mathematics
Article
Global Food Security, Economic and Health Risk Assessment of
the COVID-19 Epidemic
Sándor Kovács 1, * , Mohammad Fazle Rabbi 2 and Domicián Máté 3,4
1
2
3
4
*
Citation: Kovács, S.; Rabbi, M.F.;
Máté, D. Global Food Security,
Faculty of Economics and Business Management, University of Debrecen, H-4032 Debrecen, Hungary
Ihrig Károly Doctoral School, University of Debrecen, H-4032 Debrecen, Hungary; drrabbikhan@gmail.com
Faculty of Engineering, University of Debrecen, H-4032 Debrecen, Hungary; mate.domician@eng.unideb.hu
College of Business and Economics, University of Johannesburg, Johannesburg 2006, South Africa
Correspondence: kovacs.sandor@econ.unideb.hu
Abstract: This study addresses the complexity of global pandemic (COVID) exposures and explores
how sustainable development relates to economic and health risks and food security. Multiple factor
analysis (MFA) is applied to compute the links among blocks of variables, and results are validated by
random sampling with bootstrapping, exhaustive and split-half techniques, and analysis of variance
(ANOVA) to test the differences of the MFA factors within the different stages of competitiveness.
Comparing the MFA factors suggests that higher competitiveness is correlated with better food
security and natural resilience and the tremendous economic downturn; the most competitive
countries have lower exposures to health risks. In addition, the risk of pandemics appears to be lower
with well-established public health care (HC) system services and good health for the population.
The study also underlines that the economic and health systems are unfortunately inadequate to deal
with a crisis of this magnitude. Although the countries least affected by the epidemic are the most
competitive, they cannot protect people and the economy effectively. Formulating appropriate global
responses is a challenge, but the results may lead to more nuanced findings regarding treatment
policies that can be addressed at the country level.
Economic and Health Risk
Assessment of the COVID-19
Epidemic. Mathematics 2021, 9, 2398.
Keywords: competitiveness; economic risk; food security; health risk; MFA method; risk assessment;
sustainable development
https://doi.org/10.3390/math
9192398
Academic Editor: David Carfì
Received: 1 September 2021
Accepted: 24 September 2021
Published: 27 September 2021
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This article is an open access article
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Attribution (CC BY) license (https://
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4.0/).
1. Introduction
The latest SARS influenza pandemic in Wuhan, China, which became known in
December 2019, broke out of Pandora’s box. Since then, COVID-19 has continued to spread
around the world. Due to the infectivity of the disease, global transport has been limited
and even restricted across countries and quarantines. Global trade difficulties caused
significant distribution issues, and the loss of agricultural labour prevented continuous
production resulted in food supply disruptions. Most importantly, some sort of panic
began to emerge in input-oriented and consumer-dependent companies, leading to more
expensive food prices [1]. The food supply has declined due to increased uncertainty and
market anomalies. In addition to the social and economic downturn, the epidemic has
another necessary consequence. Global financial markets also responded to the changes,
and stock indices began to decline, anticipating a protracted crisis [2].
In addition to endangering human health, the epidemic also threatened employment,
food security and sustainable economic development. The pandemic recovery represents
another grand challenge and an excellent opportunity to achieve the 2030 Agenda and
the Sustainable Development Goals (SDGs) [3]. SDGs are pathways that can encompass
almost every aspect of the well-being of humanity and would provide both prosperous
lives for all people and ensure the health of the planet [4]. SDGs are required efforts to
promote policies and approaches, such as SDG2 relates to “end (zero) hunger, achieve
food security and improved nutrition, and promote sustainable agriculture”. Food security
Mathematics 2021, 9, 2398. https://doi.org/10.3390/math9192398
https://www.mdpi.com/journal/mathematics
Mathematics 2021, 9, 2398
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“ . . . is the state in which people at all times have physical, social, and economic access
to sufficient and nutritious food that meets their dietary needs for a healthy and active
life [5]”. SDG8 denotes “decent work and economic growth” that improve living standards
and support the elderly population with policies related to pension, social assistance and
life-long education. SDG3, “ensure healthy lives and promote well-being for all at all
age”, aims to support the health care (HC) systems to treat chronic and infectious diseases
through prevention, vaccination and rehabilitation.
The main goal of this study is to gain novel insights into whether and how SDGs
can be implemented by exploring the interrelationships between the different dimensions of economic and epidemiological risks and development stages. The novelty of
this exploratory approach is that global risk assessment is treated mathematically as a
multidimensional construct, which may lead to more nuanced findings on how economic
and health treatment policies could be addressed at the country level.
This study assumes a correspondence between the vulnerability of COVID-19 and
the development stages of countries. A cross-country comparative framework is used to
test this assumption, and datasets were published by SolAbility Sustainable Intelligence,
European Intelligence Unit, and OurWorldinData for 2019–2021. Section 2 contains the
design of selected variables and methodology. A multiple factor analysis (MFA) is applied
to compute the links among blocks of variables. The advantage of MFA is calculating
correlations between the indices in each dimension concerning the competitiveness stages.
