has the smallest impact for GM and HM.

4. Conclusions

Given the volume of research and discussion on the health, medical, economic, financial, political, and travel advisory aspects of COVID-19, it is essential to enquire if an outbreak of the epidemic might have been anticipated, in light of the well-documented history of SARS and MERS. 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 well in advance of the onset of an epidemic such as COVID-19.
In this light, this paper critically evaluated an important source of health security, namely the Global Health Security Index (2019), which provided data before the discovery of COVID-19 in January 2020. Therefore, it is possible to evaluate how countries might have been prepared for a global epidemic, or pandemic, and acted accordingly.
The GHS Index numerical score is the arithmetic mean of the data for six categories, and hence uses equal weights for each category. The AO of the GHS Index score was 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 was justified. The highest weights were determined to be around 0.244–0.246, while the lowest weights were around 0.186–0.187.
Two alternative mean scores, namely the geometric mean (GM) and harmonic mean (HM), were also calculated from the numerical GHS Index scores. In addition to presenting alternative means of the GHS scores, they also provide a check of the robustness of the arithmetic mean score (AO) in the Global Health Security Index (2019). Although the three means suggested that Rapid Response had the largest impact, albeit with different weights, AO found the smallest impact from Prevention and Risk Environment, whereas both GM and HM found Compliance and Risk Environment had the smallest impacts.
The ordinal GHS Index was regressed on the ordinal rankings of the six categories to check for the optimal weights in the calculation of the ordinal GHS Index. The highest weight was 0.368, while the lowest was 0.142, so the estimated results are wider apart at 0.226 than for the numerical score rankings. The range was smaller for GM at 0.180 and for HM at 0.199.
Overall, Rapid Response and Detection and Reporting have the largest impacts on the GHS Index score, regardless of whether AO, GM, or HM were used, albeit with different weights. Risk Environment has the smallest impact on the GHS Index score when AO is used, whereas Prevention has the lowest impacts for GM and HM.
In preparing for an epidemic or pandemic, the order and importance of risk factors need to be known so that public health and medical contingency plans can be coordinated and activated effectively and in a timely manner. In such an environment, it is revealing that Rapid Response and Detection and Reporting have the largest impacts.

Author Contributions

Conceptualization, M.M.; methodology, M.M.; validation, C.-L.C. and M.M.; formal analysis, C.-L.C. and M.M.; investigation, C.-L.C. and M.M.; writing—original draft preparation, M.M.; writing—review and editing, C.-L.C. and M.M. Both authors have read and agreed to the published version of the manuscript.

Funding

The first author (Chia-Lin Chang) acknowledges the financial support of the Ministry of Science and Technology (MOST), Taiwan. The second author (Michael McAleer) wishes to thank the Australian Research Council and the Ministry of Science and Technology (MOST), Taiwan.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. GHS Index Numerical Score Rankings for 195 Countries.
Table A1. GHS Index Numerical Score Rankings for 195 Countries.
