key: cord-0276068-2f2hzwxw authors: Nesteruk, I.; Rodionov, O. title: The impact of demographic factors on numbers of COVID-19 cases and deaths in Europe and the regions of Ukraine date: 2022-01-06 journal: nan DOI: 10.1101/2022.01.05.22268787 sha: 63279a175907089c170611262d378235e2a6e194 doc_id: 276068 cord_uid: 2f2hzwxw The accumulated numbers of COVID-19 cases and deaths per capita are important characteristics of the pandemic dynamics that may also indicate the effectiveness 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 population, 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 increase of the urbanization level. Opposite trend was revealed for the number of deaths per capita in Ukrainian regions. 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 figures 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 consideration Donetsk and Luhansk regions, 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]. 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 preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in The copyright holder for this this version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787 doi: medRxiv preprint The linear regression will be used to calculate the regression coefficient r and the coefficients a and b of corresponding straight lines, [26] : where x is the volume of population N pop , its density per square km, or the urbanization level N urb /N pop ; 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: where m=2 is the number of parameters in the regression equation, [26] . The experimental values F must be compared with the critical values 1 2 ( , ) C F k k of the Fisher function at a desired significance or confidence 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 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%, 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 this this version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787 doi: medRxiv preprint Fisher function. The results are shown in Table 3 for CC values and in Table 4 [20] and shown in brackets). Numbers of cases per 100 persons and corresponding best fitting lines are shown in Fig. 1 for 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 lineson December 23, 2021. It can be seen that CC data are very 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. Critical value of Fisher function F c (1,n-2) for the confidence level 0. 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 this this version posted January 6, 2022. ; Table 1 . Best fitting lines (1) correspond to values shown in Table 3 . 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 1 2 ( , ) C F k k 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 illustrates 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 this this version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787 doi: medRxiv preprint statistically significant relationship was revealed ( 1 2 ( , ) C F k k 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) . Fisher test applications. Results for Ukraine are shown 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 ). 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 this this version posted January 6, 2022. ; Table 4 . 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 this this version posted January 6, 2022. ; https://doi.org/10.1101/2022.01.05.22268787 doi: medRxiv preprint We also have to take into account the large number of unregistered cases observed in many countries [28] [29] [30] [31] [32] [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 the 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 absence of lockdown in 2020. The increase of DC values in the most urbanized Ukrainian regions may be also connected with the atmospheric pollution. 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