Sections 3 and 4 presents the results and discussion based on the developed hypothesis,
and the paper ends with conclusions stemming from the results that the economic and
health systems are unfortunately inadequate to deal with a crisis of this magnitude.
2. Literature Review
Responses to SDGs and SARS as a double helix pose a severe threat to human health
and the economic environment and cannot be treated with different approaches in the
future [6]. The evolution of the disease and its economic and social impacts are highly
uncertain, making it difficult to understand the costs and risks of contagion in formulating
appropriate responses. There are several direct and indirect channels through which outbreaks of infectious (HIV/AIDS, SARS) diseases affect the development of economies [7].
Individual perceptions of the risks associated with SARS may also be high, especially in
the early stages of an epidemic when no vaccine and antiviral drugs are lacking [8]. The
economic risk of an epidemic is quite different from the risk of morbidity and mortality.
Besides, economic risk is determined by biohazard and exposure, resilience, and vulnerability, which has different spatial variability [9]. The population and economic activities are
exposed to the pathogen or indirectly for epidemic-changing behaviour [10]. The vulnerability suggests that SARS may unfavourably affect the exposed economy. The concept of
disease vulnerability was introduced as the impact of natural disasters on the population,
as a kind of test of how societies can make a vulnerable population resilient when such
a disaster covers the entire economy [11]. The traditional approach uses information on
deaths (mortality) and condition of illnesses (cases) that prevent work [12]. However, it
applies to the pathology of individual health capacity and autonomy within communities
and cultures, especially social groups [13]. The speed of the process determines the degree
of resilience and when the economic system returns to full recovery [14].
Several previous relevant studies have attempted to evaluate the global and economic
impacts of SARS [15–17], but have only recently focused on identifying socioeconomic
drivers or environmental inhibitors of SARS-CoV-2 (COVID-19) transmission [18,19]. Since
then, academics have been analysing country-specific studies [20–22] due to the relatively
small number of global cases. Although SARS was highly contagious, global economic and
social costs were substantial and not limited only to the affected countries. To the best of
our knowledge, the complexity of global pandemic exposure has been underestimated in
how economic development and competitiveness relates to COVID-19-vulnerability, taking
into account the economic and health risks, food safety and natural resilience.
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3. Materials and Methods
3.1. Design of Variables
This paper carefully and systematically selected variables related to economic and
health care risk exposure, food security, and COVID-vulnerability. Table 1 presents the
variables and their descriptions. The data were collected in United Nations (UN) countries
(112) between 2019 and 2021. During the COVID crisis the population deaths and infections
were used from the first known (2019) cases until 2021. The global food security, economic
and health risks were related to the year (2019) before the onset of the crisis.
Table 1. Description of indicators.
Pillar
Variables
Economic
Independence
Fall from the Cliff
Economic Risk
Equality Resilience
Status of
Government Finance
Financial Markets
Volatility
HC (Health Care)
Infrastructure
HC System
Health Risk
Risk Group Size
General Population
Health
Affordability
Global
Food Security
Availability
Quality and Safety
Natural Resources
and resilience
Deaths
COVID
Vulnerability
Cases
Description
Dependency on imports/exports,
employment in service and agricultural
sectors, as well as innovation capabilities
A potential 10% reduction in GDP is
significantly higher in absolute terms in
high-income countries.
Internal inequality measurements (income
and asset share hold by the lowest 20%, 40%
and 60%)
The current state of government debt and
interest payments
Private and corporate debt, as dependency on
stock markets (measured as the value of stock
and annual stock turnover)
Availability of HC infrastructure (number of
beds, doctors and nurses per capita; mortality
rate from non-communicable diseases)
HC spending per capita, out-of-pocket
affordability for the lower-income segment,
government share on spending, the mortality
rate of lifestyle diseases
Elderly population measured by the
percentage of the population over age 65, 50,
and 40
Life expectancy, mortality rates due to air
pollution, and general fitness level measured
through average standardised
body-mass-index
The ability of consumers to purchase food,
their vulnerability to price shocks and the
presence of policies to support them
The availability ensures sufficient food supply,
low risk of supply disruption, and high
national capacity to disseminate food and
research efforts to expand agricultural output.