ScoreAOGMHMPRDRRRHSCORE
United States83.584.7084.3283.198.291.973.885.378.2
United Kingdom77.973.2672.7268.387.371.559.881.274.7
Netherlands75.672.9272.4573.78667.770.261.181.7
Australia75.576.9476.2768.997.379.763.57779.4
Canada75.377.8977.377096.479.167.774.782.7
Thailand73.268.9068.3975.78161.970.570.956.4
Sweden72.171.9770.5881.18667.149.371.384.5
Denmark70.471.9871.5272.98669.263.862.680.3
South Korea70.269.8468.8757.392.178.658.764.374.1
Finland68.765.3664.5268.561.649.760.875.481.1
France68.267.2266.6171.275.358.160.958.683
Slovenia67.265.5665.016773.755.154.972.173.7
Switzerland6761.2660.2652.759.14862.565.686.2
Germany6667.0765.8766.584.665.948.261.982.3
Spain65.965.5964.8352.98364.659.661.177.1
Norway64.663.0562.0468.258.647.958.564.487.1
Latvia62.964.2362.285697.379.347.351.167.2
Malaysia62.260.5960.0651.473.254.757.158.572
Belgium6161.9061.3963.562.550.260.559.778.2
Portugal60.356.4755.6852.850.545.4556377.3
Japan59.858.9757.9949.370.15246.67071.7
Brazil59.756.5555.1959.282.463.34541.956.2
Ireland5960.5558.9363.97860.240.252.877.4
Singapore58.755.4554.1956.264.550.641.447.380.9
Argentina58.658.6157.5541.474.957.754.968.860
Austria58.560.2759.0857.473.254.846.652.884.6
Chile58.356.1955.0056.272.754.339.351.570.1
Mexico57.656.7555.7745.571.252.246.973.957
Estonia5756.8654.0147.677.658.431.667.673.3
Indonesia56.654.8153.6950.268.151.739.472.553.7
Italy56.256.9555.2047.578.561.336.861.965.5
Poland55.455.9455.5350.961.749.948.958.967.9
Lithuania5557.8555.1243.581.562.934.472.167.8
South Africa54.852.7950.5944.881.562.83346.361.8
Hungary5452.8451.7956.455.547.336.658.968.2
New Zealand5449.2047.305536.733.945.259.477.2
Greece53.855.0153.6654.278.460.737.649.158.2
Croatia53.356.7256.0055.272.353.646.549.168.2
Albania52.951.8750.5643.874.356.535.95355.7
Turkey52.451.4250.8756.945.642.945.764.356.5
Serbia52.350.4350.1548.846.243.856.649.759.2
Czech Republic5251.9250.8251.150.746.437.458.974
Georgia5254.2353.1753.27557.838.35651.4
Armenia50.247.1345.0456.760.84925.750.150.4
Ecuador50.150.9949.7553.971.252.435.243.557.1
Mongolia49.550.5448.1437.677.358.230.852.660.8
Kyrgyz Republic49.346.8544.2429.764.750.829.864.856.1
Saudi Arabia49.351.9550.4234.374.456.944.850.659.7
Peru49.245.9144.9143.238.334.6456357.7
Vietnam49.148.6046.8049.557.447.528.364.653.4
China48.247.5847.104548.544.845.740.364.4
Slovakia47.949.7848.8253.54643.237.952.871.5
Philippines47.647.7246.9938.563.650.438.249.850.3
Israel47.348.4647.774452.446.642.241.568.8
Kenya47.145.7941.8345.968.651.820.767.140.7
United Arab Emirates46.741.2537.8849.331.630.122.963.472.4
India46.544.7844.3634.947.44442.747.754.4
Iceland46.344.0542.4135.337.234.246.443.281.2
Kuwait46.144.8144.2540.947.54436.542.261.5
Romania45.846.6945.8448.942.839.236.752.465.7
Bulgaria45.649.9948.9637.653.346.64161.566.3
Costa Rica45.145.5843.1444.25647.324.843.171.7
Russia44.341.0340.3142.934.132.137.652.651.4
Uganda44.337.1030.7542.750.345.111.665.435.5
Colombia44.242.6941.8937.241.737.134.360.151
El Salvador44.242.0638.1922.173.955.525.250.548
