The impact of demographic factors on numbers of COVID-19 cases and deaths in Europe and the regions of Ukraine

The accumulated numbers of COVID-19 cases and deaths per capita are important characteristics of the pandemic dynamics that may also indicate the e ffectiveness of quarantine, testing, vaccination, and treatment. The statistical analysis based on the number of cases per capita accumulated to the end of June 2021 showed no correlations with the volume of popu lation, its density, and the urbanization level both in European countries and regions of Ukraine. The same result was obtained with the use of fresher datasets (as of December 23, 2021). The number of deaths per capita and per case may depend on the urbanization level. For European countries these relative characteristics decrease with the in crease of the urbanization level. Opposite trend was revealed for the number of deaths per capita in Ukrainian regions.

COVID-19 pandemic, epidemic dynamics in Europe, epidemic dynamics in Ukraine, mathematical modeling of infectious diseases, statistical methods.

The accumulated number of COVID-19 cases per capita (CC) and deaths per capita (DC) may indicate the effectiveness of quar antine, testing, vaccination, treat ment, and also characterizes the virulence of coronavirus strains which circulated in a particular region. An important characteristic of the strain virulence and treatment effi ciencies can be the number of d eaths per case DC/CC. The CC and DC numbers are regularly reported by World Health Organization, [1] and COVID-19 Data Repository by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (JHU), [2]. The impact of some eco-demographic factors on the COVID-19 pandemic dynamics was studied in [3-20]. In particular, in [20] the influences of the volume of population, its density, and the urbanization level on the CC numbers accumulated at the end of June 2021 were investigated for European countries and the regions of Ukraine. Since no statistically signi ficant correlations were revealed, in this paper we will study the influence of the volume of population N pop, its density, and the level of urbanization Nubr/Npop (Nubr is the number of people living in cities) on the CC, DC and DC/DC values with the use of fresher figures (as of December 23, 2021) for European countries and regions of Ukraine. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint NOTE: This preprint reports new research that has not been certified by peer review and should not be used to guide clinical practice. Data For this statistical analysis we will use the data set regarding the numbers of laboratory-confirmed COVID-19 cases in the regions of Ukraine accumulated at the time December 23, 2021 and compare it with the results based on the figure s accumulated at June 27, 2021. As in paper [20], we will use the CC numbers (per 100 persons of population) registered by national statistics [21] and demographic data sets for Ukrainian regions [22] (see Table 1). The accumulated numbers of deaths registered by national statistics [21] in Ukrainian regions at December 23, 2021 are shown in the last column of Table 1. As the information from the regions of Ukraine fully or partially occupied by the Russian Federation is inaccurate, we excluded from consid eration Donetsk and Luhansk regi ons, Crimea and Sevastopol. The CC figures (per 1,000,000 persons of population) registered by JHU [2] at two moments of time: June 28, 2021 and December 23, 2021 are shown in Table 2, which contains also the accumulated number of deaths per million (DC) as of December 23, 2021, [2] and the demographic data sets for European countries taken from [23-25]. Table 1. Demographic characteristics and th e accumulated number of laboratory-confirmed COVID-19 cases and deaths in the regions of Ukraine as of June 27, 2021 and December 23, 2021     Oblast (region)      Population  Npop,  [22]      Urban  popu‐ lation,  