Variety and nutritional quality of average
diets and the safety of food
Exposure to the impacts of climate change; its
susceptibility to natural resource risks; and
how the country is adapting to these risks
Total number of deaths per 1 million residents
from the first case (2019) till 18/04/2021
Total number of infections per 1 million
residents from the first case (2019) till
18/04/2021
Measurement
Source
(1–5) 1—least
(1–5) 1—least
(1–5) 1—least
[23]
(1–5) 1—least
(1–5) 1—least
(1–5) 5—worst
(1–5) 5—worst
[23]
(1–5) 5—eldest
(1–5) 5—worst
Score (0–100)
100 = best
Score (0–100)
100 = best
[24]
Score (0–100)
100 = best
Score (0–100)
100 = best
capita/thousand
number of
cases/million
[25]
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The economic and health risk pillars contain various World Health Organization
(WHO) and World Bank (WB) indices collected by the SolAbility Sustainable Intelligence [23]. Quantitative, measurable data sets on straightforward health care and economic
performance highlight the potential impacts of the COVID-19 epidemic on altered countries in terms of global downturn and competitiveness. The perception of global sustainable competitiveness is based on the conviction that economic and human development
and ecological sustainability are essential elements for high productivity and a welfare
society [26].
The initial pillar exposed to health risks is general health, demographics, and access to
health infrastructure and funding indicators. Health risks are shaped by the response issues
and capabilities of each country. The general health and mortality of the average persons,
the size of the risk groups (elderly), the availability of health infrastructure (hospital beds,
doctors, nurses), and the health sector’s financial situation reflect the appropriate risks
before the pandemic.
The risks of the economic slump pillar involve the five key areas that determine the
economy’s resilience, i.e., the independence of global markets, the state of public finances,
exposure to financial market fluctuations, internal income inequality, and the ‘height’ of the
economic fall. The economic decline depends upon crisis management and the response to
pandemic crises.
Global food security indicators were provided by the European Intelligence Unit [24].
The pillar considers food affordability, availability, quality and safety, and natural resources
and resilience, all of which affect the incidence of food insecurity across countries. Affordability measures the capacity and cost of food payments and vulnerability to dependence
control against external price shocks. Accessibility refers to the risk of food supply to
expand agricultural production and reduce food waste and loss, and political instability.
The food quality and safety index measures sound quality and varied nutrition. The
natural resources and resilience assess exposure to the effects of a changing climate and its
sensitivity to the risks of natural resources (i.e., climate disasters, water scarcity and land
quality issues, and population pressures).
Data on COVID-19 pandemic indicators (deaths and infections) were obtained from
the updated OurWorldinData research and statistics [25]. Classification by economic
development based on the stages set out in the World Economic Forum (WEF) Global Competitiveness Report [27]. In line with the economic theory of Porter’s stages of development,
in the first stage (Stage 1), factor-driven countries compete based on their endowments,
primarily (unskilled) labour and natural resources [28]. As the economy becomes competitive, productivity growth and wages increase. Countries then enter the efficiency-driven
development phase (Stage 3). Lastly, when countries enter the innovation-driven phase
(Stage 5), wages will rise so high that they will only be sustainable if companies use the
most advanced production processes. Transitions between each stage are captured by
Stages 2 and 4 (from 1 to 3 and 3 to 5).
3.2. Multiple Factor Analysis (MFA)
The MFA method was first introduced by Thurstone [29] and later described by
Escoffier and Pagès [30]. MFA has recently been used in various scientific fields, such as
agriculture [31,32]; business management and economics [33,34]; chemometrics [35,36];
health and medicine [37]; mathematics and statistics [38,39]. The method is advantageous
in analysing a given table of inter-correlated variables that are in multiple blocks.
Denote by Xo x v = xij the raw dataset (table) that contains observations (o) in the
rows and variables (v) across the columns. xij is the entry in the i-th row, and j-th column
of the table, where 1 ≤ i ≤ o; 1 ≤ j ≤ v and o is the number of objects, v is the number of
variables. Each observation is weighted by importance called mass. All masses are equal
to 1/o, and the mass matrix is diagonal and denoted by M. The raw dataset of the same
observations consists of K variable blocks (which are sub-tables), and X[k] denotes the k-th
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block (sub-table), where 1 ≤ k ≤ K. The original data table can be divided into K sub-tables
according to:
h
i
X = X[1] , . . . , X[ k ] , . . . , X[ K ] ,
(1)
Each block should be made comparable by setting the average of each column to 0
e and the
and the sum of the squared values to 1. Denote the preprocessed data matrix by X
e [k] . The analysis aims to represent X
e with a linear combination of new
k-th block in it by X
components, which explains the maximum variance by considering the common structure
of the blocks.