Luxembourg43.844.8442.763141.737.137.952.884.7
Montenegro43.745.4544.0136.555.44729.553.558.8
Morocco43.741.3740.0034.656.847.329.532.755.9
Panama43.742.6141.8240.544.641.935.135.363.8
Liechtenstein43.539.8036.0243.122.925.931.156.987.9
Myanmar43.439.5736.4030.359.248.619.559.138.2
Laos43.137.7333.1418.970.45219.445.946.8
Lebanon43.140.6738.2227.36250.123.849.345.5
Nicaragua43.142.2241.9141.739.934.945.951.841
Oman43.141.2239.2835.341.136.225.45665.7
Cyprus4343.3140.5846.444.942.321.949.169.6
Moldova42.944.4744.0546.542.939.936.456.747.1
Bosnia and Herzegovina42.840.1639.9236.741.737.338.337.850.8
Jordan42.140.0638.9431.842.940.227.848.655.8
Uruguay41.338.5236.404433.531.324.139.374.8
Qatar41.237.6336.4233.132.730.438.832.768
Kazakhstan40.740.1237.7658.828.228.62852.859.5
Ethiopia40.636.8535.7136.833.731.52965.833.6
Bhutan40.339.4738.6035.542.839.527.939.756.9
Madagascar40.134.3532.5130.141.937.819.255.432.4
Egypt39.936.3933.0736.541.536.615.746.457.5
Bahrain39.438.3337.013645.843.227.727.857.8
Cambodia39.236.0330.1228.657.747.8126038.5
North Macedonia39.139.3838.173741.736.825.444.857.7
Dominican Republic38.334.2131.4130.537.134.116.143.559.3
Sierra Leone38.235.9534.472545.84325.352.832.8
Zimbabwe38.237.5633.0031.465.651.514.745.939.2
Ukraine3836.9635.6538.136.533.42355.143.3
Senegal37.933.7531.4325.435.132.618.55748.2
Nigeria37.835.1133.0026.344.64219.956.733.7
Iran37.737.7737.1544.737.734.534.628.750.3
Malta37.337.8535.653532.930.523.649.172.3
Trinidad and Tobago36.630.1726.6028.114.721.723.755.164.4
Suriname36.532.2729.7723.336.733.916.544.852.7
Tanzania36.432.0624.7633.542388.255.444.7
Bolivia35.834.3931.144433.130.914.948.550.9
Paraguay35.736.7535.9839.534.632.428.235.355.9
Namibia35.634.0727.79324643.510.144.254.7
Côte d’Ivoire35.535.4332.6627.344.541.617.153.642.7
Ghana35.535.7534.7432.240.535.323.43851
Pakistan35.533.4931.7624.141.736.719.949.738.7
Belarus35.331.1229.6119.428.929.240.625.853
St. Lucia35.327.5419.7422.830.329.56.354.762.1
Cuba35.231.4625.8841.410.520.737.449.857.8
Liberia35.129.4126.1414.329.129.219.971.537.4
Nepal35.131.8730.8143.72225.928.133.544.7
Bangladesh3536.0631.9927.350.946.614.752.544
Mauritius34.933.0929.7727.342.339.115.129.166.2
Cameroon34.433.5032.0328.235.632.721.459.933.6
Uzbekistan34.331.3227.8542.619.424.71660.547.8
Azerbaijan34.235.6833.2930.84542.417.936.254.2
Gambia34.233.2931.882236.934.123.544.247.3
Rwanda34.234.2333.6533.83633.124.13843.6
Sri Lanka33.934.4931.5724.24340.516.941.756.7
Maldives33.830.0927.8521.825.527.818.145.558.3
Tunisia33.731.5230.5131.726.328.4243155.7
St. Vincent and The Grenadine3329.8026.712020.625195861.7
Micronesia32.824.8322.752114.221.718.836.353.1
Guatemala32.732.2627.1321.2504511.442.249.1
Guinea32.730.9423.672757.247.5847.831.3
Monaco32.729.1324.5811.123.3263135.383.1
Brunei32.630.7529.1224.830.529.724.223.366.7
Togo32.530.7525.5823.746.843.81046.337.6
Afghanistan32.332.6930.4723.544.842.12156.323.3
Tajikistan32.328.7927.8926.724.126.520.542.638.2
Niger32.234.5233.2932.544.441.321.945.528.5
