=Paper=
{{Paper
|id=Vol-3777/paper8
|storemode=property
|title=The COVID-19 Pandemic Dynamics and Incomes
|pdfUrl=https://ceur-ws.org/Vol-3777/paper8.pdf
|volume=Vol-3777
|authors=Igor Nesteruk,Oleksii Rodionov
|dblpUrl=https://dblp.org/rec/conf/profitai/NesterukR24
}}
==The COVID-19 Pandemic Dynamics and Incomes==
https://ceur-ws.org/Vol-3777/paper8.pdf
The COVID-19 Pandemic Dynamics and Incomes
Igor Nesteruk1,2, Oleksii Rodionov3
1
Institute of Hydromechanics, National Academy of Sciences of Ukraine, Kyiv, Ukraine
2
SBIDER (Systems Biology & Infectious Disease Epidemiology Research) Centre at University of Warwick, UK
3
Private consulting office, Kyiv, Ukraine
Abstract
Objectives. Large differences in the number of registered SARS-CoV-2 cases per capita in different
countries encourage research into the causes of this phenomenon. In particular, the accumulated numbers
of cases per million (CC) demonstrated strong linear correlations with the gross domestic product per
capita (GDP) and the median age of populations. In this paper the possible correlations between GDP and
numbers of cases CC and deaths (DC) per million, case fatality risks CFR=DC/CC, vaccinations and
testing levels will be investigated. As well non-linear correlations of CC, DC and CFR values versus
vaccinations and testing levels will be considered.
Methods. A non-linear correlation and John Hopkins University (JHU) datasets for African and European
countries corresponding to August 1, 2022 were used.
Results. The numbers of CC, DC and CFR increase for richer countries, the same trends were revealed
for DC and CFR values in Africa, but opposite ones in Europe. As expected, the testing and vaccination
levels increase with the growth of GDP. Higher levels of testing probably allowed revealing more cases
and COVID-19 related deaths in rich countries. CC values showed a very strong increasing trend with the
increase of numbers of tests per capita (TC). Unexpectedly, the same increasing trend was revealed for CC
and DC values versus percentage of fully vaccinated people (VC). Nevertheless, the decrease of CFR with
the increase of VC demonstrates a positive effect of vaccinations.
Conclusions. In some countries, the number of undetected COVID-19 cases may be tens or even
hundreds of times higher than the number of registered ones due to the differences in testing levels and
age structure. This fact increases the probability of the appearance of new dangerous SARS-CoV-2 strains
and has to be taken into account in further investigations of impact of different factors on the pandemic
dynamics.
Keywords
COVID-19 pandemic, epidemic dynamics in Africa, epidemic dynamics in Europe, gross domestic
product per capita, non-linear correlation, statistical methods.1
Introduction
The general characteristics of the COVID-19 pandemic dynamics require further research despite
the vast number of available publications, including studies comparing the COVID-19 pandemic
dynamics in different regions and the impact of various factors [1-18]. In particular, a strong linear
correlation was revealed in [18] between the gross domestic product per capita (GDP) [19] and the
numbers of cases per million (CC) registered in African countries as of February 1, 2022, [20]. In
this study, a non-linear correlation between incomes and values of CC, accumulated numbers of
deaths per million (DC) and the case fatality risk (CFR=DC/CC) will be investigated with the use of
datasets for African and European countries corresponding to August 1, 2022, [20]. We will also
discuss the possible influence of the accumulated numbers of tests per capita (TC) and the
percentage of fully vaccinated people (VC) on the CC, DC and CFR values.