The MFA analysis consists of three stages: (1) singular value decomposition (SVD) of
each block; (2) the first singular value of each block is used to normalise the blocks; (3) and
e is performed. In the first stage, SVD is
generalised SVD of the total preprocessed data (X)
performed according to the following:
e [ k ] = S [ k ] V T D[ k ] ,
X
[k]
(2)
where D[k] is a diagonal matrix containing the singular values (which can be treated as
standard deviations) in the main diagonal. S[k] and V[k] satisfies the following equation (U
stands for the unit matrix, T denotes the transposition):
U = S[Tk] S[k] = V[Tk] V[k] ,
The first singular value is denoted in D by d11 , and w1 =
block. For the k-th block (wk =
1
),
d2kk
(3)
1
d211
is the weight for the first
where dkk is the k-th singular value in D. Let W be a
diagonal weight matrix containing the w1 , . . . , wk , . . . , wK weights. The rule is that all the
variables in the same block have the same weight.
e
Finally, the generalised SVD of all preprocessed data is performed on X:
e = PLQ T = FQ T , with P T MP = Q T WQ = U,
X
(4)
where L is a diagonal matrix containing the singular values, and F = PL is the so-called
factor scores matrix that describes the observations, Q is the so-called loading matrix of
the variables. The columns of the F matrix (usually the first two) are the MFA factors (also
known as dimensions) used to represent the observations, and the Q columns contain the
variables. F can be obtained from Equation (4), such as follows:
e
F = XWQ,
(5)
Due to the entire block structure, MFA provides a unique concept of partial factor
scores that allows each observation to be positioned for different groups of variables [40]:
F = ∑k F[k] =
1
e [k ] · w[k ] · Q[k ] ,
∑ K·X
K k
(6)
where F[k] is the partial factor score of the k-th block.
MFA is preferred for the graphical display of observations and construction of diverse
clusters [35]. The analysis results also represent the relationships between the objects
(factor scores) and the groups of variables examined. This mode of representation is called
the correlation circle (See Figure 1), which depicts the relationship between raw variables
and factor scores in a unit circle [40]. MFA is advantageous for analysing observations
defined by diverse groups of variables, and the method will be even more valuable when
the dataset is large and more complex [41]. Formal analysis was performed by FactoMiner,
an R software package for multivariate analysis [42].
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Figure 1. Correlation plot of individual blocks (food security, risk exposures and COVID-vulnerability) and the correlations
of each indicator to the first two dimensions. DIM1 corresponds to 51.83%, DIM2 explains 12.08% of inertia.
4. Results
The correlation circle plot discloses the common structure of the four examined pillars
(groups of variables) in the MFA model. Figure 1 shows how MFA factors (dimensions)
correlate with specific risk factors. The figure also suggests that the first dimension (DIM1)
is well linked to global food security indices (primarily food affordability, quality and security, natural resources and resilience), economic risks (primarily due to falling from the cliff
and equality resilience), and health risk factors, most notably the size of the risk group, HC
infrastructure. The second dimension (DIM2) can be interpreted as COVID-vulnerability
measured by the total number of deaths and infected cases during the pandemic crisis.
The second dimension is also related to the health status of the general population. The
eigenvalue of DIM1 corresponds to 51.83% of the inertia; DIM2 explains 12.08%. MFA
explained a relatively high 64% of the total variance, which is considered satisfactory.
The correlation circle is also needed when interpreting objects in the global space of
MFA. The direction of the vectors is essential. For example, countries with higher DIM1
factor scores have higher food security and, therefore, a higher proportion of older people
due to their development. These countries are more likely to experience a possible 10%
decline in GDP during the crisis. In addition, countries with lower or negative factor scores
have higher internal inequalities and exposure to HC infrastructure risk. In other words,
income or wealth inequality, mortality rates are high within these nations, and the ratio of
doctors and staff per bed is low. However, they are less exposed to the potentially massive
decline in GDP, and the proportion of older people is low.
Moreover, it is possible to examine how each country are related to different sources
of risk (economic and health exposure, global food security, COVID-vulnerability) based
on their stages of development. The competitiveness related stages are only used as
an additional qualitative variable in the MFA analysis, as it differs from the variable
blocks (sources of risk). The partial factor score of each index is projected onto a so-called
consensus map according to the supplementary variable (stage). Each stage is marked with
a dot, and for each stage, a line links the factor score of the stage to the partial factor score
of a particular risk type.
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Figure 2 shows each development stage as a single point on a two-dimensional map
representing the overall level of competitiveness. Each level of stages increases in parallel
with the first (DIM1) dimension. The partial points allow positioning nations according to
different sources of risk. In this way, it can be evaluated how each source of risk affects
the overall level of the competitiveness stages. The global space map can be allocated into
four quarters based on the two dimensions. Each quadrant contains countries belonging to
different stages and with different risk profiles.
Figure 2. The contributions (partial factor scores) of each block (risk exposures) to the development stages.
The first quadrant mainly includes Stage 4 countries with relatively high vulnerabilities, the most exposed to COVID. Stages 2 and 3 efficiency-driven countries are primarily
in the second quarter and face higher health risks. The difference between Stage 2 and 3
countries is that Stage 2 has a lower degree of COVID-vulnerability. Stage 1 factor-driven
countries are in the third quadrant and are more exposed to food insecurity and health
risks (lower life expectancy with higher mortality, the lower health status of the general
population). Quadrant 4 includes Stage 5 innovation-driven countries. These countries
are less exposed to health risks, and food security is not an issue here. However, there is a
higher risk of COVID-vulnerability. The figure shows that most of the variance is due to
pandemic vulnerability, health risk exposure, food security and resilience.
