# Statistical analysis of the impact of COVID-19: a study case on several countries

María Cristina Sabando-Álvarez, Darwin X. Tallana-Chimarro, Isidro R. Amaro, Franklin Camacho

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María Cristina Sabando-Álvarez, Darwin X. Tallana-Chimarro, Isidro R. Amaro, Franklin Camacho. Statistical analysis of the impact of COVID-19: a study case on several countries. Bulletin of Computational Applied Mathematics, 2021, 9 (1), pp.63-83. hal-03558824

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# Statistical analysis of the impact of COVID-19: a study case on several countries

María Cristina Sabando-Álvarez¹²,

Darwin X. Tallana-Chimarro<fnref n="3" />, Isidro R. Amaro<fnref n="4" />,

Franklin Camacho<fnref n="5" />

CompAMa Vol.9, No.1, pp.63-83, 2021 - Received April 12, 2021 - Accepted November 3, 2021

Special Issue III CICB 2020, Eds. Carmen Judith Vanegas and Saúl Buitrago Boret

## Abstract

In this work we analyzed the impact of COVID-19 in Ecuador, Mexico, Colombia, Argentina, Chile, Peru, Italy, Belgium, France, Israel, South Korea, and Japan. The variables considered reflect the impact of the pandemic, taking into account the strengths and weaknesses of each country to confront the disease. The data pertaining the first three months after each country officially reported its first case was obtained from Our World in Data. The analysis was carried out using Cluster analysis and Canonical Biplot. We found that variables representing advantages a country had a priori are not determinant in the containing of the pandemic or the

<footnote n="1">¹Department of Mathematics and Statistics, Washington University in St. Louis, USA. (sabandoalvarez@wustl.edu). (ORCID iD: https://orcid.org/0000-0001-8634-2172).</footnote>

<footnote n="2">²Corresponding author.</footnote>

<footnote n="3">³Escuela de Ciencias Matemáticas y Computacionales, Universidad Yachay Tech, Ecuador. (darwin.tallana@yachaytech.edu.ec). (ORCID iD: https://orcid.org/0000-0001-9304-9097).</footnote>

<footnote n="4">⁴Escuela de Ciencias Matemáticas y Computacionales, Universidad Yachay Tech, Ecuador. (iamaro@yachaytech.edu.ec). (ORCID iD: https://orcid.org/0000-0003-2402-910X).</footnote>

<footnote n="5">⁵Escuela de Ciencias Matemáticas y Computacionales, Universidad Yachay Tech, Ecuador. (fcamacho@yachaytech.edu.ec). (ORCID iD: https://orcid.org/0000-0001-7802-5687).</footnote>
number of deaths derived from the disease. Countries with high testing capacities had a better handling of the pandemic. The implemented analysis allowed a periodic evaluation of the situation of each country and determined countries more vulnerable and affected in regard to the studied variables.

**Keywords:** COVID-19; canonical biplot; cluster analysis; classification.

## Resumen

Este trabajo analizó el impacto de la COVID-19 en Ecuador, México, Colombia, Argentina, Chile, Perú, Italia, Bélgica, Francia, Israel, Corea del Sur, y Japón. Se consideraron variables que reflejan el impacto de la pandemia, tomando en cuenta la vulnerabilidad y fortaleza de cada país para afrontar la enfermedad. Los datos fueron obtenidos de Our World in Data y corresponden a los primeros tres meses después de que cada país reportó su primer caso oficial. El estudio fue realizado usando Análisis de Conglomerados y Biplot Canónico. Se encontró que las variables que representan las ventajas que un país tiene a priori, no son determinantes en la contención de la pandemia o en el número de muertes derivadas de la enfermedad. Los países con capacidades altas de testeo tuvieron un mejor manejo de la pandemia. El análisis implementado permitió una evaluación periódica de la situación de cada país y determinó los países más vulnerables y más afectados en cuanto a las variables estudiadas.

**Palabras claves:** COVID-19; biplot canónico; análisis de conglomerados; clasificación.

# 1 Introduction

In December 2019, the new coronavirus triggered a COVID-19 outbreak in Wuhan, China [1]. This new disease affects mainly older people, persons with high blood pressure, diabetes, cancer, or with heart or lung problems. Seriously ill people commonly develop difficulty breathing.