ProfIT AI 2024: 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024), September 25–27,
2024, Cambridge, MA, USA
inesteruk@yahoo.com (I. Nesteruk); aleksei.rdnv@gmail.com (O. Rodionov)
0000-0001-7250-2729 (I. Nesteruk); 0000-0002-4466-2183 (O. Rodionov)
© 2024 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
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CEUR Workshop Proceedings (CEUR-WS.org)
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Data, the non-linear regression and Fisher test
We will use the data sets regarding the gross domestic product per capita (GDP) based on
purchasing power parity (PPP) available in [19] and some COVID-19 characteristics reported by
John Hopkins University (JHU) as of August 1, 2022, [20]. The figures corresponding to the
versions of files available on September 4, 2022 are presented in supplementary Tables S1 and S2
and shown in the Figure for African (black markers) and European countries (blue markers).
The following non-linear correlation will be applied:
y = a + b( x + с)g ; a ³ 0 , b>0, x>-c (1)
to find links between GDP, TC, and VC (variable x) and CC, DC, DC/CC, TC, and VC (variable y).
At γ =1 relationship (1) reduces to the linear one. It can be also reduced to the linear correlation by
using new random variables z and w≡log(x+c), [11, 14]:
z≡log(y-a)=log(b)+γlog(x+c) (2)
The constant parameters γ, log(b) and corresponding best fitting lines can be found with the use
of standard linear regression formulas [21] for different values of constant parameters a and c.
Their optimal values correspond to the maximum of the correlation coefficient magnitude |r| or the
ratio of the Fisher functions F/Fc(k1,k2), (k1=1, k2=n-2, n is the number of observations, i.e., the
number of countries in datasets), [ 6, 11, 14]. The corresponding experimental values F can be
calculated with the use of formula (S1), [21], the critical values Fc(k1,k2) of the Fisher function at a
desired significance or confidence level can be found in [22]. If F/Fc(k1,k2)<1, the hypothesis about
the relationship (1) is not supported by the results of observations. The highest values of F/Fc(k1,k2)
correspond to the most reliable hypotheses.
Results
The optimal values of parameters for different non-linear correlations (1) are listed in Table 1.
Corresponding best fitting lines are shown in the Figure by the black color for African datasets,
blue - for Europe and red - for complete datasets (Africa + Europe). Rows 1-3 of Table 1 and solid
lines in Figure illustrate that the accumulated numbers of cases per million CC always increase
with the increase of the incomes. Nevertheless, the number of deaths DC and case fatality risk CFR
decrease in richer European countries (see rows 5, 8 and blue dashed and dotted lines; the
correlation DC versus GDP is supported at confidence level 0.025; Fc (1, 40) =5.47). What is
surprising is the increase in DC and CFR values with increasing income in Africa and for the full
data sets (see rows 4, 6, 7, 9 and corresponding dashed and dotted lines; the correlation CFR versus
GDP for complete dataset is supported at confidence level 0.005; Fc(1,94) =8.33).
As expected, the vaccination and testing levels (VC and TC) always increase with rising
incomes (see rows 10-15 and corresponding lines). Rows 16 and 18 represent the correlation of CC
values versus TC and VC, respectively. The strongest link between the number of cases and the
testing level (r=0.9496, and the highest F/Fc(k1,k2) ratio, see row 16) and the strong link between
TC and GDP values (see row 12) allows us to conclude that high CC values in rich countries are
probably connected with the higher testing level. Numbers of cases and deaths per capita for
complete dataset (Africa + Europe) increase with the increase of percentage of vaccinated people
VC (see rows 18 and 20). Opposite trend was revealed only for CFR values (row 22). Thus, the
positive effect of vaccinations is visible only in decreasing the probability to die for persons tested
positive. To eliminate the influence of the testing level the same correlations were investigated for
15 European countries with TC>3. Corresponding CC, DC and CFR values demonstrate decreasing
trend with the increase of VC, but it was not supported even at the significance level 0.05 (Fc
(1,13)= 4.67; see rows 19, 21 and 23).
Table 1.
Optimal values of parameters in eq. (1), correlation coefficients and the results of Fisher
test applications for Africa, Europe and complete datasets (Africa + Europe).