Figure 3 shows the two-factor indicators of each country. The first one (dimension) is
related to competitiveness, which shows the development of the different stages. Countries
are represented in different colours according to their level of development stages. The
right side of the (DIM1) dimension includes developed countries with better HC systems
and food security but more significant economic risks. The left side of this dimension
shows less developed countries with lower levels of food security, HC infrastructure
and competitiveness. The second dimension is related to exposure to health risks and
COVID-vulnerability. The upper side of the (DIM2) vertical axis is linked to more COVIDvulnerable countries at high health risk.
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Figure 3. MFA map of different countries on COVID vulnerability and competitiveness.
The following table (Table 2) shows the rankings of the best and worst-performing
countries based on MFA factors.
Table 2. MFA factors ranking by COVID-vulnerability, economic and health risk and food security.
Type
Best
Rank
Country
COVID
Country
1
2
3
4
5
Bahrain
Israel
Qatar
Serbia
Kuwait
United Arab
Emirates
Netherlands
Turkey
Jordan
Sweden
−−1.66
−1.28
−
−1.28
−−1.14
−−0.89
−
−0.86
−−0.71
Tanzania
Uganda
Burundi
Chad
Congo DR
6
7
8
9
10
10
9
8
Worst
South Africa
Brazil
United
Kingdom
Madagascar
Nigeria
Rwanda
Cameroon
Malawi
−−0.59
−0.56
−
−0.47
−
− 0.42
0.44
South Korea
Finland
0.59
Denmark
7
Slovakia
0.65
6
5
Ecuador
Italy
0.65
0.67
United Arab
Emirates
Australia
Belgium
Economic
Risk
Country
−2.99
−2.85
−2.76
−2.6
−2.38
Japan
Singapore
South Korea
Viet Nam
Cambodia
−2.35
− −2.35
− −2.29
−2.29
−
−2.29
−
− 2.12
2.18
Bangladesh
−
−
−
−
−
Country
Food
Security
Finland
Ireland
Sweden
Norway
Switzerland
2.84
2.74
2.71
2.67
2.62
Denmark
2.57
Netherlands
Canada
USA
Austria
2.46
2.42
2.42
2.41
Azerbaijan
Kazakhstan
−2.21
−
−2.21
−
−1.99
−
−1.73
−
−1.57
−
−1.44
−
−1.43
−1.21
−
−1.21
−
−1.03
−
1.28
−
1.35
Guinea
Haiti
2.19
Slovakia
1.35
Venezuela
−2.35
−2.47
−
−2.74
−
2.19
South Africa
1.36
Burundi
−
−2.92
2.2
2.21
Hungary
Romania
1.56
1.65
India
Ethiopia
Rwanda
Nepal
Health
Risk
4
Peru
0.72
Germany
2.35
Serbia
1.88
3
2
Hungary
Bulgaria
1.05
1.10
Sweden
Netherlands
2.55
2.66
1.88
2.27
1
Mexico
1.37
Switzerland
3.32
Bulgaria
Ukraine
Russian
Federation
2.32
Mozambique
Madagascar
Sierra
Leone
Chad
Congo DR
Yemen
−2.95
−
−2.98
−3.02
−
−
−3.10
−
−3.64
−
−3.75
−
−
As discussed earlier, the most COVID-vulnerable countries are South American
(Ecuador, Peru) and Mexico, thus South Africa, and most Eastern-European countries
(Hungary, Slovakia, and Bulgaria) with high health risks exposure have one of the worst
positions. The risk appears to be lower in the countries with well-established public health
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services (Israel, Netherlands). The more economically developed OECD countries appear
to be at a tremendous economic downturn due to the current global pandemic. The less
developed (African) ones are exposed to below-average economic risk, but this is to some
extent related to the lower level of living standard. Food security and natural resilience
are fundamental aspects of sustainable development, and the analyses suggest that food
security is positively related to the level of competitiveness stages. The most insecure
countries are Yemen and the African ones (Sierra Leone, Chad, and Congo), and the most
secure region is Scandinavia (Finland, Sweden, and Norway).
4.1. Validation of the MFA Results
MFA factors are validated based on several methodologies, i.e., random sampling with
replacement (bootstrapping), exhaustive (leave-one-out, LOO) [43] and non-exhaustive
(split-half) techniques [44] (See Table 3). These cross-validation techniques are crucial in
assessing the accuracy of predictive models in practice [45].
Table 3. Validation results of the explanatory power of MFA factors.
Bootstrap
Simulation *
(p-Value)
Split-Half
Test *
(p-Value)
LOO **
Validation for
Observations
(% of Variation)
LOO **
Validation for
Variables
(% of Variation)
Dimension
Explained
Variance (%)
n = 10
n = 50
n = 100
1.