Barbados31.927.3921.6433.319.124.38.54669.9
seychelles31.929.6424.039.833.431.119.947.171.1
Belize31.829.6724.763030.429.59.749.353
Turkmenistan31.831.9529.423138.634.814.439.345.1
Guyana31.727.4724.3127.920.324.812.349.350.5
Haiti31.531.6726.7431.548.344.710.648.428.9
Botswana31.129.7226.282228.228.913.346.362.4
San Marino31.130.3827.2922.333.931.916.22580.5
Eswatini (Swaziland)31.126.7520.0235.725.527.26.546.648.9
Bahamas30.625.9620.6824.721.825.57.94661.4
Andorra30.524.4619.7427.914.221.79.232.483.5
Lesotho30.227.6025.9524.41823.920.645.944.5
Burkina Faso30.124.1417.531833.330.95.644.842.6
Cabo Verde29.324.1120.1127.99.320.616.133.967.4
Antigua and Barbuda2924.6018.8717.819.124.57.455.165.2
Jamaica2926.4922.4120.124.326.81043.161.2
Mali2926.6924.4423.425.527.31353.232.1
Benin28.822.6916.6616.524.226.65.653.642.8
Chad28.824.3119.0223.236.533.76.646.223.7
Zambia28.727.8526.8024.521.925.520.33844.2
Mozambique28.129.4428.0826.529.329.31743.838.4
Malawi2827.7225.8625.523.326.215.350.737.6
Papua New Guinea27.823.7319.951031.830.211.641.438.7
Honduras27.626.3823.9821.627.728.41241.839.5
Grenada27.522.1017.358.618.624.210.346.462.9
Mauritania27.526.3122.499.939.534.81736.339.5
Central African Republic27.321.4720.091817.723.612.844.223
Comoros27.224.2820.9219.223.2269.451.636.5
Congo (Democratic Republic)26.523.7121.842425.127.111.845.920.1
Samoa26.421.9618.5120.214.121.19.230.766.1
St. Kitts and Nevis26.220.0014.898.715237.146.464.8
Sudan26.220.4816.9231.8718.714.337.633
Vanuatu26.122.0617.2124.51521.86.63857.4
Timor-Leste2623.7820.9918.225.728.39.733.941.5
Iraq25.826.7624.2322.142.238.711.829.529.2
Fiji25.721.9918.0924.616.423.17.527.459.1
Libya25.725.9522.3223.23633.29.13139
Angola25.224.0421.472417.923.610.941.442.2
Tonga25.121.5417.5619.81522.47.533.959
Dominica2419.5715.4811.210.720.78.549.354
Algeria23.622.4019.9825.71220.913.129.151.4
Congo (Brazzaville)23.617.7913.1817.6718.96.356.838.1
Djibouti23.221.2718.6516.31723.29.336.342.7
Venezuela2320.7917.7823.58.71912.942.238.2
Burundi22.819.5817.1525.111.420.88.937.628.3
Eritrea22.422.2519.8823.417.223.49.74033.2
Palau21.917.5514.208.28.819.611.53256.2
South Sudan21.720.8020.0022.615.92313.632.622.1
Tuvalu21.618.8415.8813.18.719.51228.658.7
Nauru20.815.4511.359.14.417.5123250.6
Solomon Islands20.717.7614.528.48.719.612.440.144
Niue20.515.3911.19114.417.49.129.957.9
Cook Islands20.418.5515.8210.98.819.714.329.950.5
Gabon2016.6113.2910.86.118.211.236.542.8
Guinea-Bissau2018.1813.711423.426.44.637.624.1
Syria19.914.829.5818.42.711.324.426.129.6
Kiribati19.214.3910.5810.74.417.87.332.345
Yemen18.516.4314.0815.1920.17.640.323.5
Marshall Islands18.210.935.991.94.417.67.230.752.3
São Tomé and Príncipe17.712.508.048.22.7167.233.544.6
North Korea17.517.5815.1619718.712.227.335.6
Somalia16.610.251.6815.821.525.10.328.515.9
Equatorial Guinea16.210.175.651.94.418.1533.543.6
Note: The data for AO, PR, DR. RR, HS, CO and RE are taken from the [15] Global Health Security Index (2019), pages 20–29, while the data for GM and HM are calculated in this paper.