Experi- Critical
Num- Corre- Optimal Optimal Optimal Optimal mental value of
Charac- ber lation values of values of values of values of value of Fisher
teristics of coefficient parameter parameter parameter parameter the function
No. y in eq.(1), obser- Fisher Fc(1,n-2)
dataset va- r a b c g function for the F/Fc
tions F, eq. confi-
in eq. (1) in eq. (1) in eq. (1) in eq. (1) (S1), dence
n m=2 level
0.001,
[22]
Relationships versus gross domestic product per capita (GDP), variable x in eq. (1)
1 CC, Africa 54 0.7353 0 0.1188 0 1.3038 61.22 12.35 5.0
2 CC, Europe 42 0.7206 86929.3 126.16 -11649.8 0.71678 43.20 12.87 3.4
3 CC, all 96 0.9110 0 0.0239 0 1.5141 458.63 11.66 39.3
4 DC, Africa 54 0.9013 3.02799 0.0277 -855.999 1.0143 224.98 12.35 18.2
5 DC, Europe 42 -0.3516 0 1.0965e+5 0 -0.36425 5.64 12.87 0.44
6 DC, all 96 0.9122 3.02799 0.0257 -855.999 1.0466 465.82 11.66 40.0
7 CFR, Africa 54 0.7313 8.3881e-4 3.8836e-5 -855.99 0.72518 59.76 12.35 4.8
8 CFR, Europe 42 -0.6800 0 355.45 0 -1.011 34.41 12.87 2.7
9 CFR, all 96 0.3145 8.3881e-4 8.1292e-4 -855.99 0.25827 10.32 11.66 0.89
10 TC, Africa 49 0.6956 0 1.0880e-5 0 1.06281 44.05 12.56 3.5
11 TC, Europe 39 0.6994 0.442 8.3847e-6 12985.5 1.19322 35.44 13.00 2.7
12 TC, all 88 0.9023 0 1.0167e-6 0 1.36422 376.67 11.74 32.1
13 VC, Africa 53 0.8503 0.1299 0.0857 855.998 0.67441 133.07 12.39 10.7
14 VC, Europe 41 0.7890 0 1.4678 7184 0.35864 64.31 12.91 5.0
15 VC, all 94 0.8653 0.1299 0.1440 855.998 0.58839 274.08 11.68 23.5
Relationships versus accumulated tests per capita values (TC), variable x in eq. (1)
16 CC, all 89 0.9496 0 9.689e+4 0 1.02755 797.62 11.73 68.0
17 CC, TC>3 16 0.4006 0 3.3714e+5 0 0.15396 2.68 17.27 0.16
Relationships versus percentage of fully vaccinated people (VC), variable x in eq. (1)
18 CC, all 96 0.8348 3609.84 4.4063 0 2.50244 216.20 11.66 18.5
19 CC, TC>3 15 -0.3818 214294 6.3205e24 0 -10.6211 2.22 17.93 0.12
20 DC, all 96 0.8185 3.0275 0.9319 0.1171 1.72795 190.85 11.66 16.4
21 DC, TC>3 15 -0.4486 0 5.4599e6 0 -1.82403 3.28 17.93 0.18
22 CFR, all 96 -0.4397 0.003983 0.0471 0 -0.60571 22.54 11.66 1.9
23 CFR, TC>3 15 -0.2766 0 0.0439 36.15 -0.61497 1.08 17.93 0.06
Figure 1: Characteristics of the COVID-19 pandemic in Africa (black) and Europe (blue) versus
gross domestic product based on purchasing power parity GDP (PPP) per capita in international US
dollars
The characteristics accumulated as of August 1, 2022 are: numbers of cases per million (CC,
“circles”), numbers of deaths per million (DC, “crosses”), percentage of fully vaccinated people (VC,
“dots”), numbers of test per capita (TC, “squares”). The case fatality risk was calculated with the
use of formula CFR=DC/CC and shown by “triangles. Lines represent the best fitting relationships
(1) with the optimal values of parameters listed Table 1: the black color corresponds to African
countries, the blue one – to European, the red one – to complete datasets (Africa + Europe).