51.8%
0.722
0.916
0.940
0.841
3.6
3.5
2.
12.1%
0.102
0.384
0.600
0.220
2.0
5.7
3.
9.6%
0.614
0.992
0.952
0.656
4.6
6.8
Sample Size
Notes: * N = 1000 iterations. ** Leave-one-out (LOO).
First, a bootstrap simulation is performed for different sample sizes (n = 10; 50; 100),
with repetition within each indicator in each iteration (N = 1000, where N is the total
number of iterations). From the distributions testing the concerns of the null hypothesis,
the two-sided p-values concerned to the initially explained variances do not differ from
those simulated. Concerning the split-half test, the dataset is halved into equal parts, and a
separate MFA analysis is performed for each part.
This process is repeated N = 1000 times, and the explained variance of the MFA
factors is recorded and compared using a Wilcoxon signed-rank sample test [46]. The
lack of significance indicates no statistical difference between the two splits, and the first
component is the most stable for this type of study. During leave-one-out validation,
MFA is performed in two ways, either by omitting observations or variables. In the first
version of the test, each country is excluded from the analysis. The coefficient of variation
(standard deviation divided by the mean) is estimated for each component. The coefficient
of variation must not exceed the critical 20%. In the second version of the test, each
variable is omitted from each block once, and the variability of MFA factors is assessed as
previously discussed. LOO validations show that due to missing observations or variables,
the percentage of variation in the explaining performance of each component is less than
10%. Based on the results, the selected first two components proved to be stable during
the validation tests and explain sufficient variance with less variability if an observation
or a variable is omitted from the model. The most stable component is the first, which
reflects competitiveness.
4.2. Robustness Test of Variances
Analysis of variance (ANOVA) is used to analyse the differences of the four pillars
between the stages. In order to accomplish that, the between and within stage variances
are calculated. ANOVA provides statistical evidence of whether the two types of variances
(between and within) are equal or not. The F-test is used (Table 4) to compare the between
and within mean squares of each factor. F-test is the average of the squared differences
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between the observations and the grand mean (also called sum of squares). For all pillars,
the equality of between and within stage variance was not fulfilled at 0.05 p-level. This
result means that the factors differ between the stages, and variance is much lower.
Table 4. ANOVA analysis of the selected pillars.
Pillars
Stages
Sum of Squares
df
Mean Square
F
Sig.
181.149
70.116
251.264
4
98
102
45.287
0.715
63.298
<0.001
Economic risk
Between
Within
Total
0.984
0.420
8.513
<0.001
1.014
0.404
<0.001
Levene Test
Health risk
Between
Within
Total
20.224
58.201
78.425
4
98
102
5.056
0.594
Levene Test
Food security
Between
Within
Total
229.909
64.021
293.930
4
98
102
57.477
0.653
87.984
0.659
0.622
2.294
16.906
19.200
4
98
102
0.573
0.173
3.324
0.013
6.758
<0.001
Levene Test
COVID
Between
Within
Total
Levene Test
One major condition of the ANOVA is the homogeneity of the variance (total variance
should be equal overall stages). Homogeneity test is required because the stage levels are
not uniformly distributed (frequency 1: 27.2%; 2: 11.7%; 3: 20.4%; 4: 13.6%; 5: 27.2%). The
Levene test is applied to test the homogeneity of variance assumption. The test shows that
only COVID vulnerability violates this assumption. Hence, a nonparametric Welch-test
(unpaired t-test) has to be run for only this factor. This test proves the lack of significance
(F(4, 31.6) = 2.404; p = 0.070) at a 5% significance level, highlighting no significant difference
between the level of competitiveness and COVID-19 deaths and infectious cases.
Finally, it can be stated that the MFA factors generated (per block) are different in each
stage (Table 5). Lower values are better for economic and health risks, COVID variables
and higher values are more desirable for food security.
Table 5. MFA factors per pillars and competitiveness stages.
Stages/Pillars
Economic Risk
Health Risk
Food Security
COVID
1
2
3
4
5
−1.90
−0.02
0.04
0.42
1.67
−0.54
0.42
0.53
0.42
−0.24
−1.98
−0.64
−0.08
0.70
1.96
0.05
−0.06
0.22
0.04
−0.21
From this point of view, it is clear that economic risks and food security and resilience
are moving in parallel with competitiveness stages. In other words, the better is food
security, the higher is the competitiveness, but also greater is the risk of an economic
downturn. Health risk exposure is low in the most competitive (Stage 5) innovation-driven
countries due to the high quality of HC systems and factor-driven (Stage 1) countries
where an ageing society is still not a problem. However, there is no clear evidence of a link
between COVID-vulnerability and the development stages, but it can be stated that the
least pandemic affected countries are the most competitive ones.