Appendix B

Table A2. GHS Index Ordinal Rankings for 195 countries.
Table A2. GHS Index Ordinal Rankings for 195 countries.
RankAOGMHMPRDRRRHSCORE
United States1111111119
United Kingdom255106611226
Netherlands36847833212
Australia4228226318
Canada5337444510
Thailand6873151521293
Sweden74627920116
Denmark871057752817
South Korea99121955132327
Finland101214945459414
France111015621218449
Slovenia12171812272718829
Switzerland1315133448487183
Germany141116131010222911
Spain151619321111123224
Norway16141111494914222
Latvia171392523237948
Malaysia182531352829154533
Belgium191923154242103819
Portugal202933336161172622
Japan212329403534251334
Brazil2227241612123313594
Ireland232125141818416621
Singapore2431352340403810115
Argentina252427662323181470
Austria26181718282825665
Chile273339233030437838
Mexico28262249323324689
Estonia292228441919661530
Indonesia303026383737427106
Italy312830451616542955
Poland323440374444214145
Lithuania33202059131363846
South Africa3437325113146510764
Hungary353948225553564142
New Zealand35424427107107323923
Greece373534281717509280
Croatia383237263131279242
Albania3946515725255965100
Turkey404042207474302392
Serbia415250436968168674
Czech Republic424149366060524128
Georgia4236383122224553113
Armenia4454582146468183123
Ecuador4551552932326012688
Mongolia464343732020697269
Kyrgyz Republic4753541093939702096
Saudi Arabia474447892424358171
Peru49576060102102332684
Vietnam5049533951517421107
China5159655064643014158
Slovakia525057307071486636
Philippines5361687141414784124
Israel5455645458573713841
Kenya55484548363610316155
United Arab Emirates56585940126126982531
India57697987676636100103
Iceland585646841041042812813
Kuwait5970826866665713266
Romania606270428586557553
Bulgaria614756735757393150
Costa Rica6260675354538612934
Russia638389621161165072113
Uganda63676363626215219173
Colombia6571807591926435116
El Salvador65646215026268582129
Luxembourg674521102919248664
Montenegro686374795656716377
Morocco6880868853537117097
Panama6879876978796116160
Liechtenstein713846114914967481
Myanmar727273106474711140164
Laos7381761653434112113133
Lebanon73758311643439288134
Nicaragua7374786599992976154
Oman737384849797825353
Cyprus776571477676999240
Moldova7868754683845850132
Bosnia and Herzegovina79899178919145152119
Jordan8087101978383799699
Uruguay817872541191198914625
Qatar828688931241244417044
Kazakhstan83666117133134776672
Ethiopia847666771181187317175
Bhutan85931058385857814590
Madagascar86108111107909011355180
Egypt8710211679969612810486
Bahrain8888938172718018982
Cambodia897777110505014636162
North Macedonia90901037691948211984
Dominican Republic9112012710510510512512673
Sierra Leone929410012872738466179
Zimbabwe9284811013838132113158
Ukraine9497104721091109757146
Senegal9511411412611411411647128
Nigeria969696123787810750174
Iran97100985210310362186124
Malta98858586123123949232
Trinidad and Tobago99109106112170170935758
Suriname100135139144107107123119111
Tanzania10110710991898917555137
Bolivia1021161185412212113197118
Paraguay103112117701151157516197
Namibia104106113967070160122102
Côte d'Ivoire105101108116808011961149
Ghana10512213095989896148116
Pakistan105125131136919510786160
Belarus10813111916213213140193109
St. Lucia1081261221471291281896063
Cuba110989466177176528482
Liberia111925217613113110710170
Nepal1111301295815014976167137
Bangladesh1139192116595713274142
Mauritius114111110116878713018451
Cameroon1151059511111311310137175
Uzbekistan116104906415615612734130
Azerbaijan1171151201047575118160104