Discussion
The large difference between the number of registered and real COVID-19 cases [23-33] has to
be taken into account to investigate the effects of different factors on the pandemic dynamics. In
particular, different healthcare infrastructures, public health policies, and social behaviors could
significantly change the pandemic dynamics and the analysis of these factors needs further
investigations. Here we will focus on some specific influence of the testing rate. In particular, the
TC values could approach some critical level, which allows revealing almost all COVID-19 cases.
To check this hypothesis, let us consider the countries with TC>3. Their relatively large number -
16 (all countries are located in Europe) - allows drawing some statistical conclusions (see row 17).
First, there is no correlation between CC and TC values even for significance level 0.1 (Fc (1, 14)
=3.14). It means that 3 or more tests per person were enough to reveal the majority of cases before
August 1, 2022. Its average value CCa is approximately 460,834 and can be used to calculate the
visibility coefficient
ССa
b= (3)
СС
as the ratio of real to registered number of cases.
There are some theoretical and experimental estimations of the visibility coefficient for different
periods of COVID-19 pandemic [10, 23-25]. For example, a total testing in Slovakia (89.5% of
population was tested on October 31- November 7, 2020) revealed a number of previously
undetected cases, equal to about 1.63% of the population [23, 24]. Taking into account that the
number of detected cases in Slovakia was approximately 1% of population [20], we can estimate
β≈2.63 for that period in Slovakia. As of August 1, 2022 the corresponding value CC=473,844 for
Slovakia (see Table S2) is slightly larger than CCa showing the good detection level in this country
with TC=9.41.
A random testing in two kindergartens and two schools in the Ukrainian city of Chmelnytskii
[25] revealed the value 3.9 in December 2020. Theoretical estimations based on the generalized SIR
model [6, 10] yielded values from 3.7 to 20.4 for Ukraine and 5.4 for Qatar in different periods of
the COVID-19 pandemic. As of August 1, 2022 formula (3) yields the β values 3.8 for Ukraine and
3.0 for Qatar (CC=152,375.8, [20]). Corresponding visibility coefficients are: 4.4 in Japan; 1.7 in US
and 14.7 in India.
The value CCa = 460,834 and formula (3) is probably not applicable for China and other Zero-
COVID countries [34], where the total control and maximum suppression of the pandemic were
applied. For example, mainland China has achieved the testing level TC=6.46 already on April 11,
2022, [20]. The value CC=636 registered on August 1, 2022 is much lower than the CCa figure.
Nevertheless, CC values in Australia, New Zealand and South Korea (where the zero tolerance
policy was not as severe as in China) CC values vary from 317,619 to 384,572 (see [20]). The testing
levels in Hong Kong (TC= 6.59 as of May 24, 2022, [20]) and in mainland China are very close. The
huge difference in the registered numbers of cases per million (CC= 181,231 in Hong Kong as of
August 1, 2022, [20]) probably is connected with much higher values of the tests per case ratio in
mainland China, [14].
The lack of appropriate testing makes it especially difficult to detect the first cases of a new
disease, which for SARS-CoV-2 probably appeared long before December 2019 [26]. In particular,
theoretical estimates give the date of the appearance of the first case at the beginning of August
2019, [6].
The insufficient testing and high values of visibility coefficients can lead to controversial
conclusions about the influence of vaccinations. For example, for complete datasets, unexpected
upward trends for CC and DC values with the increasing VC were revealed at the very high
significance level (see rows 18 and 20). Similar correlations were also found in [11] for average
daily numbers of COVID-19 cases and deaths. In some countries (e.g., Israel, Japan, New Zealand),
high vaccination levels did not prevent new severe pandemic waves [10, 14] with record numbers
of cases and deaths, [10, 14]. Statistical studies support the fact that vaccinations diminished CFR,
but their ability to reduce infections should be questioned [10, 11, 13, 14] and needs further
investigation.