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5. Discussion
An essential finding of MFA is that exposure to health risks and food insecurity is one
of the lowest in the most competitive countries least affected by a pandemic. Implications
are manifold.
WHO shows that two groups of people are more likely to be affected by the coronavirus: the older people without an underlying health care system and pre-existing
non-communicable diseases (NCDs) [47]. Kashnitsky and Aburto [48] have shown that
older people are much more at risk of death. Thus, people affected by NCDs, such as cancer,
diabetes, chronic respiratory (COPD), and cardiovascular diseases (high blood pressure,
stroke) are more likely to contract the virus more severely.
Not surprisingly, the highest COVID-19 deaths were reported in high-income countries (Central and Eastern Europe) where obesity, sedentary lifestyles and smoking habits
were most prevalent [49]. Schoeder [50] also found more chronic conditions, which directly
increased the likelihood of obesity. Meanwhile, in the case of Scandinavian and Mediterranean developed countries, where much of society follows an elderly, but a healthier
lifestyle [47], such as people access to eat a diversified diet with lots of fish, fruit and
vegetables, are physically active, quit smoking, limit or avoid alcohol intake, and obtain
enough sleep. Similarly, the World Bank classifies high-income countries as having higher
rates of COVID-19 morbidity and mortality than low-income countries due to the lack of
ageing issues and the lower proportion of NCDs [51].
Health and social security should provide adequate basic care for all, regardless of
income, directly and with additional health insurance. Despite many people losing their
previous lives and being disrupted by the epidemic, leading governments have been
reluctant to invest appropriately in their public health care systems, not to mention in less
developed countries, where lots of infectious diseases are likely to originate. Economic
costs could be ominously avoided with more significant investment in public health care,
primarily in economies where HC systems are less developed, and population density is
high [2]. Ji et al. propose to upsurge the availability and accessibility of medical resources
in the HC for the resource-limited regions on preparing for possible local epidemics [52].
Economic vulnerability also carries a substantial risk of stress and deterioration in
mental health [53]. Others have found increased depression, insomnia, and deteriorating
quality of life as a characteristic effect of coronavirus on mental health in Austria [54]. In
addition, physical disturbance due to an outbreak of COVID-19 have a drastically negative
effect on the mental health of the elderly, including stress, anxiety and depression [51].
Most people with chronic illnesses have lost their jobs in specific sectors of the economy,
such as tourism and retail [55]. The occurrence and spread of COVID-19 suggest that good
governance structure, investment in HC infrastructure and learning from past epidemics
have the most significant impact on the proper responses to proactive labour market
strategies [56] and achieving decent work and economic growth [57].
SARS-CoV-2 can also actively infect and replicate in the gastrointestinal tract. Adopting better hygiene practices can be a cheap and highly effective response that can reduce
infection and social and economic costs [58]. Adherence to this strategy can have significant
consequences in treating diseases, transmission and control of infections [59]. The availability and accessibility of clean and safe water, sanitation and hygiene services (WASH) are
also essential in the fight against the coronavirus and in maintaining billions’ sustainable
health and well-being [60]. The COVID epidemic cannot be curbed without vulnerable
people having access to safe food and unpolluted water and therefore not supporting core
facilities for more affected, low-capacity and fragile countries [61].
Sustainable (renewable) energy solutions must also be a priority [62], especially in
sub-Saharan Africa and other desert regions, critical for health clinics and first aiders.
All this is needed to reach vulnerable consumers, increase reliable, uninterrupted and
sufficient energy production and health care systems to prepare for economic recovery [63].
Meanwhile, the environmental costs of materials and manufacturing must be included in
Mathematics 2021, 9, 2398
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the accurate market price for optimal water, energy and food security [64], and must be
fiscally neutral about the global energy-related and carbon tax [65].
For years, scientists have warned that unrestricted deforestation [19], illegal wildlife
trade, and animal-to-human disease unleashes a globally uncontrollable epidemic [66].
More explicitly, the prevalence of infectious diseases in humans is zoonotic, and these
diseases are closely related to the health of ecosystems [67]. Green economies that benefit
the environment or conserve natural resources are essential to restoring the balance between
people and the planet and supporting economic and social recovery [68]. Green or greencollared jobs can also protect people and societies and make them more resilient to economic
downturns through a fair and inclusive transition to sustainable and inclusive growth [69].
SDGs are difficult to delineate and implement simultaneously due to their complexity and interdependence. Hence, future research has to be carried out across different
disciplines to retrace and develop novel and combined indicators that reflect the potential trajectories of pandemic issues. For example, researchers may consider physical and
mental health behaviours as a goal, as they also reduce other risks, such as changes in
decent working conditions, productive employment to promote sustainable, inclusive
economic development.