Nubr, [22]     Popula‐ tion  density  (number  of  people  per  square  km), [22]  Accumu‐ lated  number of  laboratory‐ confirmed  cases per  hundred as  of June 27,  2021, [21]  Accumu‐ lated  number of  laboratory‐ confirmed  cases per  hundred as  of Decem‐ ber 23,  2021, [21]  Accumu‐ lated  number  of deaths  as of  Decem‐ ber 23,  2021,  [21]  Vinnytsia   1 545 416  799 385  58.29  4.67  8.8 3029 Volyn  1 031 421  539 179  51.2  6.06  10.03 2148 Dnipropetrovsk   3 176 648  2 668 744  99.54  4.33  7.75 8707 Zhytomyr   1 208 212  716 457  40.5  7.43  11.79 3204 Zakarpattia   1 253 791  465 904  98.13  4.98  6.96 2283 Zaporizhzhia  1 687 401  1 306 231  62.08  6.30  10.99 5105 Ivano‐Frankivsk   1 368 097  606 764  98.23  6.38  9.32 3052 Kyiv (oblast)  1 781 044  1 105 383  63.31  7.12  10.21 4588 Kirovohrad   933 109  591 944  37.95  2.25  3.59 1458 Lviv   2 512 084  1 534 040  115.06  5.52  8.78 5900 Mykolaiv  1 119 862  768 022  45.53  6.34  10.45 3231 Odesa   2 377 230  1 597 062  71.37  5.95  10.1 5488 Poltava  1 386 978  867 201  48.25  5.70  10.16 3619 Rivne   1 152 961  548 088  57.51  6.93  11.07 2231 Sumy   1 068 247  741 430  44.82  7.43  12.5 2781 Ternopil  1 038 695  473 727  75.14  6.82  10.4 2075 Kharkiv   2 658 461  2 158 121  84.62  5.66  9.27 6231 Kherson  1 027 913  631 317  36.12  3.53  7.81 2656 Khmelnytskyi  1 254 702  720 752  60.82  7.18  11.78 3262 Cherkasy   1 192 137  678 682  57.04  6.97  10.92 2526 Chernivtsi   901 632  390 551  111.35  8.94  13.3 2976 Chernihiv   991 294  649 063  31.11  5.92  9.74 2353 Kyiv (city)  2 967 360  2 967 360  3536.78  7.22  11.01 8069 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint Table 2. Demographic characteristics and the accumulated number of laboratory-confirmed COVID-19 cases and deaths in European countries as of June 28, 2021 and December 23, 2021 Country Population, [23] Urbani- zation level Nubr/Npop % , [24] Population density (per quare km), [25] Total cases per million CC as of June 28, 2021, [2] Total cases per million CC as of December 23, 2021, [2] Total deaths per million DC as of December 23, 2021, [2] Monaco 38682 100 1,547 65615.126 116548.6 961.538 Vatican City 801 100 2,273 33374.536 33251.23 no data Malta 514564 95 1,447 69330.229 84878.9 916.489 San Marino 33785 97 561 150008.84 218965 2852.102 Netherlands 17059560 91 416 99886.646 176606.8 1205.141 Belgium 11482178 98 384 93486.963 174589 2416.54 United Kingdom 67141684 83 270 70284.988 172901.5 2167.399 Liechtenstein 37910 14 245 79555.288 154127.7 1803.733 Luxembourg 604245 91 243 112850.972 155069 1428.765 Germany 83124418 77 225 44576.917 83172 1312.555 Italy 60627291 70 207 70432.141 91391.17 2256.927 Switzerland 8525611 74 204 81198.962 140829.1 1383.398 Andorra 77006 88 183 179667.379 278860.8 1796.934 Denmark 5752126 88 136 50743.387 115907.8 545.817 Czech Republic 10665677 74 136 155658.773 224437.2 3290.394 Poland 37921592 60 122 76088.436 106289.4 2472.286 Portugal 10256193 65 112 85856.051 123239.9 1852.886 Slovakia 5453014 54 111 71720.074 245814.2 2973.78 Albania 2882740 60 107 46046.633 72029.15 1107.23 Austria 8891388 66 106 72206.875 139153.7 1503.361 France 64990511 80 105 86325.089 132236.8 1810.972 Hungary 9707499 71 105 83645.21 128431.5 3976.163 Turkey 82340088 75 105 64196.94 108763.9 953.932 Slovenia 2077837 55 104 123742.383 217967 2659.325 Moldova 4051944 43 99 63613.375 92880.14 2379.458 Spain 46692858 80 99 81117.733 122322.8 1904.345 Serbia 8802754 56 91 105279.579 187323.5 1821.715 Romania 19506114 54 89 56174.491 94160.52 3056.446 North Macedonia 2082957 58 83 74722.806 106754.3 3777.859 Greece 10522246 79 80 40416.745 101881.1 1947.594 Bosnia and Herzegovina 3323925 48 75 62481.731 87879.76 4057.964 Croatia 4156405 57 75 87610.845 168213.5 2983.837 Ireland 4818690 63 74 55002.07 136541.9 1182.042 Ukraine 44246156 69 73 52556.15 87621.13 2304.263 Bulgaria 7051608 75 63 60682.066 106153.8 4413.154 Belarus 9452617 78 46 