key: cord-0288941-d6x3si99 authors: Nesteruk, I.; Rodionov, O. title: The impact of demographic factors on the accumulated number of COVID-19 cases per capita in Europe and the regions of Ukraine in the summer of 2021 date: 2021-07-06 journal: nan DOI: 10.1101/2021.07.04.21259980 sha: 9de5ca451cc24a216a89c723e3a2c9bb1b506b7c doc_id: 288941 cord_uid: d6x3si99 The accumulated number of COVID-19 cases per capita is an important characteristic of the pandemic dynamics that may also indicate the effectiveness of quarantine, testing and vaccination. As this value increases monotonically over time, the end of June 2021 was chosen, when the growth rate in Ukraine and the vast majority of European countries was small. This allowed us to draw some intermediate conclusions about the influence of the volume of population, its density, and the level of urbanization on the accumulated number of laboratory-confirmed cases per capita in European countries and regions of Ukraine. A simple analysis showed that the number of cases per capita does not depend on these demographic factors, although it may differ by about 4 times for different regions of Ukraine and more than 9 times for different European countries. The number of COVID-19 per capita registered in Ukraine is comparable with the same characteristic in other European countries but much higher than in China, South Korea and Japan. 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] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] . The influence of the population volume N pop on the final sizes V  of the first pandemic waves in different countries and regions was studied in [19] and compared with the real CC values at fixed moments of time. In particular, relative final size of the first epidemic wave V  was approximated by following In this paper we will study the influence of the volume of population N pop , its density, and the level of urbanization N ubr /N pop (N ubr is the number of people living in cities) on the accumulated number of laboratory-confirmed cases per capita in European countries and regions of Ukraine. We will use the data set regarding the numbers of laboratory-confirmed COVID-19 cases in the regions of Ukraine accumulated at the time June All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 6, 2021. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 6, 2021. ; The CC values (per 100 persons, blue "crosses") versus the volume of population, its density and the urbanization level are shown in Figs. 1-6. We have used datasets from Tables 1 and 2. The best fitting lines (black) were calculated by the least squares method [25] . The linear regression was used to calculate the regression coefficient r and the coefficients a and b of corresponding straight lines, [25] : where x is the volume of population N pop (Figs. 1 and 2) , its density per square km (Figs. Table 2 ). The best fitting line (3) is shown in black. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. [19] for the V  (see eq. (1)). Corresponding values of r, a, b and the number n of regions or countries taken for calculations are shown in Table 3 . Table 1 ). The best fitting line (3) is shown in black. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 6, 2021. ; Blue "crosses" show the accumulated number of laboratory-confirmed cases per 100 persons ( Table 2 ). The best fitting line (3) is shown in black. Table 3 Table 1 ). The best fitting line (3) is shown in black. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 6, 2021. ; Table 2 ). The best fitting line (3) is shown in black. We can use also the F-test for the null hypothesis that says that the proposed linear relationship (3) fits the data sets. The experimental values of the Fisher function can be calculated with the use of the formula: where m=2 is the number of parameters in the regression equation, [25] . The corresponding experimental values F are shown in Table 3 . They have to be compared with the critical values Table 3 show that the critical values are much higher than the experimental F values. It means that the data sets presented in Tables 1 and 2 do not support the linear relationship (3). We have checked also the non-linear dependences: All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. The copyright holder for this preprint this version posted July 6, 2021. ; https://doi.org/10.1101/2021.07.04.21259980 doi: medRxiv preprint instead of (3) as it was done in [19, 27, 28] . Similar to the case of linear dependence (3), the calculations showed that corresponding values 1 r  and 1 2 ( , ) C F k k F  . It means that hypothesis (5) was not also supported by the datasets presented in Tables 1 and 2. Very different CC values registered in the regions of Ukraine and European countries could be a result of different coronavirus strains, quarantine measures, testing, tracing and isolating patients. One more reason may be the large number of unregistered cases observed in many countries [29] [30] [31] [32] [33] . Estimates for Ukraine and Qatar made in [29, 33] showed that the real number of cases is about 4-5 times higher than registered and reflected in the official statistics. Similar estimates can be made for the regions of Ukraine and other European countries. If we apply the visibility coefficients 10  =3.7 and 3 5.308   calculated for the Ukraine and Qatar (see [29, 33] ) and take accumulated numbers of laboratory-confirmed First of all the COVID-19 pandemic probably started in this region in August 2019, [18] . It means that first cases were not identified and registered during at least 4 months. Probably these first cases were not very severe and the symptoms were not so pronounced. Presumably mutations of the coronavirus made it more pathogenic and sick people became more noticeable in December 2019. But previous cases were not taken into account in the statistics. Recent DNA investigations of East Asia population reported the presence of coronavirus around 20,000 years ago [35] . Probably, the population of this region had a collective immunity to pathogens similar to Covid-19 before the pandemic. All rights reserved. No reuse allowed without permission. (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. 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