Gambia11713413515210610595122131
Rwanda1171291349011111289148144
Sri Lanka120121126135828212213791
Maldives12113914015413813811711779
Tunisia1221381379913613591177100
St. Vincent and The Grenadine1231181071601541541144665
Micronesia124163166157171170115157108
Guatemala1251231231566363155132126
Guinea12511097120525117699182
Monaco1258241180146147681618
Brunei1281271211291271278819549
Togo1291281281396868161107168
Afghanistan130103102140777710252191
Tajikistan130151156121144144105131164
Niger13211912594818199117186
Barbados1331241129215715817311139
seychelles1331139918712012010710237
Belize13513713610812812816388109
Turkmenistan135136138102101100134146135
Guyana13714314911315515514488121
Haiti138117115100656515898185
Botswana13913313315213313313810762
San Marino139996914911711712419416
Eswatini (Swaziland)13914114582138140188103127
Bahamas14214214113015215117711167
Andorra14395361131711701681737
Lesotho144150155134160160104113140
Burkina Faso145159164168121121191119151
Cabo Verde14614013211317817812516447
Antigua and Barbuda1471321241701571571815756
Jamaica14714514615914214216112968
Mali14714414414213813914064181
Benin15015415017214314319161147
Chad150156157145109109186110189
Zambia152155159132151151106148141
Mozambique153148154122130130120125163
Malawi15414615112514614612980168
Papua New Guinea155168170185125125152139160
Honduras156161168155135135146136156
Grenada15714714219015915915910461
Mauritania157149153186100100120157156
Central African Republic159176178168162161142122192
Comoros16015815816314814716677171
Congo (Democratic Republic)161165167137141141150113194
Samoa16215714715817317316817952
St. Kitts and Nevis16315214318916716618510457
Sudan16317217297185186135153178
Vanuatu16516416213216716918614887
Timor-Leste166174175167137137163164153
Iraq1671531521508888150183184
Fiji16816716013116516517919075
Libya168160165145111111170177159
Angola170171173137161161157139152
Tonga17116916316116716817916476
Dominica17216216117917617617388105
Algeria173170171124174174139184113
Congo (Brazzaville)17316614817118518518949167
Djibouti175181184173164164167157149
Venezuela176175177140182184141132164
Burundi177184185127175175172153187
Eritrea178177180142163163163144177
Palau17917817619218018115417594
South Sudan180183183148166166137172193
Tuvalu18117316917818218314618778
Nauru182187186188189192146175120
Solomon Islands183182182191182181143143142
Niue18417917418118919317018181
Cook Islands185180181182180180135181121
Gabon186188188183188188156156147
Guinea-Bissau186186187177145145194153188
Syria18818517916619419587192183
Kiribati189192192184189190182174136
Yemen190190190175179179178141190
Marshall Islands191189189195189191183179112
São Tomé and Príncipe192194194192194194183167139
North Korea193191191164185186145191172
Somalia194193193174153153195188195
Equatorial Guinea195195195195189189193167144
Note: The data are derived in this paper.

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Table 1. Descriptive statistics for numerical score rankings.
Table 1. Descriptive statistics for numerical score rankings.
ScoreMeanStd. Dev.MinMaxRange
PR34.7316.961.983.181.2
DR41.8823.812.798.295.5
RR38.4315.1211.391.980.6
HS26.4316.870.373.873.5
CO48.4812.6423.385.362.0
RE55.0316.2015.987.972.0
AO40.2014.5216.283.567.3
GM38.2115.5810.284.774.5
HM35.6916.711.784.382.6
Notes: 195 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average Overall. GM is the geometric mean GHS score, and HM is the harmonic mean GHS score; the mean AO score is taken from GHS Index (2019).