It would also be interesting to investigate the reasons for the increase in CC and DC values with
the increase in incomes (see rows 3 and 6 in Table 1). One of them could be a lower mobility and
less number of contacts in poor countries [18]. The age of population is another important factor in
the visible COVID-19 pandemic dynamics [11, 35], since the percentage of asymptomatic (and
unregistered) patients is much higher in children [27-30]. In particular, a one-year increment in the
median year of population yields a 12-18 thousand increase in CC values and 52-83 increase in DC
values (both figures correspond December 31, 2022), [35]. Taking into account the 24 year
difference in the median age (18 in Africa and 42 in Europe, [36]) we can expect 288- 432 thousand
higher CC figures and 1.2-2 thousand higher DC figures in Europe. The huge number of undetected
COVID-19 cases increases the probability of the appearance of new dangerous SARS-CoV-2
variants.
Conclusions
Non-linear correlation analysis (using JHU datasets for Europe and Africa corresponding to
August 1, 2022) demonstrated that the numbers of COVID-19 cases CC and deaths DC per capita
and case fatality risks CFR=DC/CC increase for richer countries. The same trends were revealed
for DC and CFR values in Africa, but opposite ones in Europe. As expected, the testing and
vaccination levels increase with the growth of GDP. Higher levels of testing probably allowed
revealing more cases and COVID-19 related deaths in rich countries. CC values showed a very
strong increasing trend with the increase of numbers of tests per capita (TC). Unexpectedly, the
same increasing trend was revealed for CC and DC values versus percentage of fully vaccinated
people (VC). Nevertheless, the decrease of CFR with the increase of VC demonstrates a positive
effect of vaccinations.
In some countries, the number of undetected COVID-19 cases may be tens or even hundreds of
times higher than the number of registered ones due to the differences in testing levels and age
structure. This fact increases the probability of the appearance of new dangerous SARS-CoV-2
strains and has to be taken into account in further investigations of impact of different factors on
the pandemic dynamics.
Conflict of Interest
The authors declare no conflict of interests
Ethical Approval statement
The study does not use any experiments with humans or animals. The data sources are available
on the Internet.
Acknowledgments
The authors are grateful to Robin Thompson, Matt Keeling, and Paul Brown for their support
and providing very useful information. Igor Nesteruk was supported by INI-LMS Solidarity
Programme at the University of Warwick, UK.
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Supplementary tables
Table S1. Gross domestic product per capita (GDP) based on purchasing power parity
(PPP), accumulated and daily characteristics of the COVID-19 pandemic dynamics in
African countries as of August 1, 2022 (figures corresponding to other days are specified
in notes).