The virus reached poor and rich countries. At the beginning of the pandemic, many of them took extreme measures to prevent its propagation and the collapse of their health systems, for example, the quarantine of entire cities, or even the entire country. However, due to the weakening of their economies, many countries were forced to resume their economic activities and opted for preventive measures against the virus. These measures include taking tests, the wearing of masks in public, and hand washing.
In this work we study the health impact of COVID-19 on several countries around the world by applying multivariate statistical techniques. For this study, we consider the number of deaths per million inhabitants, and the number of cases per million inhabitants. We perform classifications of these countries by using Cluster Analysis and Canonical Biplot. Thanks to this classification, we determine the most and least vulnerable and affected countries. The methodology used in this work gives a way of determining the status of each country with respect to the studied variables, which allows periodic control of the evolution of the pandemic.

This paper is organized as follows: in Materials and Methods, we describe our data set and the Cluster Analysis and Canonical Biplot methods. After that, we present our results, which include the Manova Biplots graphics. Finally, we describe our discussion and conclusions.

# 2 Data Description and Methodology

In this section we provide a description of the data used, together with the statistical methodology and the procedure applied for the study.

## 2.1 Data

We have obtained some COVID-19 data from [2,3] for 12 countries: Mexico, Colombia, Argentina, Chile, Peru, Italy, Belgium, France, Israel, South Korea, and Japan. This selection was done after checking the availability of the data needed for our objectives while trying to have representatives from different regions of the world, so as to get a wider picture of the pandemic and its effects. The collected data contains the following information: total cases per million of inhabitants, total deaths per million of inhabitants, total tests per 100k inhabitants, hospital beds per 100k inhabitants, Human Development Index (HDI), and median age. The time studied was the first, second, and third month after the report of the first case in each country. We divide the variables mentioned above into the following two groups:

The *first group of variables* is made up of total tests per 100k inhabitants (+), hospital beds per 100k (+), HDI (+), and median age (-). This group contains variables that indicate the advantages and disadvantages of a country to confront the health aspect of the pandemic. While *the second group of variables* contains: total cases per million, total deaths per million. The
variables in this group indicate, in some sense, the “effects” of the pandemic.

The symbol (+) means that while the variable takes larger values, it is better for confronting the pandemic, and (-) means the contrary.

## 2.2 Methods for Data Analysis

We give a description of the methods used in this work, with special emphasis on the properties of Canonical Biplot which will come in handy to interpret our results.

### 2.2.1 Cluster Analysis

Cluster analysis [4] are methods that look for patterns in data sets to group observations into clusters without any prior knowledge of the clusters.

### 2.2.2 Canonical Biplot

The Canonical Biplot Multivariate Statistical Technique [5–10], is a complementary method to MANOVA that includes all the characteristics of the Biplots Methods, [7], and is used in multivariate statistics to work with a data set with variables and individuals divided into preset groups. This technique allows, among other things, to search and establish differences and similarities between the groups.

Let X be a data matrix of n individuals by p variables and let us assume that the n individuals are divided naturally into K separate groups with $n_k$ individuals each (for $k = 1, 2, ..., K$; $n = n_1 + ... + n_k$). The aim of this method is to construct a simultaneous representation of the rows (centroids) and columns (variables) of the matrix X of the group's means, so that the groups are separated with maximal discriminatory power, i.e., represented in the space spanned by the first's canonical variables. The method considers the matrix

$$Y = D_n^{\frac{1}{2}} \overline{X} W^{-\frac{1}{2}} \quad (1)$$

of the weighted groups means and its generalized singular value decomposition,

$$Y = U D_{\lambda} V'. \quad (2)$$

Where $D_n$ is the diagonal matrix containing the groups sample sizes and

$$W = \frac{1}{n-k} (X'X - \overline{X}' D_n \overline{X})$$
is the “within-groups” matrix. From (1) and (2), we get

$$ \overline{X} = D_n^{-\frac{1}{2}} Y W^{-\frac{1}{2}} = D_n^{-\frac{1}{2}} U D_{\lambda} V' W^{-\frac{1}{2}}. \quad (3) $$

This decomposition allows for the construction of a biplot representation (of dimension r) for the matrix of group means, taking the first r columns of

$$ P = D_n^{-\frac{1}{2}} U D_{\lambda} \quad (4) $$

as row markers and the first r columns of

$$ Q = W^{\frac{1}{2}} V \quad (5) $$

as columns markers. So, $\overline{X} = PQ'$. The row markers are represented as points and the columns markers, as vectors in a scatter diagram. Thus, the similarity between groups can be measured as an inverse function of its distance on the graph. The angle between variables can be interpreted as an approximation of its correlation. Confidence circles are added to the group's markers in such a way that the projections of the circles onto the direction representing a variable, approximate a confidence interval. Checking the overlapping of two projections allows us to establish the significance of the difference between groups over a particular variable. The ratio of the circle is calculated as $\frac{t_{n-K,\alpha}}{\sqrt{n_K}}$, where $t_{n-K,\alpha}$ is the critical point of a student's t distribution with $(n-K)$ degrees of freedom, for a significance level of $\alpha$.