44001.045 72981.12 577.049 Montenegro 627809 67 44 159544.758 257851.7 3802.239 Lithuania 2801264 68 42 102372.965 188826 2685.268 Latvia 1928459 68 29 72759.97 144706.8 2404.477 Estonia 1322920 69 27 98740.406 177301.6 1433.004 Sweden 9971638 87 23 107819.278 125324.1 1502.437 Norway 5337962 82 17 24116.983 67305.34 229.983 Finland 5522576 85 16 17176.113 40943.44 271.071 Iceland 336713 94 3 19208.791 64326.07 107.759 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint Linear regression and Fisher tests The linear regression will be used to calculate the regression coefficient r and the coefficients a and b of corresponding straight lines, [26]: ya b x  (1) where x is the volume of population Npop, its density per square km, or the urbanization level Nurb/Npop; y is the numbers of cases per capita CC, numbers of deaths per capita DC and deaths per case ratio DC/CC. We will also use the F-test for the null hypothesis that says that the proposed linear relationship (1) fits the data sets. The experimental values of the Fisher function can be calculated with the use of the formula: 2 2 () (1 )( 1) rnmF rm    (2) where m=2 is the number of parameters in the regression equation, [26]. The experimental values F must be compared with the critical values 12(, )CF kk of the Fisher function at a desired significance or confidence level ( 1 1km , 2kn m , see, e.g., [27]). If 12(, )CF kk F  , the corresponding hypothesis is supported by observations.

Within six months, the numbers of cases per hundred in the regions of Ukraine increased from 1.4 to 2.2 times (see Table 1 and Fig. 1). The minimum values remained in the Kirovohrad region 3.59 (2.25), and the maximum ones - in the Chernivtsi region 13.3 (8.94). The variation (the difference between maximum and minimum values) increased 1.45 times. Rather high CC values still remained in Zhytomyr, Khmelnytskyi, Kyiv (city), and Sumy regions. As of December 23, 2021 the maximum values of deaths per 100,000 persons were registered in Chernivtsi (330.1) and Zaporizhzhia (302.5) regions. The minimum values corresponding to Kirovohrad (156.3) and Zakarpattia (182.1) regions are approximately twice smaller (see Table 1). The numbers of deaths per 100 registered cases vary much more: from 11.23 (Dnipropetrovsk) and 7.33 (Kyiv city) to 2.00 (Ternopil) and 2.02 (Rivne). As of June 28, 2021, the highest CC levels were registered in Andorra - 18%, Montenegro – 16%, Czech Republic - 15.5%, San Marino - 15%, Slovenia - 12.4%. In six months, the list of the most infected countries has changed as follows: Andorra (27.9%), Montenegro (25.8%), Slovakia (24.6 %). The lowest CC values were in the Vatican (3.3% no new cases were registered last 6 month), Finland (4.1%) and Iceland (6.4%), see Table 2. As of December 23, 2021 the maximum values of deaths per 100,000 persons were registered in Bulgaria (441.3), Bosnia and Herzegov ina (405.8) and Hungary (397 .6). The minimum values (corresponding to Iceland (10.8), Norway (23.0) and Finland (27.1)) were much smaller, see Table 2. The calculated numbers of deaths per 100 registered cases vary from 4.6 (Bosnia and Herzegovina), 4.2 (Bulgaria) and 3.5 (North Macedonia) to 0.47 (Denmark), 0.34 (Norway) and 0.17 (Iceland). Such huge differences raise questions about their cause. As in [20], we have investigated possible correlations with the volume of population, its de nsity and the level of urbanization. The results are presented in Tables 3 and 4 and in Figs. 1-4. The linear regression (1) was used to calculate the regression coefficient r, the coefficients a and b of corresponding straight lines, and the experimental values of the All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint Fisher function. The results are shown in Table 3 for CC values and in Table 4 for DC and 100000*DC/CC values. The regression analysis demonstrates that th ere are no correlations between CC values and demographic factors, since 