Country GDP People Total Total Total
(PPP) per fully cases per deaths per tests per
capita vaccinated million CC, million, DC, thousand
Int$, [19] per hundred, [20] [20] TC*1000,
%, VC, [20] [20]
Algeria 13002 15.5111 6056.096 155.643 no data
Angola 7360 22.1427 2964.922 55.414 46.9113
Benin 4137 20.6628 2101.733 12.541 no data
Botswana 19287 58.4427 125740.7 1070.15 882.8399
Burkina 2663 7.6429 955.989 17.511 14.6954
Faso
Burundi 856 0.1327 3609.85 3.028 128.37718
Cameroon 4398 4.5123 4419.892 70.996 100.41618
Cape 7740 52.427 105732.9 697.368 no data
Verde
Central 1102 22.8223 2705.44 20.707 17.36418
African
Republic
Chad 1705 21.0128 432.894 11.234 12.7419
Comoros 3355 46.525 10120.18 194.736 122.0629
Congo 4578 11.2125 4245.343 66.143 67.6927
Cote 6345 30.4627 3106.748 29.514 56.07519
D'Ivoire
Democratic 1316 2.5827 961.196 14.495 10.89919
Republic
of Congo
Djibouti 6667 17.2524 14191.94 170.955 280.15911
Egypt 14928 35.3324 4719.337 226.657 109.4157
Equatorial 19036 13.0923 10242.49 111.963 296.4616
Guinea
Eritrea 2101 No data 2777.109 28.451 6.5443
Eswatini 10411 28.6927 61464.21 1188.488 461.01619
Ethiopia 3407 30.5225 4092.664 62.918 41.77419
Gabon 17848 10.9828 20720.76 130.703 683.37418
Gambia 2646 13.5427 4575.146 139.398 58.9746
Ghana 6754 24.425 5120.666 44.376 75.08418
Guinea 3029 18.9828 2764.429 32.885 52.35119
Guinea- 2784 17.5328 4082.066 83.466 69.36819
Bissau
Kenya 6061 17.6529 6369.872 107.008 70.33519
Lesotho 3034 38.2522 14920.31 307.699 201.50818
Liberia 1779 44.8321 1451.453 56.61 26.9235
Libya 18345 18.1126 74772.13 954.823 no data
Madagascar 1778 4.6427 2299.481 48.693 15.62316
Malawi 1603 9.7227 4395.482 134.089 29.62819
Mali 2575 6.8427 1425.612 33.737 32.32619
Mauritania 6920 30.5623 13559.99 214.952 216.28619
Mauritius 25043 75.2727 188503.5 779.882 987.5818
Morocco 9041 63.2924 34014.06 437.958 316.60614
Mozambique 1439 39.9228 7157.792 69.052 41.8319
Namibia 10448 19.0725 66894.43 1609.39 417.38419
Niger 1435 11.8325 360.516 12.316 10.0810
Nigeria 5853 13.228 1222.94 14.747 24.7419
Rwanda 2808 77.9127 9823.436 108.9 411.15520
Sao Tome 4681 45.125 27408.37 336.162 21.03917
and Principe
Senegal 4093 6.35 5180.094 116.61 65.3038
Seychelles 35272 76.1227 426683.6 1577.909 no data
Sierra Leone 1958 25.7727 918.457 14.844 50.88914
Somalia 1322 11.9624 1583.304 79.751 29.3720
South Africa 15361 32.3526 67435.12 1717.093 431.66719
South Sudan 928 13.6927 1649.847 12.839 38.17218
Sudan 4442 9.9412 1379.979 108.57 12.331
Tanzania 3358 23.2427 589.888 13.226 7.1682
Togo 2599 16.4928 4406.334 32.274 86.72219
Tunisia 12300 52.0226 92040.93 2368.191 379.01314
Uganda 2961 27.1227 3691.081 79.121 59.95519
Zambia 3776 26.49 16919.88 206.182 180.45615
Zimbabwe 2523 29.35 16030.36 348.704 148.80419
Figures corresponding to different days in 2022:
February: 1 – 13; 2– 18;
March: 3 -7; 4 -10; 5 -12; 6 -24;
May: 7 -1; 8 -5; 9 -18; 10 -19; 11 -29; 12 -30;
June: 13 -2; 14 -12; 15 -15; 16 -16; 17 -19; 18 -20; 19 -22; 20 -23;
July: 21 -3; 22 -17; 23 -24; 24 -27; 25 -31;
August: 26 -2; 27 -7; 28 -14; 29 -21.