From (3), (4), and (5) we have that

$$ P = \overline{X} W^{-\frac{1}{2}} V = \overline{X} W^{-1} W^{\frac{1}{2}} V = \overline{X} W^{-1} Q $$

Thus, the projection of an individual $x$ (centered) over the Biplot is

$$ \text{prox}_x = x' W^{-1} Q Q' = x' W^{-1} W^{\frac{1}{2}} V V' W^{\frac{1}{2}} = x' W^{-\frac{1}{2}} V V' W^{\frac{1}{2}} $$

More on definitions, algebraic derivations, and quality of representation of the Canonical Biplot analysis is discussed in [5]. The proof of the theorem relating the Canonical Biplot to the MANOVA technique can be found in [11]. Recent works on which the Canonical Biplot has been used include [12–14]. For more on the properties and interpretation of the Canonical Biplot graph the reader is refered to [6].
## 2.3 Procedure

For each variable group, cluster analysis is used to sort the countries into groups. First, we standardize the variables, then use hierarchical clustering techniques and choose the method yielding the highest cophenetic correlation coefficient. For the used data this method is Average Linkage. Next, Canonical Biplot is implemented to determine if the resulting groups are significantly different and, later, to characterize them. We make the cut for groups using the large changes in distances approach, while also considering information obtained from the Canonical Biplot analysis such as significant group differentiation, total variance explained, together with groups and variables representation. To perform the hierarchical clustering we use InfoStat [?] and to obtain the Canonical Biplot analysis we use MultBiplot [15].

# 3 Results

In this section, we present our results. We start with the first group of variables during each month studied and then proceed in the same way for the second group of variables. As we stated previously, our interpretation relies heavily on the properties of Canonical Biplot. The percentage of the total variance explained by each factorial axis is presented in each Canonical Biplot graph.

## 3.1 First group of variables

### 3.1.1 First month

The averages of these variables for the first month are 36.66 for the median age, 4.7 for the total number of hospital beds, 0.625 for the number of tests, and 0.85 for HDI. The hierarchical clustering process with a cophenetic correlation coefficient of 0.883 and a cut at 1.74 yields the following groups: GR1 which contains South Korea and Japan; GR2 consisting of Italy, France, and Belgium; GR3 having Israel and Chile and GR4 consisting of Mexico, Ecuador, Peru, Colombia and Argentina. Applying Canonical Biplot, we obtain a Wilk's lambda value of 40.0396 (p-value 1.019e-08 < 0.001) which means there is a significant difference between the means of the groups. We can see graphically (Figure 1) that the regions of confidence of the groups are significantly different. Indeed, they do not intersect.
<figure><img src="image_2.png" /><figcaption>Fig. 1: Canonical Biplot for the first group of variables, month 1. GR1 in purple, GR2 in red, GR3 in blue, and GR4 in black.</figcaption></figure>

<figure><img src="image_3.png" /><figcaption>Fig. 2: Projections of the confidence circles for the first groups of variables, month 1.</figcaption></figure>
We have that our factorial axes explain the 85% of the total variance, with the first axis explaining 64% of total variance. Figure 1 shows that the variable with the largest variance is HDI. From the angles formed by the axes, we can tell there is a high positive correlation between median age, number of hospital beds and HDI. Furthermore, there does not seem to be a significant difference between hospital beds and median age, and there is also no significant correlation between HDI and number of tests taken per 100k inhabitants.

To characterize the groups, we project the provinces and the means of the groups on the variables vectors in Figure 1. Then, Figure 2 shows that GR1 is made up of countries with a high number of hospital beds, high HDI, and high median age; GR2 is characterized by countries near the average for all variables except number of tests; GR3 corresponds to countries with a high testing capacity; and GR4 is made up of countries scoring below the average for all variables. Additionally, it seems that GR1, GR2 and GR4 correspond to the continent a country belongs to geographically, that is, GR1 is formed of Asian countries, GR2 of European countries, and GR4 of Latin American countries. The variables separating GR3 from the rest of the clusters are median age and hospital beds, while GR4 is separated from GR1 and GR2 by the number of tests taken. Israel stands out as the country with the highest testing capacity in comparison to France, while Japan has the oldest population and the highest HDI. This is in contrast with the low values Mexico has on both variables. Furthermore, from Figure 2 we can see that GR1, GR2 and GR3 have no significant difference in the number of tests taken, while GR2 and GR3 are not significantly different on the HDI score, and GR3 and GR4 show no difference on their number of hospital beds and median age.