12(, )CF kk F  for all cases presented in Table 3 (compare figures in two last columns). This fact is valid both in the case of Ukrainian regions and European countries for datasets corresponding December 23, 2021 and previous study with the use of CC values accumulated at the end of June 2021 (results are published in [20] and shown in brackets). Numbers of cases per 100 persons and corresponding best fitting lines are shown in Fig. 1 fo r Ukrainian regions and in Fig. 2 for Europe. Small markers and dashed lines correspond to the situation at the end of June 2021, large markers and solid lines – on December 23, 2021. It can be seen that CC data are ve ry scattered. Some visible trends occurred only for dependence CC versus density of population in Europe at the end of 2021 (see the blue solid line in Fig. 2), but even in this case no significant correlation was revealed. Table 3. Optimal values of parameters in eq. (1), correlation coefficients and the results of Fisher test applications. The results of calculations performed in [20] with the use of data sets corresponding to the end of June 2021 are shown in brackets. Number of Figure, relationship, region n Correlation coefficient, r Optimal values of parameters a in eq. (1) Optimal values of parameters b in eq. (1) Experimental value of the Fisher function F, eq. (2), m=2 Critical value of Fisher function F c(1,n-2) for the confidence level 0.1, [27] 1, CC versus volume of population, Ukraine 23 -0.1078 ( -0.1082) 10.3503 (6.4203) -3.1785e-007 (-2.2551e-007) 0.2467 (0.2486) 2.96 2, CC versus volume of population, Europe 44 -0.1778 (-0.1607) 14.1528 (8.2330) -4.4192e-008 (-2.5094e-008) 1.3706 (1.1132) 2.84 1, CC versus density of population, Ukraine 23 0.1232 (0.1792) 9.7826 (5.9935) 3.4716e-004 (3.6060e-004) 0.3235 (0.6967) 2.96 2, CC versus density of population, Europe 44 -0.2026 (-0.0953) 14.1058 (8.0331) -0.0026 (-7.7955e-004) 1.7984 (0.3848) 2.84 1, CC versus urbanization level, Ukraine 23 0.0028 (-0.1024) 9.8339 (6.6956) 0.0388 (-1.0123) 1.6121e-004 (0.2224) 2.96 2, CC versus urbanization level, Europe 44 -0.1017 (-0.0019) 15.9213 (7.8739) -3.3644 (-0.0394) 0.4387 (1.5088e-04) 2.84 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint Figure 1. Numbers of COVID-19 cases per 100 persons (CC) in the regions of Ukraine accumulated as of June 27, 2021 versus the volume of population Npop/1000 (red), its density per square km (blue), and the urbanization level 1000*Nurb/Npop (black). Markers represent values from Table 1. Best fitting lines (1) correspond to values shown in Table 3. Figure 2. Numbers of COVID-19 cases per 100 persons (CC) in European countries accumulated as of June 28, 2021 versus the volume of population Npop/100000 (red), its density per square km (blue), and the urbanization level 1000*Nurb/Npop (black). Markers represent values from Table 2. Best fitting lines (1) correspond to values shown in Table 3 The regression analysis of DC values showed that there are correlations with the urbanization levels both for Ukrainian regions and European countries, since 12(, )CF kk F  for these cases (see Table 4). The signs of correlation coefficients and parameters b are opposite for Ukrainian regions and European countries. In Ukrainian regions the number of deaths increases with the increase of urbanization level (see the black solid line in Fig. 3). The same line in Fig.4 illustra tes the opposite trend for European countries. The mortality rate (DC/CC ratio) diminishes with the increase of the urbanization level in Europe (see last row in Table 4 and the dashed black line in Fig. 4). Opposite trend is visible in Fig. 3 for Ukrainian regions, but no All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint statistically significant relationship was revealed ( 12(, )CF kk F  ). The volume of population and its density do not affect the DC and DC/CC values (see Table 4 and Figs. 3 and 4). Table 4. Correlations for ratios