Table 2. Correlations of numerical score rankings.
Table 2. Correlations of numerical score rankings.
ScorePRDRRRHSCOREAOGMHM
PR1
DR0.772 *1
RR0.774 *0.987 *1
HS0.843 *0.741 *0.747 *1
CO0.636 *0.633 *0.633 *0.583 *1
RE0.576 *0.426 *0.430 *0.624 *0.311 *1
AO0.916 *0.894 *0.893 *0.914 *0.736 *0.647 *1
GM0.920 *0.916 *0.915 *0.914 *0.714 *0.631 *0.989 *1
HM0.918 *0.900 *0.899 *0.928 *0.693 *0.620 *0.982 *0.997 *1
Notes: * denotes significance at 1%; 195 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average, Overall. GM is the geometric mean GHS score, and HM is the harmonic mean GHS score.
Table 3. Correlations of ordinal rankings.
Table 3. Correlations of ordinal rankings.
RankPRDRRRHSCOREAOGMHM
PR1
DR0.750 *1
RR0.750 *0.999 *1
HS0.813 *0.720 *0.719 *1
CO0.594 *0.598 *0.598 *0.520 *1
RE0.550 *0.389 *0.388 *0.580 *0.285 *1
AO0.894 *0.885 *0.885 *0.887 *0.703 *0.612 *1
GM0.886 *0.884 *0.884 *0.869 *0.726 *0.658 *0.979 *1
HM0.845 *0.840 *0.840 *0.838 *0.711 *0.667 *0.950 *0.987 *1
Notes: * denotes significance at 1%; 195 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average, Overall. GM is the geometric mean GHS score, and HM is the harmonic mean GHS score.
Table 4. Numerical scores of AO regressed on six category numerical score rankings.
Table 4. Numerical scores of AO regressed on six category numerical score rankings.
VariablesAOAOAO
PR0.186 **
(0.016)
0.192 **
(0.017)
0.186 **
(0.016)
DR0.202 **
(0.029)
0.212 **
(0.009)
RR0.017
(0.044)
0.325 **
(0.017)
HS0.245 **
(0.015)
0.244 **
(0.016)
0.246 **
(0.015)
CO0.191 **
(0.013)
0.194 **
(0.014)
0.191 **
(0.013)
RE0.129 **
(0.010)
0.128 **
(0.010)
0.187 **
(0.010)
Intercept1.813 *
(0.867)
−1.864 **
(0.743)
2.013 **
(0.649)
R-squared0.9870.9840.987
F statistic2441.94 **1653.93 **2908.46 **
Notes: White’s heteroskedasticity-robust standard errors are given in parentheses; * and ** denote significance at 5% and 1%, respectively; 196 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average Overall.
Table 5. Numerical score of GM regressed on six category numerical score rankings.
Table 5. Numerical score of GM regressed on six category numerical score rankings.
VariablesGMGMGM
PR0.213 *
(0.009)
0.220 *
(0.011)
0.213 *
(0.009)
DR0.228 *
(0.015)
0.272 *
(0.006)
RR0.072 *
(0.023)
0.420 *
(0.013)
HS0.255 *
(0.009)
0.253 *
(0.010)
0.257 *
(0.010)
CO0.130 *
(0.007)
0.134 *
(0.009)
0.130 *
(0.008)
RE0.110 **
(0.007)
0.109 *
(0.008)
0.110 *
(0.007)
Intercept−0.590 *
(0.589)
−4.749 *
(0.639)
0.253 *
(0.515)
R-squared0.9960.9930.996
F statistic7255.06*2437.79*7496.75 *
Notes: White’s robust standard errors are given in parentheses; * denotes significance at 1%; 196 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average Overall.
Table 6. Numerical score of HM regressed on six category numerical score rankings.
Table 6. Numerical score of HM regressed on six category numerical score rankings.