Table S2. Gross domestic product per capita (GDP) based on purchasing power parity
(PPP), accumulated and daily characteristics of the COVID-19 pandemic dynamics in
European countries as of August 1, 2022 (figures corresponding to other days are
specified in notes)
Country GDP (PPP) People Total cases Total Total tests
per capita fully per million deaths per per thousand
Int$, [19] vaccinated CC, [20] million, TC*1000, [20]
per hundred, DC, [20]
%, VC, [20]
Albania 17.383 43.9733 109424.4 1242.858 565.33626
Andorra 63.600 67.6637 575802.8 1935.876 3799.7197
Austria 64.751 76.445 535081.7 2277.159 21272.1327
Belarus 21.686 66.5233 103781.5 743.148 1380.27312
Belgium 61.587 78.85 381107.7 2778.558 2955.33223
Bosnia and 17.471 25.874 117815.3 4849.366 458.372
Herzegovina
Bulgaria 28.593 29.99 175689.8 5431.995 1463.89826
Croatia 36.201 55.3331 292302.4 4021.049 1212.4527
Cyprus 48.443 72.0330 628244 1244.41 32925.8311
Czechia 47.527 65.49 379468.8 3853.198 5193.48826
Denmark 69.273 81.69 552355.7 1139.516 11043.2626
Estonia 44.778 63.5 441332.6 1971.851 2577.75725
Finland 58.010 78.3735 211531 905.348 1994.02224
France 56.036 78.63 503224.3 2258.135 4126.75422
Germany 63.271 76.01 371147.5 1728.24 1574.02119
Greece 35.596 73.08 416397.4 2967.728 8088.1226
Hungary 40.944 63.8634 202422.7 4818.85 1127.08113
Iceland 64.621 78.3610 545876 483.346 3709.57424
Ireland 124.596 81.18 329721.7 1539.148 2476.13527
Italy 50.216 80.95 355493.4 2906.922 3795.99826
Kosovo 13.964 46.25 142944.2 1779.346 1036.05325
Latvia 37.330 69.6929 461241.4 3146.347 3876.13726
Liechtenstein No data 67.7232 479341.2 2202.925 2321.5763
Lithuania 46.479 67.37 424716.3 3304.684 3128.55323
Luxembourg 140.694 No data 441487.1 1734.653 6725.73125
Malta 54.647 89.28 214294.1 1507.362 3703.58926
Moldova 16.719 34.7417 173831.8 3791.598 no data
Monaco No data 69.961 385133.3 1662.76 no data
Montenegro 24.878 45.27 414061.1 4376.779 no data
Netherlands 68.572 68.3532 477054.9 1290.389 1753.39324
North 19.726 39.8233 156028.8 4456.267 986.11826
Macedonia
Norway 77.808 74.96 269485.9 670.551 2064.50525
Poland 41.685 58.81 158461 3042.989 964.88120
Portugal 40.805 86.5432 519445 2392.882 4161.80816
Romania 36.622 41.9818 158772.8 3415.205 1099.9469
San Marino 70.139 70.0315 589403.2 3496.711 no data
Serbia 23.904 47.7127 309830.7 2370.063 1433.86926
Slovakia 38.620 50.7333 473844 3712.996 9405.6626
Slovenia 48.534 57.6728 511303.1 3161.257 2517.85826
Spain 46.413 85.5335 278530.9 2331.568 1961.84821
Sweden 62.926 73.1936 242638 1849.414 1758.61423
Switzerland 84.658 69.12 454386.7 1598.245 2448.13524
Ukraine 14.325 34.818 121631.5 2675.378 443.1016
United 55.301 75.11 346375.1 3035.892 7480.12114
Kingdom
Vatican City No data No data 56751.47 No data no data
Figures corresponding to different days:
1 -December, 21, 2021;
in 2022:
January: 2 -5; 3 -10; 4 -29; 5 -31;
February: 6 -18; 7 -23; 8 -27;
March: 9 -10; 10 -29;
April: 11 -14;
May: 12 -10; 13 -11; 14 -19; 15 -22;
June: 16 -1; 17 -2; 18 -11; 19 -12; 20 -16; 21 -17; 22 -18; 23 -19; 24 -20; 25 -21; 26 -22; 27 -23;
July: 28 -5; 29 -11; 30 -26; 31 -28; 32 -29; 33 -31;
August: 34 -2; 35 -3; 36 -11; 37 -21.
Fisher function
The experimental values of the Fisher function can be calculated with the use of the formula:
r 2 ( n - m)
F=
(1 - r 2 )(m - 1)
(S1)
where n is the number of observations (number of countries and regions taken for statistical
analysis); m=2 is the number of parameters in the linear regression equation (2), [22].