of deaths per million (DC) and deaths per 100,000 cases (100000*DC/CC). Optimal values of parameters in eq. (5), correlation coefficients and the results of Fisher test applications. Results for Ukraine are sh own before slash, for Europe- after slash. Number of countries in Europe taken for calculations n=43 (without Vatican, see Table 2). Number of Ukrainian regions taken for calculations n=23 (see Table 1). Relationship Correlation coefficient, R Optimal values of parameters a in eq. (1) Optimal values of parameters b in eq. (1) Experimental value of the Fisher function F, eq. (2), m=2 Critical value of Fisher function F c(1,n-2) for the confidence level 0.1, [27] DC versus density of population 0.1685 / -0.1896 2.3870e+03 / 2.1385e+03 0.0959 / -0.6561 0.6137 / 1.5289 2.96 / 2.85 DC versus volume of population 0.1867 / -0.0854 2.2354e+03 / 2.0743e+03 1.1123e-004 / -3.9774e-006 0.7584 / 0.3010 2.96 / 2.85 DC versus urbanization level 0.3971 / -0.3930 1.7152e+03 / 3.8161e+03 1.1223e+003 / -2.4985e+003 3.9314 / 7.4911 2.96 / 2.85 100000*DC/CC versus density of population -0.0230 / -0.1699 2.5318e+03 / 1.6578e+03 -0.0182 / -0.5373 0.0111 / 1.2190 2.96 / 2.85 100000*DC/CC versus volume of population 0.1656 / 0.0665 2.3162e+03 / 1.5089e+03 1.3664e-04 / 2.8323e-006 0.5918 / 0.1823 2.96 / 2.85 100000*DC/CC versus urbanization level 0.3117 / -0.4094 1.2203e+03 / 3.2717e+03 1.2203e+003 / -2.3785e+003 2.2595 / 8.2580 2.96 / 2.85 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint Figure 3. Deaths caused by coronavirus in Ukrainian regions accumulated as of December 23, 2021. Numbers of deaths per million DC (large markers and solid lines) and per 100,000 cases 100000*DC/CC (small markers and dashed lines) versus the volume of population Npop/1000 (red), its density per square km (blue), and the urbanization level 1000*Nurb/Npop (black). Markers represent values from Table 1. Best fitting lines (1) correspond to values shown in Table 4. Figure 4. Deaths caused by coronavirus in European countries accumulated as of December 23, 2021. Numbers of deaths per million DC (large markers and solid lines) and per 100,000 cases 100000*DC/CC (small markers and dashed lines) versus the volume of population Npop/100000 (red), its density per square km (blue), and the urbanization level 1000*Nurb/Npop (black). Markers represent values from Table 2. Best fitting lines (1) correspond to values shown in Table 4.

Very different CC, DC and DC/CC values registered in the regions of Ukraine and European countries do not depend on the volume of population and its density. The deaths per capita and death per case values decrease with the increase of the urbanization level of European countries. Opposite trend was revealed for Ukrainian regions. These facts motivate us to focus on the analysis of quarantine measures, testing, tracing and isolating patients. All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint We also have to take into account the large nu mber of unregistered cases observed in many countries [28-33]. Estimates for Ukraine made in [28, 33] showed that the real number of cases could be 4-20 times higher than registered and reflected in th e official statistics. Probably, in Ukrainian villages many deaths caused by coronavirus were not registered. That is why the DC values increase with the urbanization level in Ukrainian regions. The decrease of DC and DC/CC values in European cities probably are connected with better testing, isolation, and treating the COVID-19 patients. The results of this study motivate us to pay attention to the pollution effects. The smallest CC, DC, and DC/CC values were registered in the cleanest North European countries (Iceland, Norway, Denmark, and Finland). Higher figures for Sweden are probably connected with the absen ce of lockdown in 2020. The increase of DC values in the most urbanized Ukrainian regions may be also connected with the atmospheric pollution.