VariablesHMHMHM
PR0.229 *
(0.017)
0.236 *
(0.017)
0.229 *
(0.017)
DR0.244 *
(0.028)
0.259 *
(0.013)
RR0.025
(0.042)
0.398 *
(0.022)
HS0.365 *
(0.022)
0.363 *
(0.022)
0.366 *
(0.022)
CO0.096 *
(0.015)
0.100 *
(0.016)
0.096 *
(0.015)
RE0.078 *
(0.015)
0.076 *
(0.015)
0.078 *
(0.015)
Intercept−2.022
(1.286)
−6.468 *
(1.187)
−1.731
(1.088)
R-squared0.9860.9830.986
F statistic2361.52 *1418.52 *2830.30 *
Notes: White’s robust standard errors are given in parentheses; * denote significance at 1%; 196 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average Overall.
Table 7. Ordinal score of AO regressed on six category ordinal rankings.
Table 7. Ordinal score of AO regressed on six category ordinal rankings.
VariableAOAOAO
PR0.214 *
(0.028)
0.213 *
(0.028)
0.214 *
(0.028)
DR−1.027
(0.804)
0.368 *
(0.024)
RR1.392
(0.800)
0.368 *
(0.024)
HS0.278 *
(0.027)
0.277 *
(0.028)
0.277 *
(0.028)
CO0.172 *
(0.017)
0.172 *
(0.017)
0.172 *
(0.017)
RE0.142 *
(0.017)
0.142 *
(0.017)
0.142 *
(0.017)
Intercept−16.82 *
(1.538)
−16.834 *
(1.555)
−16.828 *
(1.565)
R-squared0.9700.9700.970
F statistic1610.21 *1787.49 *1758.08 *
Notes: White’s heteroskedasticity-robust standard errors are given in parentheses; * denotes significance at 1%; 196 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average Overall.
Table 8. Ordinal score of GM regressed on six category ordinal rankings.
Table 8. Ordinal score of GM regressed on six category ordinal rankings.
VariableGMGMGM
PR0.146 *
(0.023)
0.146 *
(0.023)
0.146 *
(0.023)
DR−0.434
(0.494)
0.326 *
(0.016)
RR0.758
(0.489)
0.326 *
(0.016)
HS0.168 *
(0.020)
0.168 *
(0.020)
0.168 *
(0.020)
CO0.186 *
(0.013)
0.186 *
(0.013)
0.186 *
(0.013)
RE0.196 *
(0.015)
0.196 *
(0.015)
0.196 *
(0.015)
Intercept−8.701 *
(1.029)
−8.707 *
(1.027)
−8.705 *
(1.028)
R-squared0.9840.9840.984
F statistic3060.98 *3342.36 *3298.33 *
Notes: White’s robust standard errors are given in parentheses; * denotes significance at 1%; 196 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average Overall.
Table 9. Ordinal scores of HM regressed on six category ordinal rankings.
Table 9. Ordinal scores of HM regressed on six category ordinal rankings.
VariableHMHMHM
PR0.105 *
(0.053)
0.105 *
(0.053)
0.105 *
(0.053)
DR−1.196
(1.153)
0.304 **
(0.032)
RR1.496
(1.143)
0.304 **
(0.032)
HS0.172 **
(0.047)
0.171 **
(0.047)
0.171 **
(0.047)
CO0.213 **
(0.029)
0.213 **
(0.030)
0.213 **
(0.030)
RE0.242 **
(0.035)
0.241 **
(0.035)
0.241 **
(0.035)
Intercept−16.687 **
(2.051)
−16.701 **
(2.045)
−16.694 **
(2.046)
R-squared0.9700.9240.924
F statistic1610.21 **765.06 **758.56 **
Notes: White’s robust standard errors are given in parentheses; * and ** denotes significance at 5% and 1%, respectively; 196 observations; PR = Prevention, DR = Detection and Reporting, RR = Rapid. Response, HS = Health System, CO = Compliance, RE = Risk Environment, AO = Average Overall.
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