6. Conclusions
The objective of this study was to examine the interrelations of pandemic (COVID)
risk exposures to shed light on novel research perspectives on Sustainable Development
Goals (SDGs). A multiple factor analysis (MFA) approach was used to calculate correlations
between the risk pillars while also taking the competitiveness stages of examined countries
into account. The advantage of the MFA method is that it analyses various types of
observations described by groups of variables and is even more valuable when the dataset
is large and complex.
Contrary to previous approaches, the complexity of pandemic risks was considered
by analysing COVID-vulnerability, economic and health exposure, and global food security
and resilience, which is essential for exploring the interconnections of socioeconomic and
environmental issues. The MFA factors generated are different in each development stage,
namely: (a) the better is food security, the higher is the competitiveness, but also greater is
the risk of an economic downturn; (b) health risk exposure is low in the most competitive
innovation-driven countries and the factor-driven countries.
In the ranking of MFA factors by country, (c) the most COVID-vulnerable countries
are South American, Eastern-European ones, thus South Africa; (e) the pandemic health
risk appears to be lower in the countries with well-established public HC services and
good general health conditions, such as Israel, Netherlands. (f) Controversially to Noy and
Doan [9], it can be stated that the least pandemic affected countries are the most competitive
ones, and the less developed (African) ones are exposed to below-average economic risk.
Consequently, the coronavirus epidemic again shows that the global economic and
health systems are unfortunately not sustainable, even inadequate to deal with a crisis
of this magnitude. Although there is no clear evidence to accept the presumption of
correspondence between COVID-vulnerability and economic development, the countries
least affected by the epidemic are the most competitive. However, they are also unable to
protect people and the economy effectively.
The present study makes vital contributions. First, the study investigated the global
risks of the coronavirus epidemic associated with economic and health systems and the
most prominent food security for exploring the interconnections of socioeconomic and
environmental issues. Secondly, the study first used multivariate factor analysis (MFA) to
link multiple risk pillars and introduces some validation techniques, which is also novel
because the method works with blocks of variables.
The practical implication of the study is that policymakers need to pay attention to
three areas: exposure to economic and health risks and global food security for economic
and structural reforms. Green economies that benefit the environment or conserve natural
Mathematics 2021, 9, 2398
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resources to address socio-economic and environmental issues reduce the risk of economic
downturn. In less developed countries, investment in health is essential to improve the
ability to reduce physical and mental illness to withstand the onset of contagious diseases.
Decent working conditions are needed worldwide to promote inclusive development
and productive employment. In addition, policymakers should use legislation to create a
robust social and economic welfare for all citizens, particularly for unemployed and poor
households, to stabilise individuals’ food security [70]. COVID-19 have a significant impact
on the European agri-food sector as it has affected producer prices, which could disrupt
global supply chains [71].
This study has some limitations in terms of the methodologies and variables chosen.
The most important is the bias of the omitted variables, as the pillar variables only reflect
the subjective decisions of the authors. The other is related to the dynamic characteristics
and spatial differences of epidemic waves. Thus, the quantitative indicators measured
before the transmission of the virus do not reflect the complete risks of an epidemic. They
only concentrate on the expected exposures. However, the vulnerability also depends on
the appropriate government responses and measures. The reader should bear in mind
that the study is based on the current judgment of the authors. All information in this
publication provided by the authors does not constitute any advice or opinion on specific
economic policy issues and is therefore not responsible for its use.
Further research is needed to examine the structure of complex risk indicators related
to the dilemma of selecting appropriate variables. Only a few quantitative analyses adequately addressed the relative importance of weights and the aggregate selection method
for ranking. These indicators and methods are also essential for policymakers to continuously monitor and analyse progress towards sustainable development goals. Researchers
may also consider using more comprehensive measures to assess risks in different regions
and cities.
Author Contributions: Conceptualisation, S.K. and D.M.; methodology, S.K.; software, S.K.; validation, S.K.; formal analysis, S.K.; investigation, M.F.R.; resources, S.K.; data curation, S.K. and
D.M.; writing—original draft preparation, D.M.; writing—review and editing, S.K., D.M. and M.F.R.;
visualisation, S.K.; supervision, S.K.; funding acquisition, S.K. All authors have read and agreed to
the published version of the manuscript.
Funding: This research was supported by ÚNKP-21-5-DE-3 New National Excellence Program of
the Ministry for Innovation and Technology from the source of the National Research Development
and Innovation Fund and was supported by the János Bolyai Research Scholarship of the Hungarian
Academy of Sciences.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Publicly available datasets were analyzed in this study. This data
can be found here: https://solability.com/all-news/corona-risk-exposure-by-country (accessed
on 20 July 2021); https://foodsecurityindex.eiu.com/Country (accessed on 11 May 2021); https:
//ourworldindata.org/coronavirus (accessed on 26 July 2021).
Acknowledgments: This research was supported by the János Bolyai Research Scholarship of the
Hungarian Academy of Science.
Conflicts of Interest: The authors declare no conflict of interest.
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