1. World Health Organization. “Coronavirus disease (COVID-2019) situation reports”. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/. Retrieved Oct. 3, 2020. 2. COVID-19 Data Repository by the Center for Systems Science and Engi neering (CSSE) at Johns Hopkins University (JHU). https://github.com/owid/covid-19-data/tree/master/public/data 3. Byass, P. Eco-epidemiological assessment of th e COVID-19 epidemic in China, January-February 2020. medRxiv 2020, doi:10.1101/2020.03.29.20046565. 4. Dehning J. et al. Inferring COVID-19 spreading rates and potential change points for case number forecasts. Preprint, ArXiv:2004.01105(2020). 5. Distante, C.; Gadelha Pereira, I.; Garcia Goncalves, L.M.; Piscitelli, P.; Miani, A. Forecasting Covid-19 Outbreak Progression in Italian Regions: A model based on neural network training from Chinese data. medRxiv 2020. 6. Hamzah, F.; Binti, A.; Lau, C.; Nazri, H.; Ligot, D.V.; Lee, G.; Tan, C.L. CoronaTracker: Worldwide COVID-19 Outbreak Data Analysis and Prediction. Bull. World Health Organ. 2020, 1, 32. 7. Fanelli, D.; Piazza, F. Analysis and forecast of COVID-19 spreading in China, Italy and France. Chaos Solitons Fractals 2020, 134, 109761. 8. Webb, G.F.; Magal, P.; Liu, Z.; Seydi, O. A m odel to predict COVID-19 epidemics with applications to South Korea, Italy, and Spain. medRxiv 2020. 9. Distante, C.; Piscitelli, P.; Miani, A. Covid-19 Outbreak Progression in Italian Regions: Approaching the Peak by the End of March in Northern Italy and First Week of April in Southern Italy. Int. J. Environ. Res. Public Health 2020, 17, 3025. 10. KY Ng, MM Gui. COVID-19: Development of a robust mathematical model and simulation package with consideration for ageing population and time delay for control action and resusceptibility. Physica D: Nonlinear Phenomena.Volume 411, October 2020, 132599 https://doi.org/10.1016/j.physd.2020.132599 11. Diego Carvalho, Rafael Barbastefano, Dayse Pastore, Maria Clara Lippi. A novel predictive mathematical model for COVID-19 pandemic with quarantine, contagion dynamics, and environmentally mediated transmission. medRxiv 2020.07.27.20163063; doi: https://doi.org/10.1101/2020.07.27.20163063 12. Piotr T. Chru ściel, Sebastian J. Szybka. Universal properties of the dynamics of the Covid-19 pandemics. medRxiv 2020.08.24.20181214; doi: https://doi.org/10.1101/2020.08.24.20181214 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint 13. Gyan Bhanot, Charles DeLisi. Analysis of Covid-19 Data for Eight European Countries and the United Kingdom Using a Simplified SIR Model. medRxiv 2020.05.26.20114058; doi: https://doi.org/10.1101/2020.05.26.20114058 14. Lia Humphrey et al. TES TING, TRACING AND SOCIAL DISTANCING: ASSESSING OPTIONS FOR THE CONTROL OF COVID_19 . medRxiv 2020.04.23.20077503; doi: https://doi.org/10.1101/2020.04.23.20077503 15. Md. Haider Ali Biswas, M. S. Khatun, A. K. Paul, M. R. Khatun, M. A. Islam, S. A. Samad, U. Ghosh. Modeling the Effective Control Strategy for Transmission Dynamics of Global Pandemic COVID- 19. medRxiv 2020.04.22.20076158; doi: https://doi.org/10.1101/2020.04.22.20076158 16. Elinor Aviv-Sharon, Asaph