### 3.1.2 Second month

For these variables we have the following averages: 36.65 for median age, 4.7 for hospital beds per 100k inhabs., 5.88 test per 100 inhabs., and 0.845 for HDI. From Figure 3, we get four groups: GR 1: Israel, GR2: Ecuador, Argentina, Chile, Colombia, Peru, Mexico, GR3: South Korea, Japan, GR4: Italy, France, Belgium.

From the Canonical Biplot in Figure 3, we see graphically that we can see that all the groups are significantly different. Indeed, we obtain a Wilk's lambda value of 36.7845 (p-value 1.7932e-06 < 0.001), We also see that the
variable with the largest variance is hospital beds per 100k inhabs. Moreover, there is a correlation between HDI, hospital beds per 100k inhabs, and median age.

<figure><img src="image_4.png" /><figcaption>Fig. 3: Canonical Biplot for the first group of variables, month 2. GR1 in purple, GR2 in red, GR3 in blue, and GR4 in black.</figcaption></figure>

If we consider the projection of the circles of each group, see Figure 4, we find that GR3 is different to the other groups in all the variables. GR2 and GR4 are not significantly different in hospital beds per 100k and HDI, while GR1 and GR4 are not significantly different in HDI and hospital beds per 100k.

### 3.1.3 Third month

The averages are 36.65 for median age, 4.7 for hospital beds per 100k inhabs., 16.57 for tests per 100k inhabs., and 0.845 for HDI. Note that the groups are the same as for month 2, as in shown in Figure 5. Additionally, from the Canonical Biplot analysis we get a Wilk's Lambda value of 16.3408 (p-value equal to 3.4246e-06), so all the groups are significantly different. The variable with the largest variance is median age. Moreover, median age and test per 100k inhab. are inversely correlated. See Figure 5.

By projecting individuals and mean groups in Figure 5, we see that GR1 and GR3 have the largest and the smallest value in the variable test per 100k inhab., respectively. For the other variables, the description is almost
<figure><img src="image_5.png" /><figcaption>Fig. 4: Projection of the confidence circles for the first group of variables, month 2</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 5: Canonical Biplot for the first group of variables, month 3. GR1 in purple, GR2 in red, GR3 in blue, and GR4 in black.</figcaption></figure>
the same as the one that was given for month 1 and 2. From Figure 6, we see
that GR1 and GR2, and also GR2 and GR4 are not significantly different in
the variable hospital beds per 100k. For the rest of variables, all the groups
are significantly different. So, if we compare the three months, we see that
the countries are clustered into the same four groups. This is mainly because
HDI and median age are constant values during short periods of time, and
hospital beds per 100k inhabitants was a variable that did not change during
the three months. The only variable that changed during the three months
was test per 100k inhabitants. However, it did not alter the clustering at all.

<figure><img src="image_7.png" /><figcaption>Fig. 6: Projection of the circles for the first group of variables, month 3.</figcaption></figure>

From this analysis, we see that GR3 (the group of Asian countries) is the
group with the strongest Health System (HS) and the highest development,
while GR2 (the group of Latin American countries) with the weakest HS
and the lowest HDI. GR1 (Israel) has the largest number of tests taken per
100k inhabitants, and GR4 (European countries) the “average” group, with
a small number of tests taken per 100 inhabitants.

**3.2 Second group of variables**

**3.2.1 First month**

For the first month, the averages of these variables are 48.96 for total cases per
million inhabitants and 0.9 for total number of death per million inhabitants.
The hierarchical clustering process with a cophenetic correlation coefficient of 0,929 and taking the cut at 2 yields the following groups: GR1 consisting of Peru and Ecuador; GR2 being made up of Chile, and GR3 containing Israel, Italy, Mexico, South Korea, Japan, France, Belgium, Colombia, Argentina. From the Canonical Biplot analysis, we obtain a Wilk's lambda value of 18.1949 (p-value 8.4115e-06 < 0.001). Thus, the resulting groups have significant differences. On Figure 7, we can observe that the first factorial axis is responsible for explaining the 74% of the total variance.

<figure><img src="image_8.png" /><figcaption>Fig. 7: Canonical Biplot for the second group of variables, month 1. GR1 (purple), GR2 (red), and GR3 (blue).</figcaption></figure>

We also have that the variable with the largest variance is total cases per million inhabitants and that there is no correlation between the studied variables. Additionally, by looking at the projections on Figure 8, we characterize the groups obtained.

We see that GR1 has the largest number of deaths per million inhabitants, followed by GR2 and lastly, by GR3. Looking at the total cases per million inhabitants, we have that GR2 has the most cases, followed by GR1.
<figure><img src="image_9.png" /><figcaption>Fig. 8: Projections of the confidence circles for the second group of variables, month 1.</figcaption></figure>

It is important to note that GR3 is under the mean vector for both variables, while GR1 and GR2 are above the mean. Moreover, we note that GR2 and GR1 are not so different in the number of cases even though they are different taking total deaths into account. GR3 and GR2 are similar in the number of deaths, and GR1 has similarities with GR3 in total cases. Furthermore, by comparing both Figure 7 and Figure 8 we notice that Israel is an outlier in GR3, as its number of confirmed cases is closer to those reported by GR2 while its number of deaths is closer to other members of GR3. Ecuador has the highest number of deaths, while Chile has the most confirmed cases.

### 3.2.2 Second month

For the second group of variables in this month we have the following averages: 800.48 for total cases per million inhabs., 38.76 for total deaths per million inhabs. We get three groups from the cluster analysis: GR1: Italy, GR2: Ecuador, Israel, Peru, Chile, Belgium, GR3: France, South Korea, Mexico, Japan, Colombia, Argentina. All the groups are significantly differ-
ent since we have a Wilk's Lambda value equal to 32.0968 with a p-value 1.8447e-07 < 0.001 (see also Figure 9). The variable with the highest variance is total deaths per million inhabitants and there is no correlation between the two variables.

By the projections of individuals and means in Figure 9, we see that the country with the largest number of cases and deaths per million is Italy (GR1), GR2 has values on the averages, and GR3 has values under the average. We also see that Japan and South Korea are the countries with the lowest number of cases and deaths per million. We also see that GR1 and GR2 differ mainly in the number of deaths per million inhabitants, while GR2 and GR3 in the number of cases per million inhabitants.

<figure><img src="image_10.png" /><figcaption>Fig. 9: Canonical Biplot for the second group of variables, month 2. GR1 in purple, GR2 in red, and GR3 in blue.</figcaption></figure>

From Figure 10, we see that GR1 and GR2 are not so different in the total number of cases per million, whereas GR2 and GR3 do not differ significantly in the total number of deaths. From the analysis, we can say that GR1 is the most affected group for the virus during this month, GR3 is the least affected, and GR2 the least affected.
<figure><img src="image_11.png" /><figcaption>Fig. 10: Projections of the confidence circles for the second group of variables, month 2.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 11: Canonical Biplot for the second group of variables, month 2. GR1 in purple, GR2 in red, GR3 in blue, and GR4 in black.</figcaption></figure>
### 3.2.3 Third month

In this case we have the following averages: 2290.88 for total cases per million inhabs., and 169.59 for total deaths per million inhabs. During the third month, we see that all groups are significantly different (Wilk's Lambda = 29.4005 and p-value = 3.6623e-07), see Figure 11, and the variables are not correlated.

<figure><img src="image_13.png" /><figcaption>Fig. 12: Projections of the confidence circles for the second group of variables, month 3.</figcaption></figure>

By projecting individuals and mean groups in Figure 11 and by Figure 12, we see that GR1 is the group with more cases per million inhabitants and GR2 has more deaths per million inhabitants. So GR1 and GR2 contain the most affected countries. GR3 is over the average of deaths and under the average of cases. GR4 is under the average for both variables. Then GR4 is the least affected group. We also see that GR1 is significantly different from GR3 and GR4 in the number of cases and GR2 is significantly different from GR3 and GR4 in the number of deaths.

## 4 Discussion

In this section we study the changes month to month for each group and representative cases discussed in the previous section. Then, with this information we try to get a wider picture of the situation and try to establish
relations between both groups of variables and their impact in a country's
situation.