Ah aroni. Forecasting COVID-19 pandemi c Severity in Asia. medRxiv. doi: https://doi.org/10.1101/2020.05.15.20102640 17. Ashok Krishnamurthy. Spatiotemporal Transmission Dynamics of COVID-19 in Spain. Conference: Advancing knowledge about spatial modeling, infectio us diseases, environment and health (June 8-12, 2020). Presentation. https://www.researchgate.net/publication/342247591_Spatiotemporal_Transmission_Dynamics_of_COVI D-19_in_Spain 18. Nesteruk I. COVID19 pandemic dynamics. Springer Nature, 2021, https://link.springer.com/book/10.1007/978-981-33-6416-5 19. Nesteruk I. Comparison of the First Waves of the COVID-19 Pandemic in Different Countries and Regions. In book: COVID19 pandemic dynamics. Springer Nature, 2021, https://link.springer.com/chapter/10.1007/978-981-33-6416-5_7 20. Nesteruk I, Rodionov O, Nikitin A.V, and Walczak S. Influences of seasonal and demographic factors on the COVID-19 pandemic dynamics. EAI Endorsed Transactions on Bioengineering and Bioinformatics. Published online Dec 8, 2021, DOI: 10.4108/eai.8-12-2021.172364 21. https://index.minfin.com.ua/reference/coronavirus/ukraine/ 22. https://uk.wikipedia.org/wiki/%D0%A1%D0%BF%D0%B8%D1%81%D0%BE%D0%BA_%D0%BE%D 0%B1%D0%BB%D0%B0%D1%81%D1%82%D0%B5%D0%B9_%D0%A3%D0%BA%D1%80%D0% B0%D1%97%D0%BD%D0%B8_%D0%B7%D0%B0_%D1%87%D0%B8%D1%81%D0%B5%D0%BB %D1%8C%D0%BD%D1%96%D1%81%D1%82%D1%8E_%D0%BD%D0%B0%D1%81%D0%B5%D 0%BB%D0%B5%D0%BD%D0%BD%D1%8F 23. https://en.wikipedia.org/wiki/List_of_European_countries_by_population 24. https://en.wikipedia.org/wiki/European_countries_by_percentage_of_urban_population 25. https://www.indexmundi.com/map/?v=21000&r=eu&l=en 26. Draper NR, Smith H. Applied regression analysis. 3rd ed. John Wiley; 1998. 27. https://onlinepubs.trb.org/onlinepubs/nchrp/cd-22/manual/v2appendixc.pdf 28. Nesteruk I. Visible and real sizes of new COVID-19 pandemic waves in Ukraine Innov Biosyst Bioeng, 2021, vol. 5, no. 2, pp. 85–96. DOI: 10.20535/ibb.2021.5.2.230487 http://ibb.kpi.ua/article/view/230487 29. https://podillyanews.com/2020/12/17/u-shkolah-hmelnytskogo-provely-eksperyment-z-testuvannyam- na-covid-19/ 30. https://edition.cnn.com/2020/11/02/europe/slovakia-mass-coronavirus-test-intl/index.html 31. https://www.voanews.com/covid-19-pandemic/slova kias-second-round-coronavirus-tests-draws-large- crowds All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint 32. Nesteruk I. The real COVID-19 pandemic dynamics in Qatar in 2021: simulations, predictions and verifications of the SIR model. September 20 21. Semina Ciências Exatas e Tecnológicas 42(1Suppl(2021)):55-62. DOI: 10.5433/1679-0375.2021v42n1Suplp55 33. Nesteruk I. Influence of Possible Na tural and Artificial Collective Immunity on New COVID-19 Pandemic Waves in Ukraine and Israel. Explor Res Hypothesis Med 2021; Published online: Nov 11, 2021. doi: 10.14218/ERH M.2021.00044. h ttps://www.xiahepublishing.com/2472-0712/ERHM-2021- 00044 All rights reserved. No reuse allowed without permission. perpetuity. preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for thisthis version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787doi: medRxiv preprint