**4.1 Comparisons between months**

**Month 1 to Month 2:** The only changing variable in this group is the number of tests taken, which does not majorly affect group constitution. Comparing the clusters on the first group of variables and their changes from month 1 to month 2, we have that in month 2 our clusters correspond to the continent of the world a country belongs to, with the exception of Israel in GR1. The variable with the highest variance changes from HDI in month 1 to hospital beds per 100k in month 2. On month 2 we see that hospital beds and median age, while still correlated, are now properly differentiated. Even though the testing scores have changed from month one, there is still no correlation between HDI and testing capacity. There is also an increase in the variance explained by our factor axes, going from 85 to 97%. Regarding group characteristics, we have that European countries remain below the average of tests taken, Chile has lowered its testing numbers, South Korea is now above average, and Latin American countries have upped their testing capacity. Now, considering the second group of variables we have a total change in the groups as the variable with the most variance changes to total deaths on month 2 from total cases on month 1. That is, groups in the first month were mostly determined by the confirmed cases while in the second month, the groups were determined by the deaths derived from the disease. We go from Chile having the biggest number of cases and Ecuador having the most deaths to Italy having the highest magnitude on both variables in the second month.

**Month 2 to Month 3:** If we compare month 2 to month 3, we see that there are different groups. According to this data, Ecuador decreases the number of cases with respect to the mean, while in Peru and Chile the number of cases have increased. France and Belgium increase the number of deaths per million inhabitants. Israel is now at the average number of cases.

**4.2 Comparison between variables**

Looking at the monthly changes in the two variable groups, we try to estab-
lish some possible explanations for the changes and try to see the interaction
between the two variable groups using representatives from the data set hav-
ing changed the most during the time considered. Israel keeps its testing
capacity as one of the highest for the three months and also a high number
of cases until month 3, on which it becomes the country with the lowest num-
ber of deaths and reported cases. Italy, which goes from having below average
number of cases and deaths in month 1 to the highest in month 2, remains
low on tests taken. Even though Chile has the most cases reported on month
1, we can see that their testing capacity is one of the highest, which might
be an important factor considering that despite its below average HDI and
number of hospital beds, it is not a country with a larger number of deaths
as Ecuador, which scores below average for all variables from the first group.
One remarkable change is that Peru passes Italy in the number of cases in
month 2 but with an important reduction of the deaths reported from month
1. This might be due to the increase of testing Peru shows while Italy keeps
testing below average. On month 3 we have that Belgium surpasses Italy in
the number of deaths, but it's not clear what the interaction of all the va-
riables might be here. For a better understanding of the interaction between
all the variables, we look at France and Ecuador, which are now in the same
group and reporting above the average number of deaths while having below
the average number of reported cases, but both countries differ in their test
taking and HDI. Chile and Peru have become the countries with the most
confirmed cases while having the lowest dead tolls. Taking the other vari-
ables into account, we see that these countries differ from Belgium and Italy
with a higher number of tests taken while keeping their below average values
in the other variables. Drastic changes happen from month to month in the
variables tests taken per 100k inhabitants, confirmed cases per million in-
habitants, and deaths per million inhabitants, and this changes appear to be
highly related to the testing capacity of the countries but not dependent on
variables such as HDI, hospital beds and median age. It seems that countries
with a high testing capacity can develop faster and more effective ways of
containing the spread of COVID-19 so that variables such as the number of
hospital beds or HDI do not influence the death toll and number of confirmed
cases.
# 5 Conclusions

After classifying the countries, we have found that in the first month with respect to the first group, the one showing advantages and disadvantages, the countries were classified according to their geographic location. The groups formed by South Korea and Japan, and France, Belgium and Italy seem to have more advantages, since they have more developed health systems compared to the other countries. The exception seems to be the group of Israel and Chile as they are the countries with more tests taken per 100k inhabitants. The rest of the countries are under the average in all the variables, with the exception of median age.

As time progresses, we have found that the interaction between the variables showing the advantages a country had a priori, appear to have no consequence in the containment of the pandemic or the casualties derived from the disease. In particular, countries with high numbers of tests per 100k inhabitants have a low number of deaths per million inhabitants, even though they report an above the average number of confirmed cases. This is the case of Chile and Israel during the three months, and Peru after the first month. The statistical evidence seems to point out that countries with a high testing capacity can develop faster and more effective ways of containing the spread of COVID-19, while variables such as the number of hospital beds or HDI do not influence the death toll and number of confirmed cases with the passing of time.

The effect of measures taken to avoid the spread of COVID-19 and to help its containment, such as the number of tests performed by a country, could be studied further to establish global protocols in public health for future pandemics and disease outbreaks.

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